From f2f3a77a75a7d54189b38912f13486d49dd54b26 Mon Sep 17 00:00:00 2001
From: "localizeflow[bot]" <skytin1004@users.noreply.github.com>
Date: Fri, 27 Feb 2026 11:47:34 +0000
Subject: [PATCH] chore(i18n): sync translations with latest source changes
 (chunk 1/1, 69 changes)

---
 translations/bg/.co-op-translator.json        |  22 +-
 .../01-defining-data-science/README.md        |   4 +-
 .../01-defining-data-science/notebook.ipynb   |  94 ++++----
 .../solution/notebook.ipynb                   |  91 ++++----
 .../04-stats-and-probability/README.md        |  14 +-
 translations/bg/1-Introduction/README.md      |   2 +-
 .../2-Working-With-Data/07-python/README.md   |  12 +-
 .../07-python/notebook-covidspread.ipynb      | 101 +++++----
 translations/bg/2-Working-With-Data/README.md |   2 +-
 .../12-visualization-relationships/README.md  |  14 +-
 .../R/09-visualization-quantities/README.md   |  16 +-
 .../10-visualization-distributions/README.md  |  20 +-
 .../R/11-visualization-proportions/README.md  |   6 +-
 .../12-visualization-relationships/README.md  |  14 +-
 .../R/13-meaningful-vizualizations/README.md  |  16 +-
 .../bg/3-Data-Visualization/README.md         |   2 +-
 .../14-Introduction/README.md                 |   4 +-
 .../bg/4-Data-Science-Lifecycle/README.md     |   2 +-
 .../bg/5-Data-Science-In-Cloud/README.md      |   4 +-
 .../20-Real-World-Examples/README.md          |   2 +-
 .../20-Real-World-Examples/assignment.md      |   2 +-
 translations/bg/README.md                     | 196 +++++++++--------
 translations/bg/sketchnotes/README.md         |   2 +-
 translations/ro/.co-op-translator.json        |  22 +-
 .../01-defining-data-science/README.md        |   4 +-
 .../01-defining-data-science/notebook.ipynb   |  91 ++++----
 .../solution/notebook.ipynb                   |  87 ++++----
 .../04-stats-and-probability/README.md        |  14 +-
 translations/ro/1-Introduction/README.md      |   2 +-
 .../2-Working-With-Data/07-python/README.md   |  12 +-
 .../07-python/notebook-covidspread.ipynb      | 109 +++++-----
 translations/ro/2-Working-With-Data/README.md |   2 +-
 .../12-visualization-relationships/README.md  |  14 +-
 .../R/09-visualization-quantities/README.md   |  16 +-
 .../10-visualization-distributions/README.md  |  20 +-
 .../R/11-visualization-proportions/README.md  |   6 +-
 .../12-visualization-relationships/README.md  |  14 +-
 .../R/13-meaningful-vizualizations/README.md  |  16 +-
 .../ro/3-Data-Visualization/README.md         |   2 +-
 .../14-Introduction/README.md                 |   4 +-
 .../ro/4-Data-Science-Lifecycle/README.md     |   2 +-
 .../ro/5-Data-Science-In-Cloud/README.md      |   4 +-
 .../20-Real-World-Examples/README.md          |   2 +-
 .../20-Real-World-Examples/assignment.md      |   2 +-
 translations/ro/README.md                     | 200 +++++++++--------
 translations/ro/sketchnotes/README.md         |   2 +-
 translations/sk/.co-op-translator.json        |  22 +-
 .../01-defining-data-science/README.md        |   4 +-
 .../01-defining-data-science/notebook.ipynb   |  87 ++++----
 .../solution/notebook.ipynb                   |  88 ++++----
 .../04-stats-and-probability/README.md        |  14 +-
 translations/sk/1-Introduction/README.md      |   2 +-
 .../2-Working-With-Data/07-python/README.md   |  12 +-
 .../07-python/notebook-covidspread.ipynb      | 107 +++++----
 translations/sk/2-Working-With-Data/README.md |   2 +-
 .../12-visualization-relationships/README.md  |  14 +-
 .../R/09-visualization-quantities/README.md   |  16 +-
 .../10-visualization-distributions/README.md  |  20 +-
 .../R/11-visualization-proportions/README.md  |   6 +-
 .../12-visualization-relationships/README.md  |  14 +-
 .../R/13-meaningful-vizualizations/README.md  |  16 +-
 .../sk/3-Data-Visualization/README.md         |   2 +-
 .../14-Introduction/README.md                 |   4 +-
 .../sk/4-Data-Science-Lifecycle/README.md     |   2 +-
 .../sk/5-Data-Science-In-Cloud/README.md      |   4 +-
 .../20-Real-World-Examples/README.md          |   2 +-
 .../20-Real-World-Examples/assignment.md      |   2 +-
 translations/sk/README.md                     | 204 +++++++++---------
 translations/sk/sketchnotes/README.md         |   2 +-
 69 files changed, 986 insertions(+), 949 deletions(-)

diff --git a/translations/bg/.co-op-translator.json b/translations/bg/.co-op-translator.json
index d78bd218..e0d2845f 100644
--- a/translations/bg/.co-op-translator.json
+++ b/translations/bg/.co-op-translator.json
@@ -11,12 +11,24 @@
     "source_file": "1-Introduction/01-defining-data-science/assignment.md",
     "language_code": "bg"
   },
+  "1-Introduction/01-defining-data-science/notebook.ipynb": {
+    "original_hash": "8f5eb7b3f7cc89e6d98fb32e1de65dec",
+    "translation_date": "2026-02-27T11:39:44+00:00",
+    "source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
+    "language_code": "bg"
+  },
   "1-Introduction/01-defining-data-science/solution/assignment.md": {
     "original_hash": "a8f79b9c0484c35b4f26e8aec7fc4d56",
     "translation_date": "2025-08-26T15:27:07+00:00",
     "source_file": "1-Introduction/01-defining-data-science/solution/assignment.md",
     "language_code": "bg"
   },
+  "1-Introduction/01-defining-data-science/solution/notebook.ipynb": {
+    "original_hash": "090bbfbfcb0c40d3d6e3236f836164ea",
+    "translation_date": "2026-02-27T11:40:22+00:00",
+    "source_file": "1-Introduction/01-defining-data-science/solution/notebook.ipynb",
+    "language_code": "bg"
+  },
   "1-Introduction/02-ethics/README.md": {
     "original_hash": "58860ce9a4b8a564003d2752f7c72851",
     "translation_date": "2025-10-03T16:59:02+00:00",
@@ -95,6 +107,12 @@
     "source_file": "2-Working-With-Data/07-python/assignment.md",
     "language_code": "bg"
   },
+  "2-Working-With-Data/07-python/notebook-covidspread.ipynb": {
+    "original_hash": "6335cccba01dc6ad7b15aba7a8c73f38",
+    "translation_date": "2026-02-27T11:41:30+00:00",
+    "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
+    "language_code": "bg"
+  },
   "2-Working-With-Data/08-data-preparation/README.md": {
     "original_hash": "1b560955ff39a2bcf2a049fce474a951",
     "translation_date": "2025-09-05T18:41:23+00:00",
@@ -360,8 +378,8 @@
     "language_code": "bg"
   },
   "README.md": {
-    "original_hash": "9204a2806964384a56f5cb0f22bbe953",
-    "translation_date": "2026-02-06T08:59:31+00:00",
+    "original_hash": "f671e295a294a2559fc59d1524e001b4",
+    "translation_date": "2026-02-27T11:47:24+00:00",
     "source_file": "README.md",
     "language_code": "bg"
   },
diff --git a/translations/bg/1-Introduction/01-defining-data-science/README.md b/translations/bg/1-Introduction/01-defining-data-science/README.md
index 579f36c4..35091c90 100644
--- a/translations/bg/1-Introduction/01-defining-data-science/README.md
+++ b/translations/bg/1-Introduction/01-defining-data-science/README.md
@@ -6,7 +6,7 @@
 
 ---
 
-[![Видео за определение на науката за данни](../../../../translated_images/bg/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I)
+[![Видео за определение на науката за данни](../../../../translated_images/bg/video-def-ds.6623ee2392ef1abf.webp)](https://youtu.be/beZ7Mb_oz9I)
 
 ## [Тест преди лекцията](https://ff-quizzes.netlify.app/en/ds/quiz/0)
 
@@ -142,7 +142,7 @@
 
 В това предизвикателство ще се опитаме да намерим концепции, свързани с областта на науката за данни, като разгледаме текстове. Ще вземем статия от Wikipedia за науката за данни, ще изтеглим и обработим текста, и след това ще създадем облак от думи като този:
 
-![Облак от думи за науката за данни](../../../../translated_images/bg/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png)
+![Облак от думи за науката за данни](../../../../translated_images/bg/ds_wordcloud.664a7c07dca57de0.webp)
 
 Посетете [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), за да разгледате кода. Можете също така да изпълните кода и да видите как той извършва всички трансформации на данни в реално време.
 
diff --git a/translations/bg/1-Introduction/01-defining-data-science/notebook.ipynb b/translations/bg/1-Introduction/01-defining-data-science/notebook.ipynb
index 705e5ea6..2528c842 100644
--- a/translations/bg/1-Introduction/01-defining-data-science/notebook.ipynb
+++ b/translations/bg/1-Introduction/01-defining-data-science/notebook.ipynb
@@ -3,15 +3,15 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Предизвикателство: Анализ на текст за наука за данни\n",
-    "\n",
-    "В този пример ще направим просто упражнение, което обхваща всички стъпки на традиционния процес в науката за данни. Не е необходимо да пишете код, просто можете да кликнете върху клетките по-долу, за да ги изпълните и да наблюдавате резултата. Като предизвикателство, се насърчавате да изпробвате този код с различни данни.\n",
-    "\n",
-    "## Цел\n",
-    "\n",
-    "В този урок обсъждахме различни концепции, свързани с науката за данни. Нека се опитаме да открием още свързани концепции, като направим **текстов анализ**. Ще започнем с текст за науката за данни, ще извлечем ключови думи от него и след това ще се опитаме да визуализираме резултата.\n",
-    "\n",
-    "Като текст ще използвам страницата за науката за данни от Wikipedia:\n"
+    "# Предизвикателство: Анализ на текст за дата сайънс\r\n",
+    "\r\n",
+    "В този пример, нека направим просто упражнение, покриващо всички стъпки на традиционния процес на дата сайънс. Не е необходимо да пишете код, просто можете да кликнете върху клетките по-долу, за да ги изпълните и да наблюдавате резултата. Като предизвикателство, се насърчавате да опитате този код с различни данни.\r\n",
+    "\r\n",
+    "## Цел\r\n",
+    "\r\n",
+    "В този урок обсъждахме различни концепции, свързани с дата сайънс. Нека опитаме да открием още свързани концепции чрез **текстов минен анализ**. Ще започнем с текст за дата сайънс, ще извлечем ключови думи от него и после ще опитаме да визуализираме резултата.\r\n",
+    "\r\n",
+    "Като текст ще използвам страницата за Data Science от Уикипедия:\n"
    ],
    "metadata": {}
   },
@@ -34,7 +34,7 @@
    "source": [
     "## Стъпка 1: Получаване на данните\n",
     "\n",
-    "Първата стъпка във всеки процес на анализ на данни е получаването на данните. Ще използваме библиотеката `requests`, за да направим това:\n"
+    "Първата стъпка във всеки процес на наука за данните е получаването на данните. Ще използваме библиотеката `requests` за тази цел:\n"
    ],
    "metadata": {}
   },
@@ -68,43 +68,41 @@
    "source": [
     "## Стъпка 2: Преобразуване на данните\n",
     "\n",
-    "Следващата стъпка е да преобразуваме данните във форма, подходяща за обработка. В нашия случай сме изтеглили HTML изходния код от страницата и трябва да го преобразуваме в обикновен текст.\n",
+    "Следващата стъпка е преобразуването на данните във формата, подходящ за обработка. В нашия случай, ние сме изтеглили HTML изходния код от страницата и трябва да го преобразуваме в обикновен текст.\n",
     "\n",
-    "Има много начини, по които това може да се направи. Ще използваме най-простия вграден обект [HTMLParser](https://docs.python.org/3/library/html.parser.html) от Python. Трябва да наследим класа `HTMLParser` и да дефинираме код, който ще събира целия текст вътре в HTML таговете, с изключение на таговете `<script>` и `<style>`.\n"
+    "Има много начини, по които това може да бъде направено. Ще използваме [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/), популярна Python библиотека за парсване на HTML. BeautifulSoup ни позволява да насочваме към конкретни HTML елементи, така че да се фокусираме върху основното съдържание на статията от Wikipedia и да намалим някои навигационни менюта, странични панели, футъри и друго нерелевантно съдържание (въпреки че може да остане част от шаблонен текст).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Първо, трябва да инсталираме библиотеката BeautifulSoup за парсване на HTML:\n"
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import sys\r\n",
+    "!{sys.executable} -m pip install beautifulsoup4"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 64,
    "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
+    "from bs4 import BeautifulSoup\r\n\r\n# Parse the HTML content\r\nsoup = BeautifulSoup(text, 'html.parser')\r\n\r\n# Extract only the main article content from Wikipedia\r\n# Wikipedia uses 'mw-parser-output' class for the main article content\r\ncontent = soup.find('div', class_='mw-parser-output')\r\n\r\ndef clean_wikipedia_content(content_node):\r\n    \"\"\"Remove common non-article elements from a Wikipedia content node.\"\"\"\r\n    # Strip jump links, navboxes, reference lists/superscripts, edit sections, TOC, sidebars, etc.\r\n    selectors = [\r\n        '.mw-jump-link',\r\n        '.navbox',\r\n        '.reflist',\r\n        'sup.reference',\r\n        '.mw-editsection',\r\n        '.hatnote',\r\n        '.metadata',\r\n        '.infobox',\r\n        '#toc',\r\n        '.toc',\r\n        '.sidebar',\r\n    ]\r\n    for selector in selectors:\r\n        for el in content_node.select(selector):\r\n            el.decompose()\r\n\r\nif content:\r\n    # Clean the content node to better approximate article text only.\r\n    clean_wikipedia_content(content)\r\n    text = content.get_text(separator=' ', strip=True)\r\n    print(text[:1000])\r\nelse:\r\n    print(\"Could not find main content. Using full page text.\")\r\n    text = soup.get_text(separator=' ', strip=True)\r\n    print(text[:1000])"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      " Data science - Wikipedia Data science From Wikipedia, the free encyclopedia Jump to navigation Jump to search Interdisciplinary field of study focused on deriving knowledge and insights from data Not to be confused with  information science . The existence of  Comet NEOWISE  (here depicted as a series of red dots) was discovered by analyzing  astronomical survey  data acquired by a  space telescope , the  Wide-field Infrared Survey Explorer . Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning ( classification  •  regression ) Decision trees Ensembles Bagging Boosting Random forest k -NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine \n"
+      "Data science From Wikipedia, the free encyclopedia Interdisciplinary field of study focused on deriving knowledge and insights from data Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain. Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.\n"
      ]
     }
    ],
@@ -115,9 +113,9 @@
    "source": [
     "## Стъпка 3: Получаване на прозрения\n",
     "\n",
-    "Най-важната стъпка е да преобразуваме данните си в някаква форма, от която можем да извлечем прозрения. В нашия случай искаме да извлечем ключови думи от текста и да видим кои ключови думи са по-съществени.\n",
+    "Най-важната стъпка е да преобразуваме нашите данни в някаква форма, от която можем да изведем прозрения. В нашия случай искаме да извлечем ключови думи от текста и да видим кои ключови думи са по-смислени.\n",
     "\n",
-    "Ще използваме библиотеката на Python, наречена [RAKE](https://github.com/aneesha/RAKE), за извличане на ключови думи. Първо, нека инсталираме тази библиотека, ако все още не е налична:\n"
+    "Ще използваме Python библиотеката, наречена [RAKE](https://github.com/aneesha/RAKE), за извличане на ключови думи. Първо, нека инсталираме тази библиотека, ако не е налична:\n"
    ],
    "metadata": {}
   },
@@ -162,7 +160,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Основната функционалност е достъпна от обекта `Rake`, който можем да персонализираме с помощта на някои параметри. В нашия случай ще зададем минималната дължина на ключова дума на 5 символа, минималната честота на ключова дума в документа на 3 и максималния брой думи в ключова дума - на 2. Чувствайте се свободни да експериментирате с други стойности и да наблюдавате резултата.\n"
+    "Основната функционалност е достъпна от обекта `Rake`, който можем да персонализираме с помощта на някои параметри. В нашия случай ще зададем минималната дължина на ключова дума да бъде 5 символа, минималната честота на ключова дума в документа да бъде 3 и максималния брой думи в ключова дума - 2. Чувствайте се свободни да експериментирате с други стойности и да наблюдавате резултата.\n"
    ],
    "metadata": {}
   },
@@ -211,11 +209,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Получихме списък с термини заедно със съответната степен на важност. Както можете да видите, най-значимите дисциплини, като машинно обучение и големи данни, са представени в списъка на водещи позиции.\n",
+    "Получихме списък с термини заедно със съответната степен на важност. Както виждате, най-значимите дисциплини, като машинно обучение и големи данни, присъстват в списъка на водещи позиции.\n",
     "\n",
     "## Стъпка 4: Визуализиране на резултата\n",
     "\n",
-    "Хората разбират данните най-добре, когато са представени визуално. Затова често има смисъл да се визуализират данните, за да се извлекат определени прозрения. Можем да използваме библиотеката `matplotlib` в Python, за да начертаем проста дистрибуция на ключовите думи с тяхната релевантност:\n"
+    "Хората най-добре разбират данните във визуална форма. Поради това често има смисъл да се визуализират данните, за да се изведат някои изводи. Можем да използваме библиотеката `matplotlib` в Python, за да начертаем просто разпределение на ключовите думи с тяхната релевантност:\n"
    ],
    "metadata": {}
   },
@@ -252,7 +250,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Има обаче още по-добър начин за визуализиране на честотата на думите - използвайки **Word Cloud**. Ще трябва да инсталираме друга библиотека, за да създадем облак от думи от нашия списък с ключови думи.\n"
+    "Има обаче още по-добър начин да визуализираме честотата на думите - чрез **Word Cloud**. Ще трябва да инсталираме друга библиотека, за да начертаем word cloud от нашия списък с ключови думи.\n"
    ],
    "metadata": {}
   },
@@ -268,7 +266,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "`WordCloud` обектът е отговорен за приемането на оригинален текст или предварително изчислен списък от думи с техните честоти и връща изображение, което след това може да бъде показано с помощта на `matplotlib`:\n"
+    "`WordCloud` обектът е отговорен за приемане на оригинален текст или предварително изчислен списък с думи и техните честоти и връща изображение, което след това може да бъде показано с помощта на `matplotlib`:\n"
    ],
    "metadata": {}
   },
@@ -312,7 +310,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Можем също да предадем оригиналния текст на `WordCloud` - нека видим дали можем да получим подобен резултат:\n"
+    "Можем също да подадем оригиналния текст на `WordCloud` - нека видим дали ще успеем да получим подобен резултат:\n"
    ],
    "metadata": {}
   },
@@ -372,11 +370,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Можете да видите, че облакът от думи сега изглежда по-впечатляващ, но също така съдържа много шум (например несвързани думи като `Retrieved on`). Освен това получаваме по-малко ключови думи, състоящи се от две думи, като *data scientist* или *computer science*. Това е така, защото алгоритъмът RAKE се справя много по-добре с избора на подходящи ключови думи от текста. Този пример илюстрира важността на предварителната обработка и почистването на данни, тъй като ясната картина в края ще ни позволи да вземаме по-добри решения.\n",
+    "Вече можете да видите, че облакът от думи изглежда по-впечатляващ, но той също съдържа много шум (например, несвързани думи като `Retrieved on`). Също така получаваме по-малко ключови думи, съставени от две думи, като *data scientist* или *computer science*. Това се дължи на факта, че алгоритъмът RAKE се справя много по-добре с избора на добри ключови думи от текста. Този пример илюстрира важността на предварителната обработка и почистване на данните, защото ясната картина накрая ще ни позволи да вземаме по-добри решения.\n",
     "\n",
-    "В това упражнение преминахме през прост процес на извличане на смисъл от текст в Wikipedia, под формата на ключови думи и облак от думи. Този пример е доста прост, но добре демонстрира всички типични стъпки, които един специалист по данни предприема при работа с данни, започвайки от придобиването на данни до визуализацията.\n",
+    "В това упражнение преминахме през прост процес на извличане на смисъл от текста на Уикипедия, под формата на ключови думи и облак от думи. Този пример е доста прост, но добре демонстрира всички типични стъпки, които един data scientist предприема при работа с данни, започвайки от придобиването на данни и стигайки до визуализацията.\n",
     "\n",
-    "В нашия курс ще обсъдим всички тези стъпки подробно.\n"
+    "В нашия курс ще обсъдим всички тези стъпки в детайли.\n"
    ],
    "metadata": {}
   },
@@ -394,7 +392,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за каквито и да е недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Отказ от отговорност**:\nТози документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, имайте предвид, че автоматичните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за всякакви недоразумения или неправилни тълкувания, произтичащи от използването на този превод.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -418,12 +416,6 @@
   },
   "interpreter": {
    "hash": "c28e7b6bf4e5b397b8288a85bf0a94ea8d3585ce2b01919feb195678ec71581b"
-  },
-  "coopTranslator": {
-   "original_hash": "50c0f0a5204a18405611cbe7e0fec56b",
-   "translation_date": "2025-09-01T23:29:03+00:00",
-   "source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
-   "language_code": "bg"
   }
  },
  "nbformat": 4,
diff --git a/translations/bg/1-Introduction/01-defining-data-science/solution/notebook.ipynb b/translations/bg/1-Introduction/01-defining-data-science/solution/notebook.ipynb
index 8c6cdc71..5247e957 100644
--- a/translations/bg/1-Introduction/01-defining-data-science/solution/notebook.ipynb
+++ b/translations/bg/1-Introduction/01-defining-data-science/solution/notebook.ipynb
@@ -3,17 +3,17 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Предизвикателство: Анализ на текст за наука за данни\n",
+    "# Предизвикателство: Анализ на текст за Данни Наука\n",
     "\n",
-    "> *В тази тетрадка експериментираме с използването на различни URL адреси - статия от Уикипедия за машинно обучение. Можете да видите, че за разлика от науката за данни, тази статия съдържа много термини, което прави анализа по-проблематичен. Трябва да измислим друг начин за почистване на данните след извличането на ключови думи, за да се отървем от някои често срещани, но незначителни словосъчетания.*\n",
+    "> *В този бележник експериментираме с използването на различен URL - статия от Уикипедия за Машинно Обучение. Можете да видите, че за разлика от Данни Наука, тази статия съдържа много термини, което прави анализа по-проблематичен. Трябва да измислим друг начин за изчистване на данните след извличането на ключови думи, за да се отървем от някои чести, но незначими комбинации от думи.*\n",
     "\n",
-    "В този пример ще направим просто упражнение, което обхваща всички стъпки на традиционния процес в науката за данни. Не е необходимо да пишете код, просто можете да кликнете върху клетките по-долу, за да ги изпълните и да наблюдавате резултата. Като предизвикателство, ви насърчаваме да изпробвате този код с различни данни.\n",
+    "В този пример нека направим просто упражнение, което обхваща всички стъпки на традиционния процес на наука за данни. Не е нужно да пишете код, можете просто да кликнете върху клетките по-долу, за да ги изпълните и да наблюдавате резултата. Като предизвикателство, насърчаваме ви да опитате този код с различни данни.\n",
     "\n",
     "## Цел\n",
     "\n",
-    "В този урок обсъждахме различни концепции, свързани с науката за данни. Нека се опитаме да открием повече свързани концепции, като направим **текстов анализ**. Ще започнем с текст за науката за данни, ще извлечем ключови думи от него и след това ще се опитаме да визуализираме резултата.\n",
+    "В този урок обсъждахме различни концепции, свързани с Данни Наука. Нека опитаме да открием още свързани концепции чрез **текстов минен анализ**. Ще започнем с текст за Данни Наука, ще извлечем ключови думи от него и след това ще се опитаме да визуализираме резултата.\n",
     "\n",
-    "Като текст ще използвам страницата за науката за данни от Уикипедия:\n"
+    "Като текст ще използвам страницата за Данни Наука от Уикипедия:\n"
    ],
    "metadata": {}
   },
@@ -37,7 +37,7 @@
    "source": [
     "## Стъпка 1: Получаване на данните\n",
     "\n",
-    "Първата стъпка във всеки процес на анализ на данни е получаването на данните. Ще използваме библиотеката `requests`, за да направим това:\n"
+    "Първата стъпка във всеки процес на анализ на данни е получаването на данните. Ще използваме библиотеката `requests` за това:\n"
    ],
    "metadata": {}
   },
@@ -69,45 +69,43 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Стъпка 2: Преобразуване на данните\n",
+    "## Стъпка 2: Трансформиране на данните\n",
     "\n",
-    "Следващата стъпка е да преобразуваме данните във форма, подходяща за обработка. В нашия случай сме изтеглили HTML изходния код от страницата и трябва да го преобразуваме в обикновен текст.\n",
+    "Следващата стъпка е да преобразуваме данните в подходяща за обработка форма. В нашия случай изтеглихме HTML изходния код от страницата и трябва да го преобразуваме в обикновен текст.\n",
     "\n",
-    "Има много начини, по които това може да се направи. Ще използваме най-простия вграден обект [HTMLParser](https://docs.python.org/3/library/html.parser.html) от Python. Трябва да наследим класа `HTMLParser` и да дефинираме код, който ще събира целия текст вътре в HTML таговете, с изключение на `<script>` и `<style>` таговете.\n"
+    "Има много начини да се направи това. Ще използваме [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/), популярна Python библиотека за парсване на HTML. BeautifulSoup ни позволява да насочваме определени HTML елементи, така че да се съсредоточим върху основното съдържание на статията от Wikipedia и да намалим някои навигационни менюта, странични ленти, футъри и друго неуместно съдържание (въпреки че някакъв шаблонен текст все още може да остане).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Първо, трябва да инсталираме библиотеката BeautifulSoup за парсване на HTML:\n"
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import sys\r\n",
+    "!{sys.executable} -m pip install beautifulsoup4"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
+    "from bs4 import BeautifulSoup\r\n\r\n# Parse the HTML content\r\nsoup = BeautifulSoup(text, 'html.parser')\r\n\r\n# Extract only the main article content from Wikipedia\r\n# Wikipedia uses 'mw-parser-output' class for the main article content\r\ncontent = soup.find('div', class_='mw-parser-output')\r\n\r\ndef clean_wikipedia_content(content_node):\r\n    \"\"\"Remove common non-article elements from a Wikipedia content node.\"\"\"\r\n    # Strip jump links, navboxes, reference lists/superscripts, edit sections, TOC, sidebars, etc.\r\n    selectors = [\r\n        '.mw-jump-link',\r\n        '.navbox',\r\n        '.reflist',\r\n        'sup.reference',\r\n        '.mw-editsection',\r\n        '.hatnote',\r\n        '.metadata',\r\n        '.infobox',\r\n        '#toc',\r\n        '.toc',\r\n        '.sidebar',\r\n    ]\r\n    for selector in selectors:\r\n        for el in content_node.select(selector):\r\n            el.decompose()\r\n\r\nif content:\r\n    # Clean the content node to better approximate article text only.\r\n    clean_wikipedia_content(content)\r\n    text = content.get_text(separator=' ', strip=True)\r\n    print(text[:1000])\r\nelse:\r\n    print(\"Could not find main content. Using full page text.\")\r\n    text = soup.get_text(separator=' ', strip=True)\r\n    print(text[:1000])"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      " Machine learning - Wikipedia Machine learning From Wikipedia, the free encyclopedia Jump to navigation Jump to search Study of algorithms that improve automatically through experience For the journal, see  Machine Learning (journal) . \"Statistical learning\" redirects here. For statistical learning in linguistics, see  statistical learning in language acquisition . Part of a series on Artificial intelligence Major goals Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms Philosophy Ethics Existential risk Turing test Chinese room Control problem Friendly AI History Timeline Progress AI winter Technology Applications Projects Programming languages Glossary Glossary v t e Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection Data Cleaning AutoML Associ\n"
+      "Machine learning From Wikipedia, the free encyclopedia Study of algorithms that improve automatically through experience Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.\n"
      ]
     }
    ],
@@ -116,11 +114,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Стъпка 3: Получаване на прозрения\n",
+    "## Step 3: Получаване на прозрения\n",
     "\n",
-    "Най-важната стъпка е да превърнем нашите данни във форма, от която можем да извлечем прозрения. В нашия случай искаме да извлечем ключови думи от текста и да видим кои ключови думи са по-съществени.\n",
+    "Най-важната стъпка е да превърнем нашите данни в някаква форма, от която можем да изведем прозрения. В нашия случай искаме да извлечем ключови думи от текста и да видим кои ключови думи са по-съществени.\n",
     "\n",
-    "Ще използваме Python библиотека, наречена [RAKE](https://github.com/aneesha/RAKE), за извличане на ключови думи. Първо, нека инсталираме тази библиотека, ако не е налична:\n"
+    "Ще използваме Python библиотека, наречена [RAKE](https://github.com/aneesha/RAKE) за извличане на ключови думи. Първо, нека инсталираме тази библиотека в случай, че не е налична: \n"
    ],
    "metadata": {}
   },
@@ -172,7 +170,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Основната функционалност е достъпна от обекта `Rake`, който можем да персонализираме с помощта на някои параметри. В нашия случай ще зададем минималната дължина на ключова дума на 5 символа, минималната честота на ключова дума в документа на 3 и максималния брой думи в ключова дума - на 2. Чувствайте се свободни да експериментирате с други стойности и да наблюдавате резултата.\n"
+    "Основната функционалност е налична от обекта `Rake`, който можем да персонализираме чрез някои параметри. В нашия случай ще зададем минималната дължина на ключова дума да бъде 5 символа, минималната честота на ключова дума в документа да е 3 и максималния брой думи в ключова дума - 2. Чувствайте се свободни да експериментирате с други стойности и да наблюдавате резултата.\n"
    ],
    "metadata": {}
   },
@@ -353,11 +351,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Получихме списък с термини заедно със свързаната степен на важност. Както можете да видите, най-важните дисциплини, като машинно обучение и големи данни, са представени в списъка на водещи позиции.\n",
+    "\n",
+    "Получихме списък с термини заедно със съответната степен на важност. Както можете да видите, най-значимите дисциплини, като машинно обучение и големи данни, присъстват в списъка на водещи позиции.\n",
     "\n",
     "## Стъпка 4: Визуализация на резултата\n",
     "\n",
-    "Хората най-добре интерпретират данните в визуална форма. Затова често има смисъл да се визуализират данните, за да се извлекат някои прозрения. Можем да използваме библиотеката `matplotlib` в Python, за да начертаем проста дистрибуция на ключовите думи с тяхната релевантност:\n"
+    "Хората могат най-добре да интерпретират данните във визуална форма. Затова често е разумно да се визуализират данните, за да се извлекат някои изводи. Можем да използваме библиотеката `matplotlib` в Python, за да начертаем проста разпределение на ключовите думи с тяхната релевантност:\n"
    ],
    "metadata": {}
   },
@@ -392,7 +391,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Има обаче още по-добър начин за визуализиране на честотата на думите - използване на **Word Cloud**. Ще трябва да инсталираме друга библиотека, за да създадем облак от думи от нашия списък с ключови думи.\n"
+    "Има обаче още по-добър начин за визуализиране на честотата на думите - чрез **Word Cloud**. Ще трябва да инсталираме още една библиотека, за да начертаем word cloud от нашия списък с ключови думи.\n"
    ],
    "metadata": {}
   },
@@ -408,7 +407,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "`WordCloud` обектът е отговорен за приемането на оригинален текст или предварително изчислен списък от думи с техните честоти и връща изображение, което след това може да бъде показано с помощта на `matplotlib`:\n"
+    "Обектът `WordCloud` отговаря за приемане на оригинален текст или предварително изчислен списък с думи и техните честоти, и връща изображение, което след това може да бъде показано с помощта на `matplotlib`:\n"
    ],
    "metadata": {}
   },
@@ -441,7 +440,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Можем също да подадем оригиналния текст на `WordCloud` - нека видим дали ще успеем да получим подобен резултат:\n"
+    "Можем също така да подадем оригиналния текст на `WordCloud` - нека видим дали ще успеем да получим подобен резултат:\n"
    ],
    "metadata": {}
   },
@@ -490,11 +489,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Можете да видите, че облакът от думи сега изглежда по-впечатляващ, но също така съдържа много шум (например несвързани думи като `Retrieved on`). Освен това получаваме по-малко ключови думи, състоящи се от две думи, като *data scientist* или *computer science*. Това е така, защото алгоритъмът RAKE се справя много по-добре с избора на подходящи ключови думи от текста. Този пример илюстрира важността на предварителната обработка и почистването на данни, защото ясната картина в края ще ни позволи да вземаме по-добри решения.\n",
+    "Вече можете да видите, че облакът от думи изглежда по-впечатляващ, но също така съдържа много шум (напр. нерелевантни думи като `Retrieved on`). Също така получаваме по-малко ключови думи, съставени от две думи, като *data scientist* или *computer science*. Това е защото алгоритъмът RAKE извършва много по-добра селекция на добри ключови думи от текста. Този пример илюстрира значението на предварителната обработка и почистване на данните, тъй като ясната картина накрая ще ни позволи да вземем по-добри решения.\n",
     "\n",
-    "В това упражнение преминахме през прост процес на извличане на смисъл от текст в Уикипедия под формата на ключови думи и облак от думи. Този пример е доста опростен, но добре демонстрира всички типични стъпки, които един специалист по данни предприема при работа с данни, започвайки от придобиването на данни до визуализацията.\n",
+    "В това упражнение преминахме през прост процес на извличане на смисъл от текста на Уикипедия, под формата на ключови думи и облак от думи. Този пример е доста прост, но добре демонстрира всички типични стъпки, които един data scientist ще предприеме при работа с данни, започвайки от придобиването на данните до визуализацията.\n",
     "\n",
-    "В нашия курс ще обсъдим всички тези стъпки в детайли.\n"
+    "В нашия курс ще обсъдим всички тези стъпки подробно. \n"
    ],
    "metadata": {}
   },
@@ -502,7 +501,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за каквито и да е недоразумения или неправилни тълкувания, възникнали от използването на този превод.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -526,12 +525,6 @@
   },
   "interpreter": {
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  },
-  "coopTranslator": {
-   "original_hash": "dbdf34788efab64e5d817f1df38965a8",
-   "translation_date": "2025-09-01T23:40:03+00:00",
-   "source_file": "1-Introduction/01-defining-data-science/solution/notebook.ipynb",
-   "language_code": "bg"
   }
  },
  "nbformat": 4,
diff --git a/translations/bg/1-Introduction/04-stats-and-probability/README.md b/translations/bg/1-Introduction/04-stats-and-probability/README.md
index 4e7e55c9..cbd81341 100644
--- a/translations/bg/1-Introduction/04-stats-and-probability/README.md
+++ b/translations/bg/1-Introduction/04-stats-and-probability/README.md
@@ -6,7 +6,7 @@
 
 Статистиката и теорията на вероятностите са две силно свързани области на математиката, които са изключително важни за науката за данни. Възможно е да се работи с данни без задълбочени математически познания, но все пак е добре да се знаят поне основните концепции. Тук ще представим кратко въведение, което ще ви помогне да започнете.
 
-[![Въвеждащо видео](../../../../translated_images/bg/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw)
+[![Въвеждащо видео](../../../../translated_images/bg/video-prob-and-stats.e4282e5efa2f2543.webp)](https://youtu.be/Z5Zy85g4Yjw)
 
 ## [Тест преди лекцията](https://ff-quizzes.netlify.app/en/ds/quiz/6)
 
@@ -30,7 +30,7 @@
 
 Можем да говорим само за вероятността променливата да попадне в даден интервал от стойности, напр. P(t<sub>1</sub>≤X<t<sub>2</sub>). В този случай разпределението на вероятностите се описва чрез **функция на плътността на вероятностите** p(x), така че
 
-![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/bg/probability-density.a8aad29f17a14afb519b407c7b6edeb9f3f9aa5f69c9e6d9445f604e5f8a2bf7.png)
+![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/bg/probability-density.a8aad29f17a14afb.webp)
 
 Непрекъснат аналог на равномерното разпределение се нарича **непрекъснато равномерно**, което се дефинира върху краен интервал. Вероятността стойността X да попадне в интервал с дължина l е пропорционална на l и достига до 1.
 
@@ -73,11 +73,11 @@
 
 Ето диаграма кутия и мустаци, показваща средна стойност, медиана и квартилни стойности за нашите данни:
 
-![Диаграма кутия и мустаци за тегло](../../../../translated_images/bg/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png)
+![Диаграма кутия и мустаци за тегло](../../../../translated_images/bg/weight-boxplot.1dbab1c03af26f8a.webp)
 
 Тъй като нашите данни съдържат информация за различни **ролите** на играчите, можем също да направим диаграма кутия и мустаци по роли - това ще ни позволи да добием представа как стойностите на параметрите се различават между ролите. Този път ще разгледаме височината:
 
-![Диаграма кутия и мустаци по роли](../../../../translated_images/bg/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png)
+![Диаграма кутия и мустаци по роли](../../../../translated_images/bg/boxplot_byrole.036b27a1c3f52d42.webp)
 
 Тази диаграма предполага, че средно височината на първите базови играчи е по-голяма от височината на вторите базови играчи. По-късно в този урок ще научим как можем да тестваме тази хипотеза по-формално и как да демонстрираме, че нашите данни са статистически значими, за да покажем това.
 
@@ -85,7 +85,7 @@
 
 За да видим какво е разпределението на нашите данни, можем да начертаем графика, наречена **хистограма**. Оста X ще съдържа брой различни интервали за тегло (т.нар. **кофи**), а вертикалната ос ще показва броя на случаите, когато извадката на случайната променлива е попаднала в даден интервал.
 
-![Хистограма на данни от реалния свят](../../../../translated_images/bg/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png)
+![Хистограма на данни от реалния свят](../../../../translated_images/bg/weight-histogram.bfd00caf7fc30b14.webp)
 
 От тази хистограма можете да видите, че всички стойности са съсредоточени около определено средно тегло, и колкото по-далеч от това тегло отиваме - толкова по-малко тегла с тази стойност се срещат. Т.е., много малко вероятно е теглото на бейзболен играч да бъде много различно от средното тегло. Дисперсията на теглата показва степента, до която теглата вероятно се различават от средното.
 
@@ -102,7 +102,7 @@ samples = np.random.normal(mean,std,1000)
 
 Ако начертаем хистограмата на генерираните извадки, ще видим картина, много подобна на показаната по-горе. И ако увеличим броя на извадките и броя на кофите, можем да генерираме картина на нормално разпределение, която е по-близка до идеалната:
 
-![Нормално разпределение със средна стойност=0 и стандартно отклонение=1](../../../../translated_images/bg/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png)
+![Нормално разпределение със средна стойност=0 и стандартно отклонение=1](../../../../translated_images/bg/normal-histogram.dfae0d67c202137d.webp)
 
 *Нормално разпределение със средна стойност=0 и стандартно отклонение=1*
 
@@ -224,7 +224,7 @@ array([[1.        , 0.52959196],
 
 В нашия случай стойността 0.53 показва, че има известна корелация между теглото и височината на човек. Можем също да направим диаграма на разсейване на една стойност спрямо другата, за да видим връзката визуално:
 
-![Връзка между тегло и височина](../../../../translated_images/bg/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png)
+![Връзка между тегло и височина](../../../../translated_images/bg/weight-height-relationship.3f06bde4ca2aba99.webp)
 
 > Повече примери за корелация и ковариация можете да намерите в [придружаващата тетрадка](notebook.ipynb).
 
diff --git a/translations/bg/1-Introduction/README.md b/translations/bg/1-Introduction/README.md
index ebf451ff..cf8f2fe2 100644
--- a/translations/bg/1-Introduction/README.md
+++ b/translations/bg/1-Introduction/README.md
@@ -1,6 +1,6 @@
 # Въведение в науката за данните
 
-![данни в действие](../../../translated_images/bg/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)
+![данни в действие](../../../translated_images/bg/data.48e22bb7617d8d92.webp)
 > Снимка от <a href="https://unsplash.com/@dawson2406?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Stephen Dawson</a> на <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 В тези уроци ще откриете как се дефинира науката за данните и ще научите за етичните съображения, които трябва да бъдат взети предвид от специалиста по данни. Ще научите също как се дефинират данните и ще се запознаете с основите на статистиката и вероятностите, които са основни академични области на науката за данните.
diff --git a/translations/bg/2-Working-With-Data/07-python/README.md b/translations/bg/2-Working-With-Data/07-python/README.md
index c1a00422..580fc1d2 100644
--- a/translations/bg/2-Working-With-Data/07-python/README.md
+++ b/translations/bg/2-Working-With-Data/07-python/README.md
@@ -4,7 +4,7 @@
 | :---------------------------------------------------------------------------------------------------: |
 |                 Работа с Python - _Скетч от [@nitya](https://twitter.com/nitya)_                     |
 
-[![Въвеждащо видео](../../../../translated_images/bg/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y)
+[![Въвеждащо видео](../../../../translated_images/bg/video-ds-python.245247dc811db8e4.webp)](https://youtu.be/dZjWOGbsN4Y)
 
 Докато базите данни предлагат много ефективни начини за съхранение на данни и тяхното извличане чрез език за заявки, най-гъвкавият начин за обработка на данни е да напишете собствена програма за манипулиране на данните. В много случаи използването на заявка към база данни би било по-ефективно. Въпреки това, в някои случаи, когато е необходима по-сложна обработка на данни, това не може лесно да се направи с SQL. 
 Обработката на данни може да бъде програмирана на всеки език за програмиране, но има определени езици, които са по-високо ниво, когато става въпрос за работа с данни. Специалистите по данни обикновено предпочитат един от следните езици:
@@ -66,7 +66,7 @@ print(f"Length of index is {len(idx)}")
 items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)
 items_sold.plot()
 ```
-![Графика на времеви серии](../../../../translated_images/bg/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png)
+![Графика на времеви серии](../../../../translated_images/bg/timeseries-1.80de678ab1cf727e.webp)
 
 Сега да предположим, че всяка седмица организираме парти за приятели и вземаме допълнителни 10 опаковки сладолед за партито. Можем да създадем друга серия, индексирана по седмици, за да демонстрираме това:
 ```python
@@ -77,7 +77,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W"
 total_items = items_sold.add(additional_items,fill_value=0)
 total_items.plot()
 ```
-![Графика на времеви серии](../../../../translated_images/bg/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png)
+![Графика на времеви серии](../../../../translated_images/bg/timeseries-2.aae51d575c55181c.webp)
 
 > **Забележка**: Не използваме простия синтаксис `total_items+additional_items`. Ако го направим, ще получим много стойности `NaN` (*Not a Number*) в резултатната серия. Това е така, защото липсват стойности за някои точки от индекса в серията `additional_items`, и добавянето на `NaN` към нещо води до `NaN`. Затова трябва да зададем параметъра `fill_value` по време на събирането.
 
@@ -86,7 +86,7 @@ total_items.plot()
 monthly = total_items.resample("1M").mean()
 ax = monthly.plot(kind='bar')
 ```
-![Месечни средни стойности на времеви серии](../../../../translated_images/bg/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png)
+![Месечни средни стойности на времеви серии](../../../../translated_images/bg/timeseries-3.f3147cbc8c624881.webp)
 
 ### DataFrame
 
@@ -212,7 +212,7 @@ df = pd.read_csv('file.csv')
 
 Тъй като искаме да демонстрираме как да работим с данни, ви каним да отворите [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) и да го прочетете от началото до края. Можете също така да изпълните клетките и да решите някои предизвикателства, които сме оставили за вас в края.
 
-![COVID Spread](../../../../translated_images/bg/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png)
+![COVID Spread](../../../../translated_images/bg/covidspread.f3d131c4f1d260ab.webp)
 
 > Ако не знаете как да изпълнявате код в Jupyter Notebook, разгледайте [тази статия](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
 
@@ -234,7 +234,7 @@ df = pd.read_csv('file.csv')
 
 Отворете [`notebook-papers.ipynb`](notebook-papers.ipynb) и го прочетете от началото до края. Можете също така да изпълните клетките и да решите някои предизвикателства, които сме оставили за вас в края.
 
-![Covid Medical Treatment](../../../../translated_images/bg/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png)
+![Covid Medical Treatment](../../../../translated_images/bg/covidtreat.b2ba59f57ca45fbc.webp)
 
 ## Обработка на данни от изображения
 
diff --git a/translations/bg/2-Working-With-Data/07-python/notebook-covidspread.ipynb b/translations/bg/2-Working-With-Data/07-python/notebook-covidspread.ipynb
index cc6dacd9..a89836ca 100644
--- a/translations/bg/2-Working-With-Data/07-python/notebook-covidspread.ipynb
+++ b/translations/bg/2-Working-With-Data/07-python/notebook-covidspread.ipynb
@@ -4,11 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Оценка на пандемията от COVID-19\n",
+    "# Оценка на пандемията COVID-19\n",
     "\n",
     "## Зареждане на данни\n",
     "\n",
-    "Ще използваме данни за заразени с COVID-19, предоставени от [Центъра за системни науки и инженерство](https://systems.jhu.edu/) (CSSE) към [Университета Джон Хопкинс](https://jhu.edu/). Данните са налични в [това GitHub хранилище](https://github.com/CSSEGISandData/COVID-19).\n"
+    "Ще използваме данни за инфектирани с COVID-19, предоставени от [Центъра за системни науки и инженерство](https://systems.jhu.edu/) (CSSE) към [Университета Джонс Хопкинс](https://jhu.edu/). Наборът от данни е наличен в [този GitHub репозиторий](https://github.com/CSSEGISandData/COVID-19).\n"
    ]
   },
   {
@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Можем да заредим най-новите данни директно от GitHub, използвайки `pd.read_csv`. Ако по някаква причина данните не са налични, винаги можете да използвате копието, налично локално в папката `data` - просто премахнете коментара от реда по-долу, който дефинира `base_url`:\n"
+    "Можем да заредим най-новите данни директно от GitHub, използвайки `pd.read_csv`. Ако по някаква причина данните не са налични, винаги можете да използвате копието, съхранено локално в папката `data` - просто махнете коментарите от реда по-долу, който дефинира `base_url`:\n"
    ]
   },
   {
@@ -47,7 +47,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Нека сега заредим данните за заразените лица и да видим как изглеждат данните:\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -263,7 +265,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Можем да видим, че всеки ред от таблицата определя броя на заразените лица за всяка държава и/или провинция, а колоните съответстват на дати. Подобни таблици могат да бъдат заредени за други данни, като броя на възстановените и броя на смъртните случаи.\n"
+    "Можем да видим, че всеки ред от таблицата определя броя на заразените лица за всяка страна и/или провинция, а колоните съответстват на дати. Подобни таблици могат да бъдат заредени за други данни, като например броя на оздравелите и броя на починалите.\n"
    ]
   },
   {
@@ -282,7 +284,7 @@
    "source": [
     "## Разбиране на данните\n",
     "\n",
-    "От таблицата по-горе ролята на колоната за провинция не е ясна. Нека разгледаме различните стойности, които се намират в колоната `Province/State`:\n"
+    "От таблицата по-горе ролята на колоната провинция не е ясна. Нека видим различните стойности, които присъстват в колоната `Province/State`:\n"
    ]
   },
   {
@@ -319,7 +321,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "От имената можем да заключим, че страни като Австралия и Китай имат по-подробно разбиване по провинции. Нека потърсим информация за Китай, за да видим примера:\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -1319,7 +1323,7 @@
    "source": [
     "## Предварителна обработка на данните\n",
     "\n",
-    "Не се интересуваме от разделянето на държавите на по-малки територии, затова първо ще премахнем това разделение и ще обединим информацията за всички територии, за да получим данни за цялата държава. Това може да се направи с помощта на `groupby`:\n"
+    "Не се интересуваме от разбиване на държавите на допълнителни територии, затова първо ще се отървем от това разбиване и ще добавим информация за всички територии заедно, за да получим информация за цялата държава. Това може да се направи с помощта на `groupby`:\n"
    ]
   },
   {
@@ -1574,7 +1578,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Можете да видите, че поради използването на `groupby` всички DataFrame-и сега са индексирани по държава/регион. Следователно можем да достъпим данните за конкретна държава, като използваме `.loc`:\n"
+    "Можете да видите, че поради използването на `groupby` всички DataFrame-ове сега са индексирани по Държава/Регион. По този начин можем да достъпим данните за конкретна страна, като използваме `.loc`:|\n"
    ]
   },
   {
@@ -1583,20 +1587,19 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
-    "infected.loc['US'][2:].plot()\n",
-    "recovered.loc['US'][2:].plot()\n",
+    "infected.loc['US'][3:].plot()\n",
+    "recovered.loc['US'][3:].plot()\n",
     "plt.show()"
    ]
   },
@@ -1604,7 +1607,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Забележка** как използваме `[2:]`, за да премахнем първите два елемента от последователност, които съдържат геолокацията на дадена страна. Можем също така да премахнем тези две колони изцяло:\n"
+    "> **Бележка** как използваме `[3:]`, за да премахнем първите три елемента от последователност, която съдържа метаданни, различни от датата (колоните Провинция/Щат и геолокация). Можем също така просто да изтрием тези три колони изцяло:\n"
    ]
   },
   {
@@ -1613,9 +1616,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected.drop(columns=['Lat','Long'],inplace=True)\n",
-    "recovered.drop(columns=['Lat','Long'],inplace=True)\n",
-    "deaths.drop(columns=['Lat','Long'],inplace=True)"
+    "infected.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "recovered.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "deaths.drop(columns=['Lat','Long','Province/State'],inplace=True)"
    ]
   },
   {
@@ -1624,7 +1627,7 @@
    "source": [
     "## Разследване на данните\n",
     "\n",
-    "Сега нека преминем към разследване на конкретна държава. Нека създадем рамка, която съдържа данните за инфекциите, индексирани по дата:\n"
+    "Нека сега преминем към разследване на конкретна държава. Нека създадем рамка, която съдържа данните за инфекции, индексирани по дата:\n"
    ]
   },
   {
@@ -1754,7 +1757,7 @@
    ],
    "source": [
     "def mkframe(country):\n",
-    "    df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n",
+    "    df = pd.DataFrame({ 'infected' : infected.loc[country],\n",
     "                        'recovered' : recovered.loc[country],\n",
     "                        'deaths' : deaths.loc[country]})\n",
     "    df.index = pd.to_datetime(df.index)\n",
@@ -1790,7 +1793,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Сега нека изчислим броя на новозаразените хора всеки ден. Това ще ни позволи да видим скоростта, с която пандемията напредва. Най-лесният начин да го направим е да използваме `diff`:\n"
+    "Сега нека изчислим броя на новозаразените хора всеки ден. Това ще ни позволи да видим скоростта, с която пандемията прогресира. Най-лесният начин да го направим е да използваме `diff`:\n"
    ]
   },
   {
@@ -1820,7 +1823,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Можем да видим големи колебания в данните. Нека разгледаме по-отблизо един от месеците:\n"
+    "Виждаме големи колебания в данните. Нека разгледаме по-отблизо един от месеците:\n"
    ]
   },
   {
@@ -1849,7 +1852,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Изглежда ясно, че има седмични колебания в данните. Тъй като искаме да можем да видим тенденциите, има смисъл да изгладим кривата, като изчислим подвижна средна стойност (т.е. за всеки ден ще изчислим средната стойност на предходните няколко дни):\n"
+    "Явно има седмични колебания в данните. Тъй като искаме да можем да видим тенденциите, има смисъл да изгладим кривата, като изчислим подвижната средна стойност (т.е. за всеки ден ще изчислим средната стойност на предходните няколко дни):\n"
    ]
   },
   {
@@ -2150,7 +2153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Тъй като този набор от данни съдържа информация както за държави, така и за провинции, за да получим населението на цялата държава, трябва да бъдем малко по-умни:\n"
+    "Тъй като този набор от данни съдържа информация както за страни, така и за провинции, за да получим населението на цялата страна, трябва да бъдем малко по-умни:\n"
    ]
   },
   {
@@ -2260,12 +2263,12 @@
    "source": [
     "## Изчисляване на $R_t$\n",
     "\n",
-    "За да разберем колко заразна е болестта, разглеждаме **основния репродуктивен брой** $R_0$, който показва броя на хората, които един заразен човек би заразил допълнително. Когато $R_0$ е повече от 1, епидемията вероятно ще се разпространи.\n",
+    "За да видим колко е заразно заболяването, разглеждаме **основния репродуктивен брой** $R_0$, който показва броя на хората, които един заразен човек би заразил допълнително. Когато $R_0$ е повече от 1, вероятно е епидемията да се разпространи.\n",
     "\n",
-    "$R_0$ е характеристика на самата болест и не отчита някои защитни мерки, които хората могат да предприемат, за да забавят пандемията. По време на развитието на пандемията можем да оценим репродуктивния брой $R_t$ за всеки даден момент $t$. Доказано е, че този брой може приблизително да се изчисли, като се вземе прозорец от 8 дни и се изчисли $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
-    "където $I_t$ е броят на новозаразените лица за ден $t$.\n",
+    "$R_0$ е свойство на самото заболяване и не взема предвид някои предпазни мерки, които хората могат да предприемат, за да забавят пандемията. По време на развитието на пандемията, можем да оценим репродуктивния брой $R_t$ във всеки даден момент $t$. Показано е, че този брой може приблизително да се изчисли, като се вземе прозорец от 8 дни и се пресметне $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
+    "където $I_t$ е броят на новозаразените в ден $t$.\n",
     "\n",
-    "Нека изчислим $R_t$ за нашите данни за пандемията. За да направим това, ще вземем подвижен прозорец от 8 стойности на `ninfected` и ще приложим функцията за изчисляване на горното съотношение:\n"
+    "Нека изчислим $R_t$ за нашите данни за пандемията. За това ще вземем плъзгащ прозорец от 8 стойности `ninfected` и ще приложим функцията за изчисляване на горното съотношение:\n"
    ]
   },
   {
@@ -2295,9 +2298,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Можете да забележите, че има някои пропуски в графиката. Те могат да бъдат причинени от `NaN` или `inf` стойности, присъстващи в набора от данни. `inf` може да се появи при деление на 0, а `NaN` може да показва липсващи данни или липса на налични данни за изчисляване на резултата (например в самото начало на нашия кадър, където плъзгащият прозорец с ширина 8 все още не е наличен). За да направим графиката по-приятна, трябва да запълним тези стойности, използвайки функциите `replace` и `fillna`.\n",
+    "Можете да видите, че в графиката има някои пропуски. Те могат да бъдат причинени или от `NaN`, или ако в набора от данни присъстват стойности `inf`. `inf` може да бъде причинено от деление на 0, а `NaN` може да показва липсващи данни или липса на данни за изчисляване на резултата (като в самото начало на нашия фрейм, където подвижният прозорец с ширина 8 все още не е наличен). За да направим графиката по-красива, трябва да запълним тези стойности, като използваме функциите `replace` и `fillna`.\n",
     "\n",
-    "Нека разгледаме по-подробно началото на пандемията. Също така ще ограничим стойностите на y-оста, за да показваме само стойности под 6, с цел по-добра видимост, и ще начертаем хоризонтална линия на стойност 1.\n"
+    "Нека разгледаме по-отблизо началото на пандемията. Ще ограничим и стойностите на y-оста, за да показва само стойности под 6, за да се вижда по-добре, и ще начертаем хоризонтална линия на 1.\n"
    ]
   },
   {
@@ -2327,7 +2330,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Друг интересен индикатор на пандемията е **производната**, или **дневната разлика** в новите случаи. Той ни позволява ясно да видим кога пандемията се увеличава или намалява.\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -2355,7 +2360,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Като се има предвид фактът, че има много колебания в данните, причинени от докладването, има смисъл да се изглади кривата чрез прилагане на подвижна средна стойност, за да се получи общата картина. Нека отново се съсредоточим върху първите месеци на пандемията:\n"
+    "Предвид факта, че има много колебания в данните, причинени от отчетността, има смисъл да се изглади кривата чрез изчисляване на подвижна средна, за да се получи цялостната картина. Нека отново се съсредоточим върху първите месеци на пандемията:\n"
    ]
   },
   {
@@ -2387,24 +2392,24 @@
    "source": [
     "## Предизвикателство\n",
     "\n",
-    "Сега е време да се забавлявате повече с кода и данните! Ето няколко предложения, с които можете да експериментирате:\n",
-    "* Вижте разпространението на пандемията в различни държави.\n",
-    "* Начертайте графики на $R_t$ за няколко държави на един график за сравнение или направете няколко графики една до друга.\n",
-    "* Вижте как броят на смъртните случаи и възстановяванията се корелира с броя на заразените случаи.\n",
-    "* Опитайте да разберете колко дълго трае типичното заболяване, като визуално корелирате скоростта на заразяване и скоростта на смъртност и търсите някои аномалии. Може да се наложи да разгледате различни държави, за да откриете това.\n",
-    "* Изчислете смъртността и как тя се променя с времето. Може да искате да вземете предвид продължителността на заболяването в дни, за да изместите една времева серия преди да направите изчисления.\n"
+    "Сега е време да поиграете повече с кода и данните! Ето няколко предложения, с които може да експериментирате:\n",
+    "* Вижте разпространението на пандемията в различни страни.\n",
+    "* Начертайте графики на $R_t$ за няколко страни на една графика за сравнение, или направете няколко графики една до друга.\n",
+    "* Вижте как броят на смъртните случаи и излекуваните се корелира с броя на заразените случаи.\n",
+    "* Опитайте да разберете колко дълго обикновено продължава болестта, като визуално корелирате скоростта на инфекция и скоростта на смъртност и търсите някои аномалии. Може да се наложи да разгледате различни страни, за да разберете това.\n",
+    "* Изчислете смъртността и как тя се променя с времето. Може да искате да вземете предвид продължителността на болестта в дни, за да изместите един времеви ред преди да направите изчисления.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Референции\n",
+    "## References\n",
     "\n",
     "Можете да разгледате допълнителни изследвания за разпространението на епидемията от COVID в следните публикации:\n",
     "* [Модел Sliding SIR за оценка на Rt по време на пандемията от COVID](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/), блог пост от [Дмитрий Сошников](http://soshnikov.com)\n",
-    "* Т.Петрова, Д.Сошников, А.Грунин. [Оценка на времево-зависимия коефициент на възпроизводство за глобалното разпространение на COVID-19](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
-    "* [Код за горепосочената статия в GitHub](https://github.com/shwars/SlidingSIR)\n"
+    "* Т. Петрова, Д. Сошников, А. Грунин. [Оценка на времевозависимия коефициент на възпроизводство за глобалната COVID-19 епидемия](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
+    "* [Код за горното изследване в GitHub](https://github.com/shwars/SlidingSIR)\n"
    ]
   },
   {
@@ -2418,7 +2423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Отказ от отговорност**:\nТози документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматичните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за каквито и да е недоразумения или неправилни тълкувания, възникнали от употребата на този превод.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -2442,13 +2447,7 @@
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
   },
-  "orig_nbformat": 4,
-  "coopTranslator": {
-   "original_hash": "68a0ada1dae45c7d1d3519aa0a6d1a0f",
-   "translation_date": "2025-09-01T20:44:19+00:00",
-   "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
-   "language_code": "bg"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/translations/bg/2-Working-With-Data/README.md b/translations/bg/2-Working-With-Data/README.md
index 19a2ca5f..c29814d0 100644
--- a/translations/bg/2-Working-With-Data/README.md
+++ b/translations/bg/2-Working-With-Data/README.md
@@ -1,6 +1,6 @@
 # Работа с данни
 
-![data love](../../../translated_images/bg/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)
+![data love](../../../translated_images/bg/data-love.a22ef29e6742c852.webp)
 > Снимка от <a href="https://unsplash.com/@swimstaralex?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexander Sinn</a> на <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 В тези уроци ще научите някои от начините, по които данните могат да бъдат управлявани, манипулирани и използвани в приложения. Ще се запознаете с релационни и нерелационни бази данни и как данните могат да се съхраняват в тях. Ще научите основите на работата с Python за управление на данни и ще откриете някои от многото начини, по които можете да използвате Python за управление и анализ на данни.
diff --git a/translations/bg/3-Data-Visualization/12-visualization-relationships/README.md b/translations/bg/3-Data-Visualization/12-visualization-relationships/README.md
index 182202ed..56c90713 100644
--- a/translations/bg/3-Data-Visualization/12-visualization-relationships/README.md
+++ b/translations/bg/3-Data-Visualization/12-visualization-relationships/README.md
@@ -42,7 +42,7 @@ honey.head()
 ```python
 sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);
 ```
-![scatterplot 1](../../../../translated_images/bg/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png)
+![scatterplot 1](../../../../translated_images/bg/scatter1.5e1aa5fd6706c5d1.webp)
 
 Сега покажете същите данни с цветова схема, свързана с меда, за да покажете как цената се променя през годините. Можете да направите това, като добавите параметър 'hue', който показва промяната година след година:
 
@@ -51,7 +51,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);
 ```python
 sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5);
 ```
-![scatterplot 2](../../../../translated_images/bg/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png)
+![scatterplot 2](../../../../translated_images/bg/scatter2.c0041a58621ca702.webp)
 
 С тази промяна в цветовата схема можете да видите, че има очевидна силна прогресия през годините по отношение на цената на килограм мед. Всъщност, ако разгледате примерен набор от данни, за да проверите (например за щата Аризона), можете да видите модел на увеличение на цената година след година, с малки изключения:
 
@@ -80,7 +80,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec
 ```
 Можете да видите как размерът на точките постепенно се увеличава.
 
-![scatterplot 3](../../../../translated_images/bg/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png)
+![scatterplot 3](../../../../translated_images/bg/scatter3.3c160a3d1dcb36b3.webp)
 
 Дали това е просто случай на търсене и предлагане? Поради фактори като климатични промени и колапс на колониите, има ли по-малко наличен мед за покупка година след година, което води до увеличение на цената?
 
@@ -95,7 +95,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey);
 ```
 Отговор: Да, с някои изключения около 2003 година:
 
-![line chart 1](../../../../translated_images/bg/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png)
+![line chart 1](../../../../translated_images/bg/line1.f36eb465229a3b1f.webp)
 
 ✅ Тъй като Seaborn агрегира данните около една линия, той показва „многобройните измервания за всяка стойност на x, като изобразява средната стойност и 95% доверителен интервал около средната стойност“. [Източник](https://seaborn.pydata.org/tutorial/relational.html). Това поведение, което отнема време, може да бъде изключено, като добавите `ci=None`.
 
@@ -105,7 +105,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey);
 sns.relplot(x="year", y="totalprod", kind="line", data=honey);
 ```
 
-![line chart 2](../../../../translated_images/bg/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png)
+![line chart 2](../../../../translated_images/bg/line2.a5b3493dc01058af.webp)
 
 Отговор: Не съвсем. Ако разгледате общото производство, то всъщност изглежда, че се е увеличило през тази конкретна година, въпреки че като цяло количеството произведен мед намалява през тези години.
 
@@ -130,7 +130,7 @@ sns.relplot(
 ```
 В тази визуализация можете да сравните добива на колония и броя на колониите година след година, една до друга, с настройка на wrap на 3 за колоните:
 
-![facet grid](../../../../translated_images/bg/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png)
+![facet grid](../../../../translated_images/bg/facet.6a34851dcd540050.webp)
 
 За този набор от данни нищо особено не изпъква по отношение на броя на колониите и техния добив, година след година и щат след щат. Има ли друг начин да се намери корелация между тези две променливи?
 
@@ -153,7 +153,7 @@ sns.despine(right=False)
 plt.ylabel('colony yield')
 ax.figure.legend();
 ```
-![superimposed plots](../../../../translated_images/bg/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png)
+![superimposed plots](../../../../translated_images/bg/dual-line.a4c28ce659603fab.webp)
 
 Докато нищо не изпъква около 2003 година, това ни позволява да завършим урока с малко по-оптимистична нотка: въпреки че като цяло броят на колониите намалява, броят на колониите се стабилизира, дори ако техният добив на колония намалява.
 
diff --git a/translations/bg/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/bg/3-Data-Visualization/R/09-visualization-quantities/README.md
index c3a29de2..ce074d3c 100644
--- a/translations/bg/3-Data-Visualization/R/09-visualization-quantities/README.md
+++ b/translations/bg/3-Data-Visualization/R/09-visualization-quantities/README.md
@@ -57,7 +57,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 ```  
 Тук инсталирате пакета `ggplot2` и след това го импортирате в работната среда с командата `library("ggplot2")`. За да създадете графика в ggplot, използвате функцията `ggplot()` и задавате набора от данни, x и y променливите като атрибути. В този случай използваме функцията `geom_line()`, тъй като целим да създадем линейна графика.
 
-![MaxWingspan-lineplot](../../../../../translated_images/bg/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png)
+![MaxWingspan-lineplot](../../../../../translated_images/bg/MaxWingspan-lineplot.b12169f99d26fdd2.webp)
 
 Какво забелязвате веднага? Изглежда, че има поне един аутлайър – това е доста голям размах на крилата! Размах от над 2000 сантиметра е повече от 20 метра – дали в Минесота се разхождат птеродактили? Нека разследваме.
 
@@ -75,7 +75,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 ```  
 Задаваме ъгъла в `theme` и задаваме етикетите на x и y осите с `xlab()` и `ylab()` съответно. `ggtitle()` дава име на графиката.
 
-![MaxWingspan-lineplot-improved](../../../../../translated_images/bg/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png)
+![MaxWingspan-lineplot-improved](../../../../../translated_images/bg/MaxWingspan-lineplot-improved.04b73b4d5a59552a.webp)
 
 Дори с ротацията на етикетите, зададена на 45 градуса, те са твърде много, за да се четат. Нека опитаме различна стратегия: да етикетираме само аутлайърите и да поставим етикетите вътре в графиката. Можете да използвате точкова диаграма, за да освободите повече място за етикетиране:
 
@@ -91,7 +91,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 
 Какво откривате?
 
-![MaxWingspan-scatterplot](../../../../../translated_images/bg/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png)
+![MaxWingspan-scatterplot](../../../../../translated_images/bg/MaxWingspan-scatterplot.60dc9e0e19d32700.webp)
 
 ## Филтрирайте данните си
 
@@ -110,7 +110,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) +
 ```  
 Създадохме нов датафрейм `birds_filtered` и след това начертахме точкова диаграма. Чрез филтриране на аутлайърите, данните ви сега са по-цялостни и разбираеми.
 
-![MaxWingspan-scatterplot-improved](../../../../../translated_images/bg/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png)
+![MaxWingspan-scatterplot-improved](../../../../../translated_images/bg/MaxWingspan-scatterplot-improved.7d0af81658c65f3e.webp)
 
 Сега, когато имаме по-чист набор от данни поне по отношение на размаха на крилата, нека открием повече за тези птици.
 
@@ -151,7 +151,7 @@ birds_filtered %>% group_by(Category) %>%
 ```  
 В следващия кодов фрагмент инсталираме пакетите [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) и [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0), за да помогнем при манипулирането и групирането на данни с цел начертаване на стълбовидна диаграма. Първо, групирате данните по `Category` на птиците и след това обобщавате колоните `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. След това начертавате стълбовидната диаграма, използвайки пакета `ggplot2`, и задавате цветовете за различните категории и етикетите.
 
-![Stacked bar chart](../../../../../translated_images/bg/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png)
+![Stacked bar chart](../../../../../translated_images/bg/stacked-bar-chart.0c92264e89da7b39.webp)
 
 Тази стълбовидна диаграма обаче е нечетлива, защото има твърде много негрупирани данни. Трябва да изберете само данните, които искате да визуализирате, така че нека разгледаме дължината на птиците въз основа на тяхната категория.
 
@@ -166,7 +166,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip()
 ```  
 Първо преброявате уникалните стойности в колоната `Category` и след това ги сортирате в нов датафрейм `birds_count`. Тези сортирани данни след това се подреждат на същото ниво, за да бъдат начертани в сортиран вид. Използвайки `ggplot2`, след това начертавате данните в стълбовидна диаграма. `coord_flip()` начертава хоризонтални стълбове.
 
-![category-length](../../../../../translated_images/bg/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png)
+![category-length](../../../../../translated_images/bg/category-length.7e34c296690e85d6.webp)
 
 Тази стълбовидна диаграма показва добър изглед на броя на птиците във всяка категория. С един поглед виждате, че най-големият брой птици в този регион са в категорията Патици/Гъски/Водоплаващи. Минесота е "земята на 10 000 езера", така че това не е изненадващо!
 
@@ -189,7 +189,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl
 ```  
 Групираме данните `birds_filtered` по `Category` и след това начертаваме стълбовидна диаграма.
 
-![comparing data](../../../../../translated_images/bg/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png)
+![comparing data](../../../../../translated_images/bg/comparingdata.f486a450d61c7ca5.webp)
 
 Тук няма нищо изненадващо: колибритата имат най-малка максимална дължина в сравнение с пеликаните или гъските. Добре е, когато данните имат логически смисъл!
 
@@ -201,7 +201,7 @@ ggplot(data=birds_grouped, aes(x=Category)) +
   geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+
   coord_flip()
 ```  
-![super-imposed values](../../../../../translated_images/bg/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png)
+![super-imposed values](../../../../../translated_images/bg/superimposed-values.5363f0705a1da416.webp)
 
 ## 🚀 Предизвикателство
 
diff --git a/translations/bg/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/bg/3-Data-Visualization/R/10-visualization-distributions/README.md
index 1230eaca..bcd84f78 100644
--- a/translations/bg/3-Data-Visualization/R/10-visualization-distributions/README.md
+++ b/translations/bg/3-Data-Visualization/R/10-visualization-distributions/README.md
@@ -36,7 +36,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) +
   geom_point() +
   ggtitle("Max Length per order") + coord_flip()
 ```
-![макс дължина по разред](../../../../../translated_images/bg/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png)
+![макс дължина по разред](../../../../../translated_images/bg/max-length-per-order.e5b283d952c78c12.webp)
 
 Това дава общ преглед на разпределението на дължината на тялото по разред на птиците, но не е най-добрият начин за показване на истински разпределения. Тази задача обикновено се изпълнява чрез създаване на хистограма.
 ## Работа с хистограми
@@ -47,7 +47,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) +
 ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + 
   geom_histogram(bins=10)+ylab('Frequency')
 ```
-![разпределение за целия набор от данни](../../../../../translated_images/bg/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png)
+![разпределение за целия набор от данни](../../../../../translated_images/bg/distribution-over-the-entire-dataset.d22afd3fa96be854.webp)
 
 Както виждате, повечето от 400+ птици в този набор от данни попадат в диапазона под 2000 за тяхната максимална телесна маса. Получете повече информация за данните, като промените параметъра `bins` на по-голямо число, например 30:
 
@@ -55,7 +55,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) +
 ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency')
 ```
 
-![разпределение-30групи](../../../../../translated_images/bg/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png)
+![разпределение-30групи](../../../../../translated_images/bg/distribution-30bins.6a3921ea7a421bf7.webp)
 
 Тази диаграма показва разпределението по малко по-гранулиран начин. Диаграма, която е по-малко изкривена наляво, може да бъде създадена, като се уверите, че избирате само данни в даден диапазон:
 
@@ -67,7 +67,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) +
   geom_histogram(bins=30)+ylab('Frequency')
 ```
 
-![филтрирана хистограма](../../../../../translated_images/bg/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png)
+![филтрирана хистограма](../../../../../translated_images/bg/filtered-histogram.6bf5d2bfd8253322.webp)
 
 ✅ Опитайте някои други филтри и точки от данни. За да видите пълното разпределение на данните, премахнете филтъра `['MaxBodyMass']`, за да покажете етикетирани разпределения.
 
@@ -81,7 +81,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) +
 ```
 Изглежда, че има очаквана корелация между тези два елемента по очаквана ос, с една особено силна точка на сближаване:
 
-![2d диаграма](../../../../../translated_images/bg/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png)
+![2d диаграма](../../../../../translated_images/bg/2d-plot.c504786f439bd7eb.webp)
 
 Хистограмите работят добре по подразбиране за числови данни. Ами ако трябва да видите разпределения според текстови данни? 
 ## Изследване на набора от данни за разпределения с текстови данни 
@@ -112,7 +112,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) +
   scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern"))
 ```
 
-![размах на крилата и статус на опазване](../../../../../translated_images/bg/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png)
+![размах на крилата и статус на опазване](../../../../../translated_images/bg/wingspan-conservation-collation.4024e9aa6910866a.webp)
 
 Не изглежда да има добра корелация между минималния размах на крилата и статуса на опазване. Тествайте други елементи от набора от данни, използвайки този метод. Можете да опитате различни филтри. Откривате ли някаква корелация?
 
@@ -126,7 +126,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) +
 ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + 
   geom_density()
 ```
-![плот на плътност](../../../../../translated_images/bg/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png)
+![плот на плътност](../../../../../translated_images/bg/density-plot.675ccf865b76c690.webp)
 
 Можете да видите как плотът отразява предишния за данните за минималния размах на крилата; той е просто малко по-гладък. Ако искате да преразгледате тази назъбена линия на MaxBodyMass във втората диаграма, която създадохте, можете да я изгладите много добре, като я пресъздадете, използвайки този метод:
 
@@ -134,7 +134,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) +
 ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
   geom_density()
 ```
-![плътност на телесната маса](../../../../../translated_images/bg/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png)
+![плътност на телесната маса](../../../../../translated_images/bg/bodymass-smooth.d31ce526d82b0a1f.webp)
 
 Ако искате гладка, но не прекалено гладка линия, редактирайте параметъра `adjust`: 
 
@@ -142,7 +142,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) +
 ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
   geom_density(adjust = 1/5)
 ```
-![по-малко гладка телесна маса](../../../../../translated_images/bg/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png)
+![по-малко гладка телесна маса](../../../../../translated_images/bg/less-smooth-bodymass.10f4db8b683cc17d.webp)
 
 ✅ Прочетете за наличните параметри за този тип плот и експериментирайте!
 
@@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) +
 ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) +
   geom_density(alpha=0.5)
 ```
-![телесна маса по разред](../../../../../translated_images/bg/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png)
+![телесна маса по разред](../../../../../translated_images/bg/bodymass-per-order.9d2b065dd931b928.webp)
 
 ## 🚀 Предизвикателство
 
diff --git a/translations/bg/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/bg/3-Data-Visualization/R/11-visualization-proportions/README.md
index c097d5bd..db3aa0f7 100644
--- a/translations/bg/3-Data-Visualization/R/11-visualization-proportions/README.md
+++ b/translations/bg/3-Data-Visualization/R/11-visualization-proportions/README.md
@@ -88,7 +88,7 @@ pie(grouped$count,grouped$class, main="Edible?")
 ```
 Voilà, кръгова диаграма, показваща пропорциите на тези данни според двата класа гъби. Много е важно да се уверите, че редът на етикетите е правилен, особено тук, така че проверете реда, с който е изградена масивът от етикети!
 
-![кръгова диаграма](../../../../../translated_images/bg/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png)
+![кръгова диаграма](../../../../../translated_images/bg/pie1-wb.685df063673751f4.webp)
 
 ## Пръстени!
 
@@ -123,7 +123,7 @@ library(webr)
 PieDonut(habitat, aes(habitat, count=count))
 ```
 
-![пръстеновидна диаграма](../../../../../translated_images/bg/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png)
+![пръстеновидна диаграма](../../../../../translated_images/bg/donut-wb.34e6fb275da9d834.webp)
 
 Този код използва две библиотеки - ggplot2 и webr. Използвайки функцията PieDonut от библиотеката webr, можем лесно да създадем пръстеновидна диаграма!
 
@@ -160,7 +160,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual
 
 С помощта на вафлена диаграма можете ясно да видите пропорциите на цветовете на шапките в този набор от данни за гъби. Интересно е, че има много гъби със зелени шапки!
 
-![вафлена диаграма](../../../../../translated_images/bg/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png)
+![вафлена диаграма](../../../../../translated_images/bg/waffle.aaa75c5337735a6e.webp)
 
 В този урок научихте три начина за визуализиране на пропорции. Първо, трябва да групирате данните си в категории и след това да решите кой е най-добрият начин за показване на данните - кръгова, пръстеновидна или вафлена диаграма. Всички са вкусни и предоставят на потребителя моментална снимка на набора от данни.
 
diff --git a/translations/bg/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/bg/3-Data-Visualization/R/12-visualization-relationships/README.md
index b14b874d..89464306 100644
--- a/translations/bg/3-Data-Visualization/R/12-visualization-relationships/README.md
+++ b/translations/bg/3-Data-Visualization/R/12-visualization-relationships/README.md
@@ -42,7 +42,7 @@ library(ggplot2)
 ggplot(honey, aes(x = priceperlb, y = state)) +
   geom_point(colour = "blue")
 ```
-![scatterplot 1](../../../../../translated_images/bg/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png)
+![scatterplot 1](../../../../../translated_images/bg/scatter1.86b8900674d88b26.webp)
 
 Сега покажете същите данни с цветова схема, вдъхновена от мед, за да покажете как цената се развива през годините. Можете да направите това, като добавите параметър 'scale_color_gradientn', който показва промяната година след година:
 
@@ -52,7 +52,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) +
 ggplot(honey, aes(x = priceperlb, y = state, color=year)) +
   geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7))
 ```
-![scatterplot 2](../../../../../translated_images/bg/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png)
+![scatterplot 2](../../../../../translated_images/bg/scatter2.4d1cbc693bad20e2.webp)
 
 С тази промяна в цветовата схема можете да видите, че има очевидна прогресия през годините по отношение на цената на килограм мед. Всъщност, ако разгледате примерен набор от данни, за да проверите (например щат Аризона), можете да видите модел на увеличение на цената година след година, с малко изключения:
 
@@ -83,7 +83,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) +
 ```
 Можете да видите как размерът на точките постепенно се увеличава.
 
-![scatterplot 3](../../../../../translated_images/bg/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png)
+![scatterplot 3](../../../../../translated_images/bg/scatter3.722d21e6f20b3ea2.webp)
 
 Дали това е просто случай на търсене и предлагане? Поради фактори като климатични промени и колапс на колониите, има ли по-малко мед за покупка година след година, и затова цената се увеличава?
 
@@ -98,7 +98,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab
 ```
 Отговор: Да, с някои изключения около 2003 година:
 
-![line chart 1](../../../../../translated_images/bg/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png)
+![line chart 1](../../../../../translated_images/bg/line1.299b576fbb2a59e6.webp)
 
 Въпрос: Е, през 2003 година можем ли да видим и скок в предлагането на мед? Какво ще стане, ако разгледате общото производство година след година?
 
@@ -106,7 +106,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab
 qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod")
 ```
 
-![line chart 2](../../../../../translated_images/bg/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png)
+![line chart 2](../../../../../translated_images/bg/line2.3b18fcda7176ceba.webp)
 
 Отговор: Не съвсем. Ако разгледате общото производство, всъщност изглежда, че то се е увеличило през тази конкретна година, въпреки че като цяло количеството произведен мед намалява през тези години.
 
@@ -126,7 +126,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) +
 ```
 В тази визуализация можете да сравните добива на колония и броя на колониите година след година, една до друга, с настройка на wrap на 3 за колоните:
 
-![facet grid](../../../../../translated_images/bg/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png)
+![facet grid](../../../../../translated_images/bg/facet.491ad90d61c2a7cc.webp)
 
 За този набор от данни нищо особено не изпъква по отношение на броя на колониите и техния добив, година след година и щат след щат. Има ли различен начин за откриване на корелация между тези две променливи?
 
@@ -143,7 +143,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3,
 axis(side = 4, at = pretty(range(y2)))      
 mtext("colony yield", side = 4, line = 3)   
 ```
-![superimposed plots](../../../../../translated_images/bg/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png)
+![superimposed plots](../../../../../translated_images/bg/dual-line.fc4665f360a54018.webp)
 
 Докато нищо не изпъква около 2003 година, това ни позволява да завършим урока с малко по-щастлива нотка: въпреки че броят на колониите като цяло намалява, той се стабилизира, дори ако техният добив на колония намалява.
 
diff --git a/translations/bg/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/bg/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
index 96807b74..6a09862f 100644
--- a/translations/bg/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
+++ b/translations/bg/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
@@ -38,25 +38,25 @@
 
 Дори ако специалистът по данни внимателно избере правилната диаграма за правилните данни, има много начини, по които данните могат да бъдат представени, за да докажат дадена теза, често за сметка на самите данни. Има много примери за подвеждащи диаграми и инфографики!
 
-[![Как диаграмите лъжат от Алберто Кайро](../../../../../translated_images/bg/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Как диаграмите лъжат")
+[![Как диаграмите лъжат от Алберто Кайро](../../../../../translated_images/bg/tornado.2880ffc7f135f82b.webp)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Как диаграмите лъжат")
 
 > 🎥 Кликнете върху изображението по-горе за конференция относно подвеждащи диаграми
 
 Тази диаграма обръща оста X, за да покаже обратното на истината, базирано на дата:
 
-![лоша диаграма 1](../../../../../translated_images/bg/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png)
+![лоша диаграма 1](../../../../../translated_images/bg/bad-chart-1.596bc93425a8ac30.webp)
 
 [Тази диаграма](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) е още по-подвеждаща, тъй като окото се насочва към заключението, че с времето случаите на COVID са намалели в различните окръзи. Всъщност, ако погледнете внимателно датите, ще откриете, че те са пренаредени, за да създадат този подвеждащ низходящ тренд.
 
-![лоша диаграма 2](../../../../../translated_images/bg/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg)
+![лоша диаграма 2](../../../../../translated_images/bg/bad-chart-2.62edf4d2f30f4e51.webp)
 
 Този известен пример използва цветове И обърната ос Y, за да заблуди: вместо да заключите, че смъртните случаи от огнестрелни оръжия са се увеличили след приемането на законодателство, благоприятстващо оръжията, окото е заблудено да мисли, че обратното е вярно:
 
-![лоша диаграма 3](../../../../../translated_images/bg/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg)
+![лоша диаграма 3](../../../../../translated_images/bg/bad-chart-3.e201e2e915a230bc.webp)
 
 Тази странна диаграма показва как пропорциите могат да бъдат манипулирани, с комичен ефект:
 
-![лоша диаграма 4](../../../../../translated_images/bg/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg)
+![лоша диаграма 4](../../../../../translated_images/bg/bad-chart-4.8872b2b881ffa96c.webp)
 
 Сравняването на несравними неща е още един съмнителен трик. Има [чудесен уебсайт](https://tylervigen.com/spurious-correlations), посветен на 'фалшиви корелации', показващ 'факти', които корелират неща като процента на разводите в Мейн и консумацията на маргарин. Група в Reddit също събира [грозни примери](https://www.reddit.com/r/dataisugly/top/?t=all) за използване на данни.
 
@@ -91,13 +91,13 @@
 
 Ако вашите данни са текстови и подробни на оста X, можете да наклоните текста за по-добра четливост. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) предлага 3D графики, ако вашите данни го поддържат. Сложни визуализации на данни могат да бъдат създадени с него.
 
-![3D диаграми](../../../../../translated_images/bg/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png)
+![3D диаграми](../../../../../translated_images/bg/3d.db1734c151eee87d.webp)
 
 ## Анимация и 3D визуализация
 
 Някои от най-добрите визуализации на данни днес са анимирани. Шърли Ву има невероятни примери, направени с D3, като '[цветя от филми](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', където всяко цвете е визуализация на филм. Друг пример за Guardian е 'bussed out', интерактивно преживяване, комбиниращо визуализации с Greensock и D3 плюс формат на статия с разказ, за да покаже как Ню Йорк се справя с проблема с бездомните, като ги изпраща извън града.
 
-![busing](../../../../../translated_images/bg/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png)
+![busing](../../../../../translated_images/bg/busing.8157cf1bc89a3f65.webp)
 
 > "Bussed Out: Как Америка премества своите бездомни" от [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Визуализации от Надие Бремер и Шърли Ву
 
@@ -107,7 +107,7 @@
 
 Ще завършите уеб приложение, което ще показва анимиран изглед на тази социална мрежа. То използва библиотека, създадена за [визуализация на мрежа](https://github.com/emiliorizzo/vue-d3-network) с Vue.js и D3. Когато приложението работи, можете да местите възлите на екрана, за да пренареждате данните.
 
-![liaisons](../../../../../translated_images/bg/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png)
+![liaisons](../../../../../translated_images/bg/liaisons.90ce7360bcf84765.webp)
 
 ## Проект: Създаване на диаграма за показване на мрежа с D3.js
 
diff --git a/translations/bg/3-Data-Visualization/README.md b/translations/bg/3-Data-Visualization/README.md
index ac3a2810..4158f620 100644
--- a/translations/bg/3-Data-Visualization/README.md
+++ b/translations/bg/3-Data-Visualization/README.md
@@ -1,6 +1,6 @@
 # Визуализации
 
-![пчела върху цвете лавандула](../../../translated_images/bg/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)
+![пчела върху цвете лавандула](../../../translated_images/bg/bee.0aa1d91132b12e3a.webp)
 > Снимка от <a href="https://unsplash.com/@jenna2980?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Jenna Lee</a> на <a href="https://unsplash.com/s/photos/bees-in-a-meadow?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 Визуализирането на данни е една от най-важните задачи на специалиста по данни. Една картина казва повече от хиляда думи, а визуализацията може да ви помогне да идентифицирате всякакви интересни аспекти на вашите данни, като пикове, отклонения, групирания, тенденции и други, които могат да ви помогнат да разберете историята, която вашите данни се опитват да разкажат.
diff --git a/translations/bg/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/bg/4-Data-Science-Lifecycle/14-Introduction/README.md
index 7afbc73f..9a3d5ec7 100644
--- a/translations/bg/4-Data-Science-Lifecycle/14-Introduction/README.md
+++ b/translations/bg/4-Data-Science-Lifecycle/14-Introduction/README.md
@@ -16,7 +16,7 @@
 
 Тази лекция се фокусира върху 3 части от жизнения цикъл: събиране, обработка и поддръжка.
 
-![Диаграма на жизнения цикъл на науката за данни](../../../../translated_images/bg/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg)  
+![Диаграма на жизнения цикъл на науката за данни](../../../../translated_images/bg/data-science-lifecycle.a1e362637503c4fb.webp)  
 > Снимка от [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/)
 
 ## Събиране
@@ -92,7 +92,7 @@
 
 |Процес за екипна наука за данни (TDSP)|Стандарт за процеса на извличане на данни в различни индустрии (CRISP-DM)|
 |--|--|
-|![Жизнен цикъл на екипната наука за данни](../../../../translated_images/bg/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Изображение на алианса за процеса на извличане на данни](../../../../translated_images/bg/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) |
+|![Жизнен цикъл на екипната наука за данни](../../../../translated_images/bg/tdsp-lifecycle2.e19029d598e2e73d.webp) | ![Изображение на алианса за процеса на извличане на данни](../../../../translated_images/bg/CRISP-DM.8bad2b4c66e62aa7.webp) |
 | Изображение от [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Изображение от [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) |
 
 ## [Тест след лекцията](https://ff-quizzes.netlify.app/en/ds/quiz/27)
diff --git a/translations/bg/4-Data-Science-Lifecycle/README.md b/translations/bg/4-Data-Science-Lifecycle/README.md
index 0531a0a7..9268c7ac 100644
--- a/translations/bg/4-Data-Science-Lifecycle/README.md
+++ b/translations/bg/4-Data-Science-Lifecycle/README.md
@@ -1,6 +1,6 @@
 # Жизненият цикъл на науката за данни
 
-![communication](../../../translated_images/bg/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)
+![communication](../../../translated_images/bg/communication.06d8e2a88d30d168.webp)
 > Снимка от <a href="https://unsplash.com/@headwayio?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Headway</a> на <a href="https://unsplash.com/s/photos/communication?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 В тези уроци ще разгледате някои аспекти на жизнения цикъл на науката за данни, включително анализ и комуникация, свързани с данните.
diff --git a/translations/bg/5-Data-Science-In-Cloud/README.md b/translations/bg/5-Data-Science-In-Cloud/README.md
index 05846c77..1820f415 100644
--- a/translations/bg/5-Data-Science-In-Cloud/README.md
+++ b/translations/bg/5-Data-Science-In-Cloud/README.md
@@ -1,12 +1,12 @@
 # Наука за данни в облака
 
-![cloud-picture](../../../translated_images/bg/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)
+![cloud-picture](../../../translated_images/bg/cloud-picture.f5526de3c6c6387b.webp)
 
 > Снимка от [Jelleke Vanooteghem](https://unsplash.com/@ilumire) от [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)
 
 Когато става въпрос за работа с големи данни в науката за данни, облакът може да бъде истинска промяна. В следващите три урока ще разгледаме какво представлява облакът и защо може да бъде много полезен. Ще изследваме набор от данни за сърдечна недостатъчност и ще изградим модел, който да помогне за оценка на вероятността някой да има сърдечна недостатъчност. Ще използваме силата на облака, за да обучим, внедрим и използваме модел по два различни начина. Единият начин е чрез потребителския интерфейс в стил "Low code/No code", а другият - чрез Azure Machine Learning Software Developer Kit (Azure ML SDK).
 
-![project-schema](../../../translated_images/bg/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png)
+![project-schema](../../../translated_images/bg/project-schema.420e56d495624541.webp)
 
 ### Теми
 
diff --git a/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
index 45d2148d..a5446bc6 100644
--- a/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
+++ b/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
@@ -32,7 +32,7 @@
 
  * [Наука за данни в здравеопазването](https://data-flair.training/blogs/data-science-in-healthcare/) - подчертава приложения като медицинско изображение (напр. MRI, рентген, CT-сканиране), геномика (секвениране на ДНК), разработка на лекарства (оценка на риска, прогноза за успех), предсказателен анализ (грижа за пациенти и логистика на доставки), проследяване и предотвратяване на заболявания и др.
 
-![Приложения на науката за данни в реалния свят](../../../../translated_images/bg/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Кредит за изображението: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/)
+![Приложения на науката за данни в реалния свят](../../../../translated_images/bg/data-science-applications.4e5019cd8790ebac.webp) Кредит за изображението: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/)
 
 Фигурата показва други области и примери за прилагане на техники за наука за данни. Искате ли да разгледате други приложения? Вижте секцията [Преглед и самостоятелно обучение](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) по-долу.
 
diff --git a/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
index 18dfe81d..6ce3a9a6 100644
--- a/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
+++ b/translations/bg/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
@@ -13,7 +13,7 @@
  2. Разгледайте [Каталога](https://planetarycomputer.microsoft.com/catalog) на наборите от данни - научете за целта на всеки.
  3. Използвайте Explorer - изберете набор от данни, който ви интересува, изберете подходящо запитване и опция за визуализация.
 
-![The Planetary Computer Explorer](../../../../translated_images/bg/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png)
+![The Planetary Computer Explorer](../../../../translated_images/bg/planetary-computer-explorer.c1e95a9b053167d6.webp)
 
 `Вашата задача:`
 Сега изучете визуализацията, която се генерира в браузъра, и отговорете на следните въпроси:
diff --git a/translations/bg/README.md b/translations/bg/README.md
index 71e32000..beca0f44 100644
--- a/translations/bg/README.md
+++ b/translations/bg/README.md
@@ -1,4 +1,4 @@
-# Наука за данни за начинаещи - Учебна програма
+# Data Science за начинаещи - Учебна програма
 
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
 
@@ -17,177 +17,187 @@
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
-Azure Cloud Advocates в Microsoft с удоволствие предлагат 10-седмична, 20-урочна учебна програма, изцяло посветена на науката за данни. Всеки урок включва изпити преди и след урока, писмени инструкции за изпълнение на урока, решение и задача. Нашата проектно-ориентирана педагогика ви позволява да учите, докато изграждате, което е доказан начин новите умения да "закрепят".
+Адвокатите на Azure Cloud в Microsoft с удоволствие предлагат 10-седмична учебна програма с 20 урока, изцяло посветена на Data Science. Всеки урок включва тест преди и след урока, писмени инструкции за изпълнение на урока, решение и задача. Нашата педагогика, базирана на проекти, ви позволява да учите, докато строите, което е доказан начин новите умения да "залепнат".
 
 **Сърдечни благодарности на нашите автори:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer).
 
-**🙏 Специални благодарности 🙏 на нашите автори, прегледачи и сътрудници по съдържанието от [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** особено на Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
+**🙏 Специални благодарности 🙏 на нашите [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) автори, рецензенти и съдържателни сътрудници,** в частност Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
 [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
 
 |![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/bg/00-Title.8af36cd35da1ac55.webp)|
 |:---:|
-| Наука за данни за начинаещи - _Sketchnote от [@nitya](https://twitter.com/nitya)_ |
+| Data Science за начинаещи - _Sketchnote от [@nitya](https://twitter.com/nitya)_ |
 
-### 🌐 Многоезична поддръжка
+### 🌐 Поддръжка на множество езици
 
-#### Поддържа се чрез GitHub Action (автоматизирано и винаги актуално)
+#### Поддържа се чрез GitHub Action (Автоматизирано и винаги актуално)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
 [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](./README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
 
-> **Предпочитате да клонирате локално?**
-
-> Този репозиторий включва над 50 езикови превода, което значително увеличава размера за сваляне. За да клонирате без преводи, използвайте sparse checkout:
+> **Предпочитате локално клониране?**
+>
+> Това хранилище включва над 50 езикови превода, което значително увеличава размера за изтегляне. За да клонирате без преводи, използвайте sparse checkout:
+>
+> **Bash / macOS / Linux:**
 > ```bash
 > git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
 > cd Data-Science-For-Beginners
 > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
 > ```
-> Това ви дава всичко необходимо, за да завършите курса с много по-бързо сваляне.
+>
+> **CMD (Windows):**
+> ```cmd
+> git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
+> cd Data-Science-For-Beginners
+> git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"
+> ```
+>
+> Това ви дава всичко необходимо за завършване на курса с много по-бързо изтегляне.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-**Ако желаете да има поддръжка на допълнителни езици за превод, те са изброени [тук](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
+**Ако искате допълнителни преводи, поддържаните езици са изброени [тук](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
 
 #### Присъединете се към нашата общност 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Имаме текуща Discord серия за учене с AI, научете повече и се присъединете към нас в [Learn with AI Series](https://aka.ms/learnwithai/discord) от 18 до 30 септември 2025 г. Ще получите съвети и трикове за използване на GitHub Copilot за науката за данни.
+Имаме текуща Discord серия „учене с AI“, научете повече и се присъединете към нас на [Learn with AI Series](https://aka.ms/learnwithai/discord) от 18 до 30 септември, 2025 г. Ще получите съвети и трикове за използване на GitHub Copilot за Data Science.
 
 ![Learn with AI series](../../translated_images/bg/1.2b28cdc6205e26fe.webp)
 
-# Студент ли сте?
+# Вие студент ли сте?
 
 Започнете с следните ресурси:
 
-- [Страница на Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) На тази страница ще намерите ресурси за начинаещи, студентски пакети и дори начини за получаване на безплатен сертификат. Това е страница, която искате да маркирате и да проверявате от време на време, тъй като съдържанието се обновява поне веднъж месечно.
-- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Присъединете се към глобална общност от студентски посланици, това може да е вашият път към Microsoft.
+- [Student Hub страница](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Тук ще намерите ресурси за начинаещи, студентски пакети и дори начини да получите безплатен сертификатен ваучер. Това е страница, която искате да запазите и да проверявате от време на време, тъй като съдържанието се обновява поне веднъж месечно.
+- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Присъединете се към глобална общност от студенти посланици, това може да е вашият път към Microsoft.
 
-# Запознаване с курса
+# Започване
 
 ## 📚 Документация
 
-- **[Ръководство за инсталация](INSTALLATION.md)** - Стъпка по стъпка инструкции за начинаещи
-- **[Ръководство за употреба](USAGE.md)** - Примери и често срещани работни потоци
+- **[Ръководство за инсталиране](INSTALLATION.md)** - Стъпка по стъпка инструкции за настройка за начинаещи
+- **[Ръководство за използване](USAGE.md)** - Примери и често използвани работни потоци
 - **[Отстраняване на проблеми](TROUBLESHOOTING.md)** - Решения на често срещани проблеми
-- **[Ръководство за принос](CONTRIBUTING.md)** - Как да се включите в този проект
-- **[За учители](for-teachers.md)** - Насоки за преподаване и ресурси за класната стая
+- **[Ръководство за принос](CONTRIBUTING.md)** - Как да допринесете за този проект
+- **[За учители](for-teachers.md)** - Препоръки за преподаване и материали за класната стая
 
 ## 👨‍🎓 За студенти
-> **Напълно начинаещи**: Нови сте в науката за данни? Започнете с нашите [примери за начинаещи](examples/README.md)! Тези прости, добре коментирани примери ще ви помогнат да разберете основите преди да се потопите в цялата учебна програма.
-> **[Студенти](https://aka.ms/student-page)**: за да използвате тази учебна програма самостоятелно, форкнете целия репозиторий и изпълнете задачите самостоятелно, започвайки с предварителен тест преди лекцията. След това прочетете лекцията и завършете останалите активности. Опитайте да създадете проектите, като разбирате уроците, а не копирате кода на решенията; все пак този код е наличен в папките /solutions във всеки проектно-ориентиран урок. Друга идея е да сформирате учебна група с приятели и да преминете през съдържанието заедно. За по-нататъшно обучение препоръчваме [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
+> **Пълни начинаещи**: Нови ли сте в data science? Започнете с нашите [примери за начинаещи](examples/README.md)! Тези прости, добре коментирани примери ще ви помогнат да разберете основите преди да се потопите в цялата учебна програма.
+> **[Студенти](https://aka.ms/student-page)**: за да използвате тази учебна програма самостоятелно, направете форк на цялото хранилище и направете упражненията сами, започвайки с тест преди лекцията. После прочетете лекцията и завършете останалите дейности. Опитайте се да създавате проектите, като разбирате уроците вместо да копирате кода за решения; въпреки това този код е наличен в папките /solutions във всеки урок, ориентиран към проект. Друга идея е да създадете учебна група с приятели и да преминете съдържанието заедно. За по-нататъшно учене препоръчваме [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
 
 **Бърз старт:**
 1. Прегледайте [Ръководството за инсталация](INSTALLATION.md), за да настроите средата си
-2. Разгледайте [Ръководството за употреба](USAGE.md), за да научите как да работите с учебната програма
-3. Започнете с Урок 1 и следвайте поредно
+2. Прегледайте [Ръководството за използване](USAGE.md), за да научите как да работите с учебната програма
+3. Започнете с урок 1 и продължете последователно
 4. Присъединете се към нашата [Discord общност](https://aka.ms/ds4beginners/discord) за подкрепа
 
 ## 👩‍🏫 За учители
+> **Учители**: ние сме [включили някои предложения](for-teachers.md) за това как да използвате тази учебна програма. Ще се радваме на вашата обратна връзка [в нашия дискусионен форум](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
 
-> **Учители**: включили сме [някои предложения](for-teachers.md) как да използвате тази учебна програма. Ще се радваме да чуем мнението ви [в нашия дискусионен форум](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
 ## Запознайте се с екипа
 
 [![Промо видео](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Промо видео")
 
 **Gif от** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
 
-> 🎥 Кликнете върху изображението по-горе за видео за проекта и хората, които го създадоха!
+> 🎥 Кликнете на изображението по-горе за видео за проекта и хората, които са го създали!
 
 ## Педагогика
 
-Избрахме две педагогически основи при изграждането на тази учебна програма: да бъде базирана на проекти и да включва чести викторини. Към края на тази серия, студентите ще са научили основните принципи на науката за данни, включително етични концепции, подготовка на данни, различни начини за работа с данни, визуализация на данни, анализ на данни, реални случаи на използване на науката за данни и още.
+Избрахме два педагогически принципа при създаването на тази учебна програма: да бъде базирана на проект и да включва често повтарящи се тестове. В края на този курс студентите ще са научили основните принципи на науката за данните, включително етични концепции, подготовка на данни, различни начини за работа с данни, визуализация на данни, анализ на данни, реални случаи на използване на науката за данните и още.
 
-Освен това ниско-рисковата викторина преди час задава намерението на студента към изучаване на темата, докато втора викторина след часа осигурява по-нататъшно задържане на знанията. Тази учебна програма е проектирана да бъде гъвкава и забавна и може да се изучава цялостно или частично. Проектите започват малки и стават все по-сложни към края на 10-седмичния цикъл.
+Освен това, тест с нисък залог преди урок задава намерението на студента да изучава дадена тема, докато втори тест след урок осигурява по-нататъшна запаметяемост. Тази учебна програма е проектирана да бъде гъвкава и забавна и може да се премине изцяло или частично. Проектите започват малки и стават все по-сложни до края на 10-седмичния цикъл.
 
-> Вижте нашите [Правила за поведение](CODE_OF_CONDUCT.md), [Указания за принос](CONTRIBUTING.md), [Превод](TRANSLATIONS.md). Очакваме вашата конструктивна обратна връзка!
+> Намерете нашите [Правила за поведение](CODE_OF_CONDUCT.md), [Принос](CONTRIBUTING.md), [Превод](TRANSLATIONS.md) указания. Очакваме вашата конструктивна обратна връзка!
 
-## Всяко занятие включва:
+## Всеки урок включва:
 
-- По желание скичноут
-- По желание допълнително видео
-- Предварителна викторина за затопляне преди урока
+- Допълнителна скица (по желание)
+- Допълнително видео (по желание)
+- Предварителен тест за загряване преди урока
 - Писмен урок
-- За уроци на базата на проекти — стъпка по стъпка насоки за изграждане на проекта
+- За уроци базирани на проекти – стъпка по стъпка ръководства за изграждане на проекта
 - Проверки на знанията
 - Предизвикателство
 - Допълнително четиво
 - Задача
-- [Викторина след урока](https://ff-quizzes.netlify.app/en/)
+- [Тест след урока](https://ff-quizzes.netlify.app/en/)
 
-> **Забележка относно викторините**: Всички викторини се намират в папката Quiz-App, общо 40 викторини с по три въпроса всяка. Те са свързани от уроците, но приложението за викторини може да се изпълнява локално или да се разполага в Azure; следвайте инструкциите в папката `quiz-app`. Постепенно се локализират.
+> **Забележка относно тестовете**: Всички тестове се съдържат в папката Quiz-App, общо 40 теста с по три въпроса всеки. Те са свързани от уроците, но приложението за тестове може да се изпълнява локално или да се деплойва в Azure; следвайте инструкциите в папката `quiz-app`. Те постепенно се локализират.
 
-## 🎓 Примери, подходящи за начинаещи
+## 🎓 Примери за начинаещи
 
-**Новак в науката за данни?** Създадохме специална [директория с примери](examples/README.md) с прост, добре коментиран код, за да ви помогнем да започнете:
+**Нов в науката за данните?** Създадохме специална [директория с примери](examples/README.md) с прост и добре коментиран код, който да ви помогне да започнете:
 
-- 🌟 **Hello World** - Вашата първа програма за наука за данни
-- 📂 **Зареждане на данни** - Научете се как да четете и разглеждате набори от данни
+- 🌟 **Hello World** - Вашата първа програма по наука за данните
+- 📂 **Зареждане на данни** - Научете как да четете и изследвате набори от данни
 - 📊 **Прост анализ** - Изчисляване на статистики и намиране на модели
-- 📈 **Базова визуализация** - Създаване на диаграми и графики
-- 🔬 **Реален проект** - Пълен работен процес от начало до край
+- 📈 **Основна визуализация** - Създаване на диаграми и графики
+- 🔬 **Реален проект** - Цялостен работен процес от начало до край
 
-Всеки пример включва подробни коментари, които обясняват всяка стъпка, което го прави перфектен за абсолютни начинаещи!
+Всеки пример включва подробни коментари, обясняващи всяка стъпка, което го прави перфектен за абсолютни начинаещи!
 
 👉 **[Започнете с примерите](examples/README.md)** 👈
 
 ## Уроци
 
 
-|![ Скичноут от @sketchthedocs https://sketchthedocs.dev](../../translated_images/bg/00-Roadmap.4905d6567dff4753.webp)|
+|![ Скицата от @sketchthedocs https://sketchthedocs.dev](../../translated_images/bg/00-Roadmap.4905d6567dff4753.webp)|
 |:---:|
-| Наука за данни за начинаещи: Пътна карта - _Скичноут от [@nitya](https://twitter.com/nitya)_ |
+| Наука за данни за начинаещи: План за обучение - _Скица от [@nitya](https://twitter.com/nitya)_ |
 
 
-| Номер на урок | Тема | Група уроци | Учебни цели | Връзка към урок | Автор |
+| Номер на урок | Тема | Групиране на урока | Учебни цели | Свързан урок | Автор |
 | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
-| 01 | Определяне на науката за данни | [Въведение](1-Introduction/README.md) | Научете основните концепции зад науката за данни и как тя е свързана с изкуствения интелект, машинното обучение и големите данни. | [урок](1-Introduction/01-defining-data-science/README.md) [видео](https://youtu.be/beZ7Mb_oz9I) | [Дмитрий](http://soshnikov.com) |
-| 02 | Етика в науката за данни | [Въведение](1-Introduction/README.md) | Концепции, предизвикателства и рамки за етиката на данните. | [урок](1-Introduction/02-ethics/README.md) | [Нитя](https://twitter.com/nitya) |
-| 03 | Определяне на данни | [Въведение](1-Introduction/README.md) | Как се класифицират данните и техните често срещани източници. | [урок](1-Introduction/03-defining-data/README.md) | [Жасмин](https://www.twitter.com/paladique) |
-| 04 | Въведение в статистиката и вероятностите | [Въведение](1-Introduction/README.md) | Математическите техники по вероятности и статистика за разбиране на данни. | [урок](1-Introduction/04-stats-and-probability/README.md) [видео](https://youtu.be/Z5Zy85g4Yjw) | [Дмитрий](http://soshnikov.com) |
-| 05 | Работа с релационни данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в релационните данни и основи на изследване и анализ на релационни данни с езика за структурирани заявки, известен и като SQL (произнася се "си-квел"). | [урок](2-Working-With-Data/05-relational-databases/README.md) | [Кристофър](https://www.twitter.com/geektrainer) | | |
-| 06 | Работа с NoSQL данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в нерелационните данни, различните им типове и основи на изследване и анализ на документирани бази данни. | [урок](2-Working-With-Data/06-non-relational/README.md) | [Жасмин](https://twitter.com/paladique)|
-| 07 | Работа с Python | [Работа с данни](2-Working-With-Data/README.md) | Основи на използването на Python за изследване на данни с библиотеки като Pandas. Препоръчително е основно разбиране на програмирането с Python. | [урок](2-Working-With-Data/07-python/README.md) [видео](https://youtu.be/dZjWOGbsN4Y) | [Дмитрий](http://soshnikov.com) |
-| 08 | Подготовка на данни | [Работа с данни](2-Working-With-Data/README.md) | Теми върху техники за почистване и трансформация на данни за справяне с предизвикателства като липсващи, неточни или непълни данни. | [урок](2-Working-With-Data/08-data-preparation/README.md) | [Жасмин](https://www.twitter.com/paladique) |
-| 09 | Визуализация на количества | [Визуализация на данни](3-Data-Visualization/README.md) | Научете как да използвате Matplotlib за визуализация на данни за птици 🦆 | [урок](3-Data-Visualization/09-visualization-quantities/README.md) | [Джен](https://twitter.com/jenlooper) |
-| 10 | Визуализация на разпределения на данни | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на наблюдения и тенденции в интервал. | [урок](3-Data-Visualization/10-visualization-distributions/README.md) | [Джен](https://twitter.com/jenlooper) |
+| 01 | Дефиниране на науката за данни | [Въведение](1-Introduction/README.md) | Научете основните концепции зад науката за данни и как тя се свързва с изкуствения интелект, машинното обучение и големите данни. | [урок](1-Introduction/01-defining-data-science/README.md) [видео](https://youtu.be/beZ7Mb_oz9I) | [Дмитрий](http://soshnikov.com) |
+| 02 | Етика в науката за данни | [Въведение](1-Introduction/README.md) | Концепции, предизвикателства и рамки на етиката в данните. | [урок](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
+| 03 | Дефиниране на данни | [Въведение](1-Introduction/README.md) | Как се класифицират данните и техните чести източници. | [урок](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
+| 04 | Въведение в статистиката и вероятността | [Въведение](1-Introduction/README.md) | Математическите техники на вероятността и статистиката за разбиране на данните. | [урок](1-Introduction/04-stats-and-probability/README.md) [видео](https://youtu.be/Z5Zy85g4Yjw) | [Дмитрий](http://soshnikov.com) |
+| 05 | Работа с релационни данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в релационните данни и основите на изследване и анализ на релационни данни със структуриран език за заявки, известен като SQL (произнася се “си-куел”). | [урок](2-Working-With-Data/05-relational-databases/README.md) | [Кристофър](https://www.twitter.com/geektrainer) | | |
+| 06 | Работа с NoSQL данни | [Работа с данни](2-Working-With-Data/README.md) | Въведение в нерелационните данни, техните различни типове и основите на изследване и анализ на документ-ориентирани бази данни. | [урок](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
+| 07 | Работа с Python | [Работа с данни](2-Working-With-Data/README.md) | Основи на използване на Python за изследване на данни с библиотеки като Pandas. Препоръчва се основно разбиране на Python програмирането. | [урок](2-Working-With-Data/07-python/README.md) [видео](https://youtu.be/dZjWOGbsN4Y) | [Дмитрий](http://soshnikov.com) |
+| 08 | Подготовка на данни | [Работа с данни](2-Working-With-Data/README.md) | Теми за техники за почистване и трансформиране на данните за справяне с проблемите на липсващи, неточни или непълни данни. | [урок](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
+| 09 | Визуализация на количества | [Визуализация на данни](3-Data-Visualization/README.md) | Научете как да използвате Matplotlib за визуализиране на данни за птици 🦆 | [урок](3-Data-Visualization/09-visualization-quantities/README.md) | [Джен](https://twitter.com/jenlooper) |
+| 10 | Визуализация на разпределение на данните | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на наблюдения и тенденции в интервал. | [урок](3-Data-Visualization/10-visualization-distributions/README.md) | [Джен](https://twitter.com/jenlooper) |
 | 11 | Визуализация на пропорции | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на дискретни и групирани проценти. | [урок](3-Data-Visualization/11-visualization-proportions/README.md) | [Джен](https://twitter.com/jenlooper) |
-| 12 | Визуализация на връзки | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на връзки и корелации между набори от данни и техните променливи. | [урок](3-Data-Visualization/12-visualization-relationships/README.md) | [Джен](https://twitter.com/jenlooper) |
-| 13 | Значими визуализации | [Визуализация на данни](3-Data-Visualization/README.md) | Техники и насоки за създаване на визуализации, ценни за ефективно решаване на проблеми и извличане на прозрения. | [урок](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Джен](https://twitter.com/jenlooper) |
-| 14 | Въведение в жизнения цикъл на науката за данни | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Въведение в жизнения цикъл на науката за данни и първата му стъпка - придобиване и извличане на данни. | [урок](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Жасмин](https://twitter.com/paladique) |
-| 15 | Анализ | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Този етап от жизнения цикъл на науката за данни се фокусира върху техники за анализ на данни. | [урок](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Жасмин](https://twitter.com/paladique) | | |
-| 16 | Комуникация | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Този етап от жизнения цикъл на науката за данни се фокусира върху представянето на извлечените прозрения от данните по начин, който го прави по-лесен за разбиране от вземащите решения. | [урок](4-Data-Science-Lifecycle/16-communication/README.md) | [Джален](https://twitter.com/JalenMcG) | | |
-| 17 | Наука за данни в облака | [Облачни данни](5-Data-Science-In-Cloud/README.md) | Тази серия от уроци въвежда науката за данни в облака и нейните предимства. | [урок](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) |
-| 18 | Наука за данни в облака | [Облачни данни](5-Data-Science-In-Cloud/README.md) | Обучение на модели с инструменти Low Code. |[урок](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) |
-| 19 | Наука за данни в облака | [Облачни данни](5-Data-Science-In-Cloud/README.md) | Разгръщане на модели с Azure Machine Learning Studio. | [урок](5-Data-Science-In-Cloud/19-Azure/README.md)| [Тифани](https://twitter.com/TiffanySouterre) и [Мод](https://twitter.com/maudstweets) |
-| 20 | Наука за данни в реалния свят | [В дивата природа](6-Data-Science-In-Wild/README.md) | Проекти, базирани на науката за данни, в реалния свят. | [урок](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Нитя](https://twitter.com/nitya) |
+| 12 | Визуализация на взаимоотношения | [Визуализация на данни](3-Data-Visualization/README.md) | Визуализиране на връзки и корелации между набори от данни и техните променливи. | [урок](3-Data-Visualization/12-visualization-relationships/README.md) | [Джен](https://twitter.com/jenlooper) |
+| 13 | Смислени визуализации | [Визуализация на данни](3-Data-Visualization/README.md) | Техники и насоки за правене на визуализациите стойностни за ефективно решаване на проблеми и изводи. | [урок](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Джен](https://twitter.com/jenlooper) |
+| 14 | Въведение в жизнения цикъл на науката за данни | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Въведение в жизнения цикъл на науката за данни и първата му стъпка – придобиване и извличане на данни. | [урок](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
+| 15 | Анализиране | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Тази фаза от жизнения цикъл на науката за данни се фокусира върху техники за анализ на данни. | [урок](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
+| 16 | Комуникация | [Жизнен цикъл](4-Data-Science-Lifecycle/README.md) | Тази фаза от жизнения цикъл на науката за данни се фокусира върху представянето на изводите от данните по начин, който улеснява разбирането им от вземащите решения. | [урок](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
+| 17 | Наука за данни в облака | [Облачни данни](5-Data-Science-In-Cloud/README.md) | Тази серия от уроци представя науката за данни в облака и нейните предимства. | [урок](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мауд](https://twitter.com/maudstweets) |
+| 18 | Наука за данни в облака | [Облачни данни](5-Data-Science-In-Cloud/README.md) | Обучение на модели с използване на Low Code инструменти. |[урок](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Тифани](https://twitter.com/TiffanySouterre) и [Мауд](https://twitter.com/maudstweets) |
+| 19 | Наука за данни в облака | [Облачни данни](5-Data-Science-In-Cloud/README.md) | Деплойване на модели с Azure Machine Learning Studio. | [урок](5-Data-Science-In-Cloud/19-Azure/README.md)| [Тифани](https://twitter.com/TiffanySouterre) и [Мауд](https://twitter.com/maudstweets) |
+| 20 | Наука за данни в реалния свят | [В дивата природа](6-Data-Science-In-Wild/README.md) | Проекти, базирани на науката за данните, в реалния свят. | [урок](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
 
 ## GitHub Codespaces
 
 Следвайте тези стъпки, за да отворите този пример в Codespace:
 1. Кликнете на падащото меню Code и изберете опцията Open with Codespaces.
 2. Изберете + New codespace в долната част на панела.
-За повече информация вижте [документацията на GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
+За повече информация вижте [GitHub документацията](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
 
 ## VSCode Remote - Контейнери
-Следвайте тези стъпки, за да отворите това хранилище в контейнер, използвайки локалния си компютър и VSCode чрез разширението VS Code Remote - Containers:
+Следвайте тези стъпки, за да отворите това репо в контейнер, използвайки локалната си машина и VSCode чрез разширението VS Code Remote - Containers:
 
-1. Ако това е първият ви път с development контейнер, уверете се, че системата ви отговаря на изискванията (например, че Docker е инсталиран) в [ръководството за започване](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
+1. Ако използвате контейнер за разработка за първи път, моля уверете се, че системата ви отговаря на изискванията (т.е. имате инсталиран Docker) в [документацията за начало](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
 
-За да използвате това хранилище, можете да отворите хранилището в изолиран Docker обем:
+За да използвате това репо, можете или да го отворите в изолиран Docker volume:
 
-**Забележка**: Под капака, това използва командата Remote-Containers: **Clone Repository in Container Volume...**, за да клонира изходния код в Docker обем вместо в локалната файлова система. [Обемите](https://docs.docker.com/storage/volumes/) са предпочитаният механизъм за съхранение на данни на контейнер.
+**Забележка**: Под капака това ще използва командата Remote-Containers: **Clone Repository in Container Volume...**, за да клонира изходния код в Docker volume вместо в локалната файлова система. [Volumes](https://docs.docker.com/storage/volumes/) са предпочитаният механизъм за съхранение на данни на контейнера.
 
-Или отворете локално клонирана или изтеглена версия на хранилището:
+Или да отворите локално клонирано или изтеглено копие на репото:
 
-- Клонирайте това хранилище на локалната файлова система.
-- Натиснете F1 и изберете командата **Remote-Containers: Open Folder in Container...**.
-- Изберете клонираното копие на тази папка, изчакайте контейнерът да стартира и тествате.
+- Клонирайте това репо на локалната файлова система.
+- Натиснете F1 и изберете команда **Remote-Containers: Open Folder in Container...**.
+- Изберете клонираното копие на тази папка, изчакайте да стартира контейнера и пробвайте.
 
 ## Офлайн достъп
 
-Можете да използвате тази документация офлайн чрез [Docsify](https://docsify.js.org/#/). Форкнете това хранилище, [инсталирайте Docsify](https://docsify.js.org/#/quickstart) на локалната си машина и след това в основната папка на това хранилище въведете `docsify serve`. Уебсайтът ще се обслужва на порт 3000 на вашия локален хост: `localhost:3000`.
+Можете да изпълнявате тази документация офлайн чрез [Docsify](https://docsify.js.org/#/). Форкнете това репо, [инсталирайте Docsify](https://docsify.js.org/#/quickstart) на локалната си машина, след това в основната папка на това репо, изпълнете `docsify serve`. Уебсайтът ще бъде обслужван на порт 3000 на localhost: `localhost:3000`.
 
-> Забележка: бележниците няма да се рендерират чрез Docsify, затова ако трябва да стартирате бележник, направете го отделно във VS Code с изпълняващ се Python kernel.
+> Забележка: бележниците няма да се визуализират с Docsify, така че когато трябва да изпълните бележник, направете го отделно в VS Code, използвайки Python kernel.
 
 ## Други учебни програми
 
@@ -200,54 +210,54 @@ Azure Cloud Advocates в Microsoft с удоволствие предлагат
 [![LangChain за начинаещи](https://img.shields.io/badge/LangChain%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://github.com/microsoft/langchain-for-beginners?WT.mc_id=m365-94501-dwahlin)
 ---
 
-### Azure / Edge / MCP / Агентии
+### Azure / Edge / MCP / Агентите
 [![AZD за начинаещи](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Edge AI за начинаещи](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![MCP за начинаещи](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![AI агенти за начинаещи](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
-
-### Серия за Генеративен AI
+ 
+### Поредицата за генеративен AI
 [![Генеративен AI за начинаещи](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Генеративен AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
 [![Генеративен AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
 [![Генеративен AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
-
+ 
 ### Основно обучение
 [![Машинно обучение за начинаещи](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![Наука за данни за начинаещи](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![Изкуствен интелект за начинаещи](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![Данни науки за начинаещи](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI за начинаещи](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
 [![Киберсигурност за начинаещи](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
 [![Уеб разработка за начинаещи](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
 [![IoT за начинаещи](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![XR разработка за начинаещи](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR Разработка за начинаещи](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
-
-### Серия Copilot
-[![Copilot за съвместно програмиране с AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+ 
+### Поредицата Copilot
+[![Copilot за AI съвместно програмиране](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
 [![Copilot за C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![Приключение с Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Copilot приключение](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## Получаване на помощ
+## Помощ
 
-**Имате проблеми?** Разгледайте нашето [Ръководство за отстраняване на проблеми](TROUBLESHOOTING.md) за решения на често срещани проблеми.
+**Срещате проблеми?** Вижте нашето [Ръководство за отстраняване на проблеми](TROUBLESHOOTING.md) за решения на често срещани проблеми.
 
-Ако се затруднявате или имате въпроси относно създаването на AI приложения, присъединете се към други учащи и опитни разработчици в дискусии за MCP. Това е подкрепяща общност, където въпросите са добре дошли и знанията се споделят свободно.
+Ако забиете или имате въпроси относно създаването на AI приложения, присъединете се към други учащи и опитни разработчици в дискусиите за MCP. Това е подкрепяща общност, където въпросите са добре дошли и знанието се споделя свободно.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Ако имате обратна връзка за продукта или намерите грешки при разработка, посетете:
+Ако имате обратна връзка за продукта или грешки по време на изграждането, посетете:
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-**Отказ от отговорност**:
-Този документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Докато се стремим към точност, моля, имайте предвид, че автоматизираните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия оригинален език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Не носим отговорност за никакви недоразумения или неправилни тълкувания, възникнали при използването на този превод.
+**Отказ от отговорност**:  
+Този документ е преведен с помощта на автоматизирана услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, имайте предвид, че автоматизираните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия първоначален език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Не носим отговорност за възникнали недоразумения или неправилни тълкувания вследствие на използването на този превод.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/bg/sketchnotes/README.md b/translations/bg/sketchnotes/README.md
index a9b496e0..8ec23233 100644
--- a/translations/bg/sketchnotes/README.md
+++ b/translations/bg/sketchnotes/README.md
@@ -4,7 +4,7 @@
 
 Нитя Нарасимхан, художник
 
-![скицнота на пътна карта](../../../translated_images/bg/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)
+![скицнота на пътна карта](../../../translated_images/bg/00-Roadmap.4905d6567dff4753.webp)
 
 ---
 
diff --git a/translations/ro/.co-op-translator.json b/translations/ro/.co-op-translator.json
index 2108074b..cc71c9aa 100644
--- a/translations/ro/.co-op-translator.json
+++ b/translations/ro/.co-op-translator.json
@@ -11,12 +11,24 @@
     "source_file": "1-Introduction/01-defining-data-science/assignment.md",
     "language_code": "ro"
   },
+  "1-Introduction/01-defining-data-science/notebook.ipynb": {
+    "original_hash": "8f5eb7b3f7cc89e6d98fb32e1de65dec",
+    "translation_date": "2026-02-27T11:37:35+00:00",
+    "source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
+    "language_code": "ro"
+  },
   "1-Introduction/01-defining-data-science/solution/assignment.md": {
     "original_hash": "a8f79b9c0484c35b4f26e8aec7fc4d56",
     "translation_date": "2025-08-26T15:26:51+00:00",
     "source_file": "1-Introduction/01-defining-data-science/solution/assignment.md",
     "language_code": "ro"
   },
+  "1-Introduction/01-defining-data-science/solution/notebook.ipynb": {
+    "original_hash": "090bbfbfcb0c40d3d6e3236f836164ea",
+    "translation_date": "2026-02-27T11:38:10+00:00",
+    "source_file": "1-Introduction/01-defining-data-science/solution/notebook.ipynb",
+    "language_code": "ro"
+  },
   "1-Introduction/02-ethics/README.md": {
     "original_hash": "58860ce9a4b8a564003d2752f7c72851",
     "translation_date": "2025-10-03T16:57:37+00:00",
@@ -95,6 +107,12 @@
     "source_file": "2-Working-With-Data/07-python/assignment.md",
     "language_code": "ro"
   },
+  "2-Working-With-Data/07-python/notebook-covidspread.ipynb": {
+    "original_hash": "6335cccba01dc6ad7b15aba7a8c73f38",
+    "translation_date": "2026-02-27T11:39:05+00:00",
+    "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
+    "language_code": "ro"
+  },
   "2-Working-With-Data/08-data-preparation/README.md": {
     "original_hash": "1b560955ff39a2bcf2a049fce474a951",
     "translation_date": "2025-09-05T18:23:34+00:00",
@@ -360,8 +378,8 @@
     "language_code": "ro"
   },
   "README.md": {
-    "original_hash": "9204a2806964384a56f5cb0f22bbe953",
-    "translation_date": "2026-02-06T08:56:46+00:00",
+    "original_hash": "f671e295a294a2559fc59d1524e001b4",
+    "translation_date": "2026-02-27T11:45:05+00:00",
     "source_file": "README.md",
     "language_code": "ro"
   },
diff --git a/translations/ro/1-Introduction/01-defining-data-science/README.md b/translations/ro/1-Introduction/01-defining-data-science/README.md
index eec3c1ce..ab1029df 100644
--- a/translations/ro/1-Introduction/01-defining-data-science/README.md
+++ b/translations/ro/1-Introduction/01-defining-data-science/README.md
@@ -6,7 +6,7 @@
 
 ---
 
-[![Video despre Definirea Științei Datelor](../../../../translated_images/ro/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I)
+[![Video despre Definirea Științei Datelor](../../../../translated_images/ro/video-def-ds.6623ee2392ef1abf.webp)](https://youtu.be/beZ7Mb_oz9I)
 
 ## [Chestionar înainte de curs](https://ff-quizzes.netlify.app/en/ds/quiz/0)
 
@@ -144,7 +144,7 @@ Dacă dorim să complicăm și mai mult lucrurile, putem reprezenta grafic timpu
 
 În această provocare, vom încerca să identificăm concepte relevante pentru domeniul Științei Datelor analizând texte. Vom lua un articol de pe Wikipedia despre Știința Datelor, vom descărca și procesa textul, iar apoi vom construi un nor de cuvinte asemănător cu acesta:
 
-![Nor de cuvinte pentru Știința Datelor](../../../../translated_images/ro/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png)
+![Nor de cuvinte pentru Știința Datelor](../../../../translated_images/ro/ds_wordcloud.664a7c07dca57de0.webp)
 
 Vizitează [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') pentru a parcurge codul. Poți, de asemenea, să rulezi codul și să vezi cum efectuează toate transformările de date în timp real.
 
diff --git a/translations/ro/1-Introduction/01-defining-data-science/notebook.ipynb b/translations/ro/1-Introduction/01-defining-data-science/notebook.ipynb
index 761751a4..b764d74c 100644
--- a/translations/ro/1-Introduction/01-defining-data-science/notebook.ipynb
+++ b/translations/ro/1-Introduction/01-defining-data-science/notebook.ipynb
@@ -3,15 +3,15 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Provocare: Analiza textului despre Știința Datelor\n",
+    "# Provocare: Analiza textului despre Știința datelor\n",
     "\n",
-    "În acest exemplu, vom face un exercițiu simplu care acoperă toate etapele unui proces tradițional de știința datelor. Nu trebuie să scrii niciun cod, poți doar să dai clic pe celulele de mai jos pentru a le executa și a observa rezultatul. Ca provocare, ești încurajat să încerci acest cod cu date diferite.\n",
+    "În acest exemplu, să facem un exercițiu simplu care acoperă toți pașii unui proces tradițional de știință a datelor. Nu trebuie să scrii cod, poți doar să dai clic pe celulele de mai jos pentru a le executa și a observa rezultatul. Ca provocare, ești încurajat să încerci acest cod cu date diferite.\n",
     "\n",
     "## Scop\n",
     "\n",
-    "În această lecție, am discutat diferite concepte legate de Știința Datelor. Haideți să încercăm să descoperim mai multe concepte conexe prin **analiza textului**. Vom începe cu un text despre Știința Datelor, vom extrage cuvinte-cheie din acesta și apoi vom încerca să vizualizăm rezultatul.\n",
+    "În această lecție, am discutat diverse concepte legate de Știința datelor. Să încercăm să descoperim mai multe concepte conexe prin efectuarea unei **extragere de text**. Vom începe cu un text despre Știința datelor, vom extrage cuvinte-cheie din acesta, și apoi vom încerca să vizualizăm rezultatul.\n",
     "\n",
-    "Ca text, voi folosi pagina despre Știința Datelor de pe Wikipedia:\n"
+    "Ca text, voi folosi pagina despre Știința datelor de pe Wikipedia:\n"
    ],
    "metadata": {}
   },
@@ -32,9 +32,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Pasul 1: Obținerea datelor\n",
+    "## Step 1: Obținerea datelor\n",
     "\n",
-    "Primul pas în orice proces de știința datelor este obținerea datelor. Vom folosi biblioteca `requests` pentru a face acest lucru:\n"
+    "Primul pas în orice proces de știință a datelor este obținerea datelor. Vom folosi biblioteca `requests` pentru a face acest lucru:\n"
    ],
    "metadata": {}
   },
@@ -68,43 +68,41 @@
    "source": [
     "## Pasul 2: Transformarea datelor\n",
     "\n",
-    "Următorul pas este să convertim datele într-o formă potrivită pentru procesare. În cazul nostru, am descărcat codul sursă HTML de pe pagină și trebuie să-l transformăm în text simplu.\n",
+    "Următorul pas este să convertim datele într-o formă potrivită pentru procesare. În cazul nostru, am descărcat codul sursă HTML de pe pagină și trebuie să îl convertim în text simplu.\n",
     "\n",
-    "Există multe moduri prin care acest lucru poate fi realizat. Vom folosi cel mai simplu obiect [HTMLParser](https://docs.python.org/3/library/html.parser.html) integrat din Python. Trebuie să extindem clasa `HTMLParser` și să definim codul care va colecta tot textul din interiorul etichetelor HTML, cu excepția etichetelor `<script>` și `<style>`.\n"
+    "Există multe moduri prin care se poate face acest lucru. Vom folosi [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/), o bibliotecă Python populară pentru parsarea HTML. BeautifulSoup ne permite să țintim anumite elemente HTML specifice, astfel încât să ne concentrăm pe conținutul principal al articolului de pe Wikipedia și să reducem unele meniuri de navigare, bare laterale, footere și alte conținuturi irelevante (deși unele texte tip boilerplate pot rămâne).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mai întâi, trebuie să instalăm biblioteca BeautifulSoup pentru parsarea HTML:\n"
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import sys\r\n",
+    "!{sys.executable} -m pip install beautifulsoup4"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 64,
    "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
+    "from bs4 import BeautifulSoup\r\n\r\n# Parse the HTML content\r\nsoup = BeautifulSoup(text, 'html.parser')\r\n\r\n# Extract only the main article content from Wikipedia\r\n# Wikipedia uses 'mw-parser-output' class for the main article content\r\ncontent = soup.find('div', class_='mw-parser-output')\r\n\r\ndef clean_wikipedia_content(content_node):\r\n    \"\"\"Remove common non-article elements from a Wikipedia content node.\"\"\"\r\n    # Strip jump links, navboxes, reference lists/superscripts, edit sections, TOC, sidebars, etc.\r\n    selectors = [\r\n        '.mw-jump-link',\r\n        '.navbox',\r\n        '.reflist',\r\n        'sup.reference',\r\n        '.mw-editsection',\r\n        '.hatnote',\r\n        '.metadata',\r\n        '.infobox',\r\n        '#toc',\r\n        '.toc',\r\n        '.sidebar',\r\n    ]\r\n    for selector in selectors:\r\n        for el in content_node.select(selector):\r\n            el.decompose()\r\n\r\nif content:\r\n    # Clean the content node to better approximate article text only.\r\n    clean_wikipedia_content(content)\r\n    text = content.get_text(separator=' ', strip=True)\r\n    print(text[:1000])\r\nelse:\r\n    print(\"Could not find main content. Using full page text.\")\r\n    text = soup.get_text(separator=' ', strip=True)\r\n    print(text[:1000])"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      " Data science - Wikipedia Data science From Wikipedia, the free encyclopedia Jump to navigation Jump to search Interdisciplinary field of study focused on deriving knowledge and insights from data Not to be confused with  information science . The existence of  Comet NEOWISE  (here depicted as a series of red dots) was discovered by analyzing  astronomical survey  data acquired by a  space telescope , the  Wide-field Infrared Survey Explorer . Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning ( classification  •  regression ) Decision trees Ensembles Bagging Boosting Random forest k -NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine \n"
+      "Data science From Wikipedia, the free encyclopedia Interdisciplinary field of study focused on deriving knowledge and insights from data Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain. Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.\n"
      ]
     }
    ],
@@ -113,11 +111,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Pasul 3: Obținerea de informații\n",
+    "## Step 3: Obținerea informațiilor\n",
     "\n",
-    "Cel mai important pas este să transformăm datele noastre într-o formă din care să putem extrage informații utile. În cazul nostru, dorim să extragem cuvinte cheie din text și să vedem care dintre acestea sunt mai semnificative.\n",
+    "Cel mai important pas este să transformăm datele într-o formă din care putem extrage informații. În cazul nostru, vrem să extragem cuvinte cheie din text și să vedem care cuvinte cheie sunt mai semnificative.\n",
     "\n",
-    "Vom folosi o bibliotecă Python numită [RAKE](https://github.com/aneesha/RAKE) pentru extragerea cuvintelor cheie. Mai întâi, să instalăm această bibliotecă dacă nu este deja prezentă:\n"
+    "Vom folosi biblioteca Python numită [RAKE](https://github.com/aneesha/RAKE) pentru extragerea cuvintelor cheie. Mai întâi, să instalăm această bibliotecă în cazul în care nu este prezentă:\n"
    ],
    "metadata": {}
   },
@@ -162,7 +160,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Funcționalitatea principală este disponibilă prin obiectul `Rake`, pe care îl putem personaliza folosind câțiva parametri. În cazul nostru, vom seta lungimea minimă a unui cuvânt cheie la 5 caractere, frecvența minimă a unui cuvânt cheie în document la 3 și numărul maxim de cuvinte dintr-un cuvânt cheie la 2. Simțiți-vă liber să experimentați cu alte valori și să observați rezultatul.\n"
+    "Funcționalitatea principală este disponibilă prin obiectul `Rake`, pe care îl putem personaliza folosind câțiva parametri. În cazul nostru, vom seta lungimea minimă a unui cuvânt-cheie la 5 caractere, frecvența minimă a unui cuvânt-cheie în document la 3 și numărul maxim de cuvinte într-un cuvânt-cheie - la 2. Simțiți-vă liber să experimentați cu alte valori și să observați rezultatul.\n"
    ],
    "metadata": {}
   },
@@ -211,11 +209,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Am obținut o listă de termeni împreună cu gradul lor de importanță asociat. După cum puteți vedea, cele mai relevante discipline, cum ar fi machine learning și big data, sunt prezente în listă pe pozițiile de top.\n",
     "\n",
-    "## Pasul 4: Vizualizarea Rezultatelor\n",
+    "Am obținut o listă de termeni împreună cu gradul asociat de importanță. După cum puteți vedea, disciplinele cele mai relevante, cum ar fi învățarea automată și big data, sunt prezente în listă în poziții de top.\n",
     "\n",
-    "Oamenii interpretează cel mai bine datele în formă vizuală. Astfel, deseori are sens să vizualizăm datele pentru a obține anumite perspective. Putem folosi biblioteca `matplotlib` din Python pentru a reprezenta grafic distribuția simplă a cuvintelor cheie împreună cu relevanța lor:\n"
+    "## Pasul 4: Vizualizarea rezultatului\n",
+    "\n",
+    "Oamenii pot interpreta cel mai bine datele în formă vizuală. Astfel, adesea are sens să vizualizăm datele pentru a extrage unele concluzii. Putem folosi biblioteca `matplotlib` în Python pentru a reprezenta grafic distribuția simplă a cuvintelor cheie cu relevanța lor:\n"
    ],
    "metadata": {}
   },
@@ -252,7 +251,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Există, totuși, o modalitate și mai bună de a vizualiza frecvențele cuvintelor - folosind **Word Cloud**. Va trebui să instalăm o altă bibliotecă pentru a crea un word cloud din lista noastră de cuvinte cheie.\n"
+    "Există, totuși, o metodă și mai bună de a vizualiza frecvențele cuvintelor - utilizând **Nori de Cuvinte**. Va trebui să instalăm o altă bibliotecă pentru a genera norul de cuvinte din lista noastră de cuvinte cheie.\n"
    ],
    "metadata": {}
   },
@@ -268,7 +267,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Obiectul `WordCloud` este responsabil pentru preluarea fie a textului original, fie a unei liste pre-calculate de cuvinte cu frecvențele lor, și returnează o imagine, care poate fi apoi afișată folosind `matplotlib`:\n"
+    "Obiectul `WordCloud` este responsabil pentru preluarea fie a textului original, fie a unei liste pre-computate de cuvinte cu frecvențele lor, și returnează o imagine, care poate fi apoi afișată folosind `matplotlib`:\n"
    ],
    "metadata": {}
   },
@@ -312,7 +311,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Putem, de asemenea, să introducem textul original în `WordCloud` - să vedem dacă putem obține un rezultat similar:\n"
+    "Putem, de asemenea, să trecem textul original în `WordCloud` - să vedem dacă putem obține un rezultat similar:\n"
    ],
    "metadata": {}
   },
@@ -372,11 +371,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Puteți observa că norul de cuvinte arată acum mai impresionant, dar conține și mult zgomot (de exemplu, cuvinte fără legătură precum `Retrieved on`). De asemenea, obținem mai puține cuvinte-cheie formate din două cuvinte, cum ar fi *data scientist* sau *computer science*. Acest lucru se întâmplă deoarece algoritmul RAKE face o treabă mult mai bună în selectarea cuvintelor-cheie relevante din text. Acest exemplu ilustrează importanța pre-procesării și curățării datelor, deoarece o imagine clară la final ne va permite să luăm decizii mai bune.\n",
+    "Puteți vedea că norul de cuvinte arată acum mai impresionant, dar conține și mult zgomot (de ex. cuvinte fără legătură, cum ar fi `Retrieved on`). De asemenea, obținem mai puține cuvinte cheie formate din două cuvinte, cum ar fi *data scientist* sau *computer science*. Acest lucru se datorează faptului că algoritmul RAKE face o treabă mult mai bună în selectarea cuvintelor cheie bune din text. Acest exemplu ilustrează importanța pre-procesării și curățării datelor, deoarece o imagine clară la final ne va permite să luăm decizii mai bune.\n",
     "\n",
-    "În acest exercițiu am parcurs un proces simplu de extragere a unor semnificații dintr-un text de pe Wikipedia, sub forma cuvintelor-cheie și a unui nor de cuvinte. Acest exemplu este destul de simplu, dar demonstrează bine toate etapele tipice pe care un data scientist le parcurge atunci când lucrează cu date, începând de la achiziția datelor până la vizualizare.\n",
+    "În acest exercițiu am parcurs un proces simplu de extragere a unor semnificații din textul Wikipedia, sub forma de cuvinte cheie și nor de cuvinte. Acest exemplu este destul de simplu, dar demonstrează bine toate etapele tipice pe care un data scientist le parcurge când lucrează cu date, începând de la achiziția datelor, până la vizualizare.\n",
     "\n",
-    "În cadrul cursului nostru vom discuta toate aceste etape în detaliu.\n"
+    "În cursul nostru vom discuta toate aceste etape în detaliu.  \n"
    ],
    "metadata": {}
   },
@@ -394,7 +393,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Declinare a responsabilității**:\nAcest document a fost tradus folosind serviciul de traducere automată AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim pentru acuratețe, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original, în limba sa nativă, trebuie considerat sursa autorizată. Pentru informații critice, se recomandă traducerea profesională realizată de un traducător uman. Nu ne asumăm responsabilitatea pentru eventualele neînțelegeri sau interpretări greșite care pot rezulta din utilizarea acestei traduceri.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -418,12 +417,6 @@
   },
   "interpreter": {
    "hash": "c28e7b6bf4e5b397b8288a85bf0a94ea8d3585ce2b01919feb195678ec71581b"
-  },
-  "coopTranslator": {
-   "original_hash": "50c0f0a5204a18405611cbe7e0fec56b",
-   "translation_date": "2025-09-02T10:04:51+00:00",
-   "source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
-   "language_code": "ro"
   }
  },
  "nbformat": 4,
diff --git a/translations/ro/1-Introduction/01-defining-data-science/solution/notebook.ipynb b/translations/ro/1-Introduction/01-defining-data-science/solution/notebook.ipynb
index a523aaa6..8254748a 100644
--- a/translations/ro/1-Introduction/01-defining-data-science/solution/notebook.ipynb
+++ b/translations/ro/1-Introduction/01-defining-data-science/solution/notebook.ipynb
@@ -5,13 +5,13 @@
    "source": [
     "# Provocare: Analiza textului despre Știința Datelor\n",
     "\n",
-    "> *În acest notebook, experimentăm cu utilizarea unui URL diferit - articolul Wikipedia despre Învățarea Automată. Puteți observa că, spre deosebire de Știința Datelor, acest articol conține o mulțime de termeni, ceea ce face analiza mai problematică. Trebuie să găsim o altă metodă pentru a curăța datele după extragerea cuvintelor cheie, pentru a elimina unele combinații de cuvinte frecvente, dar lipsite de semnificație.*\n",
+    "> *În acest caiet, experimentăm cu folosirea diferitelor URL-uri - articol Wikipedia despre Învățarea Automată. Se poate observa că, spre deosebire de Știința Datelor, acest articol conține o mulțime de termeni, ceea ce face analiza mai problematică. Trebuie să găsim o altă metodă de a curăța datele după extragerea cuvintelor cheie, pentru a scăpa de unele combinații frecvente, dar fără semnificație.*\n",
     "\n",
-    "În acest exemplu, să facem un exercițiu simplu care acoperă toți pașii unui proces tradițional de știința datelor. Nu trebuie să scrieți niciun cod, puteți doar să faceți clic pe celulele de mai jos pentru a le executa și a observa rezultatul. Ca provocare, sunteți încurajați să încercați acest cod cu date diferite.\n",
+    "În acest exemplu, să facem un exercițiu simplu care acoperă toate etapele unui proces tradițional de știință a datelor. Nu trebuie să scrieți niciun cod, puteți doar să faceți clic pe celulele de mai jos pentru a le executa și a observa rezultatul. Ca provocare, sunteți încurajați să încercați acest cod cu date diferite.\n",
     "\n",
     "## Obiectiv\n",
     "\n",
-    "În această lecție, am discutat diferite concepte legate de Știința Datelor. Să încercăm să descoperim mai multe concepte conexe prin **minarea textului**. Vom începe cu un text despre Știința Datelor, vom extrage cuvinte cheie din acesta și apoi vom încerca să vizualizăm rezultatul.\n",
+    "În această lecție, am discutat diferite concepte legate de Știința Datelor. Să încercăm să descoperim mai multe concepte aferente făcând o **extragere de text**. Vom începe cu un text despre Știința Datelor, vom extrage cuvintele cheie din acesta, apoi vom încerca să vizualizăm rezultatul.\n",
     "\n",
     "Ca text, voi folosi pagina despre Știința Datelor de pe Wikipedia:\n"
    ],
@@ -37,7 +37,7 @@
    "source": [
     "## Pasul 1: Obținerea datelor\n",
     "\n",
-    "Primul pas în orice proces de știința datelor este obținerea datelor. Vom folosi biblioteca `requests` pentru a face acest lucru:\n"
+    "Primul pas în orice proces de știință a datelor este obținerea datelor. Vom folosi biblioteca `requests` pentru asta:\n"
    ],
    "metadata": {}
   },
@@ -71,43 +71,41 @@
    "source": [
     "## Pasul 2: Transformarea datelor\n",
     "\n",
-    "Următorul pas este să convertim datele într-o formă potrivită pentru procesare. În cazul nostru, am descărcat codul sursă HTML al paginii și trebuie să-l transformăm în text simplu.\n",
+    "Următorul pas este să convertim datele într-o formă potrivită pentru procesare. În cazul nostru, am descărcat codul sursă HTML al paginii și trebuie să îl transformăm în text simplu.\n",
     "\n",
-    "Există multe moduri în care acest lucru poate fi realizat. Vom folosi cel mai simplu obiect [HTMLParser](https://docs.python.org/3/library/html.parser.html) integrat în Python. Trebuie să extindem clasa `HTMLParser` și să definim codul care va colecta tot textul din interiorul etichetelor HTML, cu excepția etichetelor `<script>` și `<style>`.\n"
+    "Există multe moduri în care acest lucru poate fi făcut. Vom folosi [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/), o bibliotecă Python populară pentru parsarea HTML. BeautifulSoup ne permite să țintim elemente HTML specifice, astfel încât să ne putem concentra pe conținutul principal al articolului de pe Wikipedia și să reducem unele meniuri de navigare, bare laterale, subsoluri și alte conținuturi irelevante (deși poate rămâne încă unele texte standard).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mai întâi, trebuie să instalăm biblioteca BeautifulSoup pentru analiza HTML:\n"
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import sys\r\n",
+    "!{sys.executable} -m pip install beautifulsoup4"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
+    "from bs4 import BeautifulSoup\r\n\r\n# Parse the HTML content\r\nsoup = BeautifulSoup(text, 'html.parser')\r\n\r\n# Extract only the main article content from Wikipedia\r\n# Wikipedia uses 'mw-parser-output' class for the main article content\r\ncontent = soup.find('div', class_='mw-parser-output')\r\n\r\ndef clean_wikipedia_content(content_node):\r\n    \"\"\"Remove common non-article elements from a Wikipedia content node.\"\"\"\r\n    # Strip jump links, navboxes, reference lists/superscripts, edit sections, TOC, sidebars, etc.\r\n    selectors = [\r\n        '.mw-jump-link',\r\n        '.navbox',\r\n        '.reflist',\r\n        'sup.reference',\r\n        '.mw-editsection',\r\n        '.hatnote',\r\n        '.metadata',\r\n        '.infobox',\r\n        '#toc',\r\n        '.toc',\r\n        '.sidebar',\r\n    ]\r\n    for selector in selectors:\r\n        for el in content_node.select(selector):\r\n            el.decompose()\r\n\r\nif content:\r\n    # Clean the content node to better approximate article text only.\r\n    clean_wikipedia_content(content)\r\n    text = content.get_text(separator=' ', strip=True)\r\n    print(text[:1000])\r\nelse:\r\n    print(\"Could not find main content. Using full page text.\")\r\n    text = soup.get_text(separator=' ', strip=True)\r\n    print(text[:1000])"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      " Machine learning - Wikipedia Machine learning From Wikipedia, the free encyclopedia Jump to navigation Jump to search Study of algorithms that improve automatically through experience For the journal, see  Machine Learning (journal) . \"Statistical learning\" redirects here. For statistical learning in linguistics, see  statistical learning in language acquisition . Part of a series on Artificial intelligence Major goals Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms Philosophy Ethics Existential risk Turing test Chinese room Control problem Friendly AI History Timeline Progress AI winter Technology Applications Projects Programming languages Glossary Glossary v t e Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection Data Cleaning AutoML Associ\n"
+      "Machine learning From Wikipedia, the free encyclopedia Study of algorithms that improve automatically through experience Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.\n"
      ]
     }
    ],
@@ -116,11 +114,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Pasul 3: Obținerea de informații\n",
+    "## Pasul 3: Obținerea Insight-urilor\n",
     "\n",
-    "Cel mai important pas este să transformăm datele noastre într-o formă din care să putem extrage informații utile. În cazul nostru, dorim să extragem cuvinte cheie din text și să vedem care dintre acestea sunt mai semnificative.\n",
+    "Cel mai important pas este să transformăm datele noastre într-o formă din care putem extrage insight-uri. În cazul nostru, dorim să extragem cuvinte cheie din text și să vedem care cuvinte cheie sunt mai relevante.\n",
     "\n",
-    "Vom folosi biblioteca Python numită [RAKE](https://github.com/aneesha/RAKE) pentru extragerea cuvintelor cheie. Mai întâi, să instalăm această bibliotecă în cazul în care nu este deja prezentă:\n"
+    "Vom folosi biblioteca Python numită [RAKE](https://github.com/aneesha/RAKE) pentru extragerea cuvintelor cheie. Mai întâi, să instalăm această bibliotecă în cazul în care nu este deja prezentă: \n"
    ],
    "metadata": {}
   },
@@ -172,7 +170,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Funcționalitatea principală este disponibilă prin obiectul `Rake`, pe care îl putem personaliza folosind anumiți parametri. În cazul nostru, vom seta lungimea minimă a unui cuvânt cheie la 5 caractere, frecvența minimă a unui cuvânt cheie în document la 3 și numărul maxim de cuvinte dintr-un cuvânt cheie la 2. Simțiți-vă liber să experimentați cu alte valori și să observați rezultatul.\n"
+    "Funcționalitatea principală este disponibilă prin obiectul `Rake`, pe care îl putem personaliza folosind câțiva parametri. În cazul nostru, vom seta lungimea minimă a unui cuvânt cheie la 5 caractere, frecvența minimă a unui cuvânt cheie în document la 3 și numărul maxim de cuvinte într-un cuvânt cheie - la 2. Simțiți-vă liber să experimentați cu alte valori și să observați rezultatul.\n"
    ],
    "metadata": {}
   },
@@ -353,11 +351,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Am obținut o listă de termeni împreună cu gradul lor de importanță asociat. După cum puteți observa, cele mai relevante discipline, precum machine learning și big data, sunt prezente în listă pe pozițiile de top.\n",
     "\n",
-    "## Pasul 4: Vizualizarea Rezultatelor\n",
+    "Am obținut o listă de termeni împreună cu gradul asociat de importanță. După cum puteți observa, cele mai relevante discipline, cum ar fi învățarea automată și big data, sunt prezente în listă în poziții de top.\n",
     "\n",
-    "Oamenii interpretează cel mai bine datele în formă vizuală. Prin urmare, deseori are sens să vizualizăm datele pentru a extrage anumite informații. Putem folosi biblioteca `matplotlib` din Python pentru a crea o distribuție simplă a cuvintelor cheie împreună cu relevanța lor:\n"
+    "## Pasul 4: Vizualizarea rezultatului\n",
+    "\n",
+    "Oamenii pot interpreta cel mai bine datele în formă vizuală. Prin urmare, adesea are sens să vizualizăm datele pentru a extrage unele concluzii. Putem folosi biblioteca `matplotlib` în Python pentru a reprezenta grafic distribuția simplă a cuvintelor cheie împreună cu relevanța lor:\n"
    ],
    "metadata": {}
   },
@@ -392,7 +391,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Există, totuși, o modalitate și mai bună de a vizualiza frecvențele cuvintelor - folosind **Word Cloud**. Va trebui să instalăm o altă bibliotecă pentru a crea un word cloud din lista noastră de cuvinte cheie.\n"
+    "Există, totuși, o metodă și mai bună de a vizualiza frecvențele cuvintelor - folosind **Nor de Cuvinte**. Va trebui să instalăm o altă bibliotecă pentru a reprezenta grafic norul de cuvinte din lista noastră de cuvinte cheie.\n"
    ],
    "metadata": {}
   },
@@ -408,7 +407,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Obiectul `WordCloud` este responsabil pentru preluarea fie a textului original, fie a unei liste pre-calculate de cuvinte cu frecvențele lor, și returnează o imagine, care poate fi apoi afișată folosind `matplotlib`:\n"
+    "Obiectul `WordCloud` este responsabil pentru preluarea fie a textului original, fie a unei liste pre-calculate de cuvinte cu frecvențele lor și returnează o imagine, care poate fi apoi afișată folosind `matplotlib`:\n"
    ],
    "metadata": {}
   },
@@ -441,7 +440,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Putem, de asemenea, să introducem textul original în `WordCloud` - să vedem dacă putem obține un rezultat similar:\n"
+    "Putem, de asemenea, să transmitem textul original către `WordCloud` - să vedem dacă putem obține un rezultat similar:\n"
    ],
    "metadata": {}
   },
@@ -490,11 +489,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Poți observa că acum norul de cuvinte arată mai impresionant, dar conține și mult zgomot (de exemplu, cuvinte nerelevante precum `Retrieved on`). De asemenea, obținem mai puține cuvinte-cheie formate din două cuvinte, cum ar fi *data scientist* sau *computer science*. Acest lucru se întâmplă deoarece algoritmul RAKE face o treabă mult mai bună în selectarea cuvintelor-cheie relevante din text. Acest exemplu ilustrează importanța pre-procesării și curățării datelor, deoarece o imagine clară la final ne va permite să luăm decizii mai bune.\n",
+    "Puteți vedea că norul de cuvinte arată acum mai impresionant, dar conține și mult zgomot (de exemplu, cuvinte fără legătură precum `Retrieved on`). De asemenea, obținem mai puține cuvinte cheie formate din două cuvinte, cum ar fi *data scientist* sau *computer science*. Acest lucru se datorează faptului că algoritmul RAKE face o treabă mult mai bună la selectarea cuvintelor cheie bune din text. Acest exemplu ilustrează importanța preprocesării și curățării datelor, deoarece o imagine clară la final ne va permite să luăm decizii mai bune.\n",
     "\n",
-    "În acest exercițiu am parcurs un proces simplu de extragere a unor informații dintr-un text de pe Wikipedia, sub forma cuvintelor-cheie și a unui nor de cuvinte. Acest exemplu este destul de simplu, dar demonstrează bine toți pașii tipici pe care un data scientist îi urmează atunci când lucrează cu date, începând de la achiziția datelor până la vizualizare.\n",
+    "În acest exercițiu am parcurs un proces simplu de extragere a unui anumit sens din textul Wikipedia, sub forma unor cuvinte cheie și a unui nor de cuvinte. Acest exemplu este destul de simplu, dar demonstrează bine toți pașii tipici pe care un data scientist îi va urma atunci când lucrează cu date, începând de la achiziția datelor și până la vizualizare.\n",
     "\n",
-    "În cadrul cursului nostru vom discuta în detaliu toți acești pași.\n"
+    "În cursul nostru vom discuta toți acești pași în detaliu.\n"
    ],
    "metadata": {}
   },
@@ -502,7 +501,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să aveți în vedere că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Declinare de responsabilitate**:\nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim să asigurăm acuratețea, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă trebuie considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot rezulta din utilizarea acestei traduceri.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -526,12 +525,6 @@
   },
   "interpreter": {
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  },
-  "coopTranslator": {
-   "original_hash": "dbdf34788efab64e5d817f1df38965a8",
-   "translation_date": "2025-09-02T10:19:08+00:00",
-   "source_file": "1-Introduction/01-defining-data-science/solution/notebook.ipynb",
-   "language_code": "ro"
   }
  },
  "nbformat": 4,
diff --git a/translations/ro/1-Introduction/04-stats-and-probability/README.md b/translations/ro/1-Introduction/04-stats-and-probability/README.md
index 69e8f8db..ef0c1684 100644
--- a/translations/ro/1-Introduction/04-stats-and-probability/README.md
+++ b/translations/ro/1-Introduction/04-stats-and-probability/README.md
@@ -6,7 +6,7 @@
 
 Teoria statisticii și probabilității sunt două domenii strâns legate ale matematicii, extrem de relevante pentru știința datelor. Este posibil să lucrăm cu date fără o cunoaștere profundă a matematicii, dar este totuși mai bine să știm cel puțin câteva concepte de bază. Aici vom prezenta o scurtă introducere care te va ajuta să începi.
 
-[![Video introductiv](../../../../translated_images/ro/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw)
+[![Video introductiv](../../../../translated_images/ro/video-prob-and-stats.e4282e5efa2f2543.webp)](https://youtu.be/Z5Zy85g4Yjw)
 
 ## [Chestionar înainte de lecție](https://ff-quizzes.netlify.app/en/ds/quiz/6)
 
@@ -30,7 +30,7 @@ Este mai dificil să descriem distribuția probabilității unei variabile conti
 
 Putem vorbi doar despre probabilitatea ca o variabilă să se încadreze într-un anumit interval de valori, de exemplu P(t<sub>1</sub>≤X<t<sub>2</sub>). În acest caz, distribuția probabilității este descrisă printr-o **funcție de densitate a probabilității** p(x), astfel încât
 
-![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/ro/probability-density.a8aad29f17a14afb519b407c7b6edeb9f3f9aa5f69c9e6d9445f604e5f8a2bf7.png)
+![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/ro/probability-density.a8aad29f17a14afb.webp)
 
 Un analog continuu al distribuției uniforme se numește **uniform continuu**, care este definit pe un interval finit. Probabilitatea ca valoarea X să se încadreze într-un interval de lungime l este proporțională cu l și crește până la 1.
 
@@ -73,11 +73,11 @@ Când analizăm date din viața reală, acestea nu sunt adesea variabile aleatoa
 
 Iată box plot-ul care arată media, mediana și quartilele pentru datele noastre:
 
-![Box Plot Greutate](../../../../translated_images/ro/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png)
+![Box Plot Greutate](../../../../translated_images/ro/weight-boxplot.1dbab1c03af26f8a.webp)
 
 Deoarece datele noastre conțin informații despre diferite **roluri** ale jucătorilor, putem face și un box plot pe roluri - acest lucru ne va permite să înțelegem cum diferă valorile parametrilor între roluri. De această dată vom considera înălțimea:
 
-![Box plot pe roluri](../../../../translated_images/ro/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png)
+![Box plot pe roluri](../../../../translated_images/ro/boxplot_byrole.036b27a1c3f52d42.webp)
 
 Acest diagramă sugerează că, în medie, înălțimea jucătorilor de primă bază este mai mare decât înălțimea jucătorilor de a doua bază. Mai târziu în această lecție vom învăța cum putem testa această ipoteză mai formal și cum să demonstrăm că datele noastre sunt semnificative din punct de vedere statistic pentru a arăta acest lucru.
 
@@ -85,7 +85,7 @@ Acest diagramă sugerează că, în medie, înălțimea jucătorilor de primă b
 
 Pentru a vedea ce distribuție au datele noastre, putem trasa un grafic numit **histogramă**. Axa X ar conține un număr de intervale de greutate diferite (așa-numitele **bin-uri**), iar axa verticală ar arăta numărul de ori când proba variabilei aleatoare a fost în acel interval.
 
-![Histogramă a datelor din lumea reală](../../../../translated_images/ro/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png)
+![Histogramă a datelor din lumea reală](../../../../translated_images/ro/weight-histogram.bfd00caf7fc30b14.webp)
 
 Din această histogramă poți vedea că toate valorile sunt centrate în jurul unei anumite greutăți medii, iar cu cât ne îndepărtăm de acea greutate - cu atât mai puține greutăți de acea valoare sunt întâlnite. Adică, este foarte improbabil ca greutatea unui jucător de baseball să fie foarte diferită de greutatea medie. Varianța greutăților arată măsura în care greutățile sunt susceptibile să difere de medie.
 
@@ -102,7 +102,7 @@ samples = np.random.normal(mean,std,1000)
 
 Dacă trasăm histograma probelor generate, vom vedea o imagine foarte similară cu cea prezentată mai sus. Și dacă mărim numărul de probe și numărul de bin-uri, putem genera o imagine a unei distribuții normale care este mai aproape de ideal:
 
-![Distribuție normală cu medie=0 și abatere standard=1](../../../../translated_images/ro/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png)
+![Distribuție normală cu medie=0 și abatere standard=1](../../../../translated_images/ro/normal-histogram.dfae0d67c202137d.webp)
 
 *Distribuție normală cu medie=0 și abatere standard=1*
 
@@ -224,7 +224,7 @@ array([[1.        , 0.52959196],
 
 În cazul nostru, valoarea 0.53 indică faptul că există o anumită corelație între greutatea și înălțimea unei persoane. Putem, de asemenea, să realizăm un grafic scatter al unei valori în raport cu cealaltă pentru a vedea relația vizual:
 
-![Relația dintre greutate și înălțime](../../../../translated_images/ro/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png)
+![Relația dintre greutate și înălțime](../../../../translated_images/ro/weight-height-relationship.3f06bde4ca2aba99.webp)
 
 > Mai multe exemple de corelație și covarianță pot fi găsite în [notebook-ul asociat](notebook.ipynb).
 
diff --git a/translations/ro/1-Introduction/README.md b/translations/ro/1-Introduction/README.md
index 434ee7ca..cf3a9c2e 100644
--- a/translations/ro/1-Introduction/README.md
+++ b/translations/ro/1-Introduction/README.md
@@ -1,6 +1,6 @@
 # Introducere în Știința Datelor
 
-![date în acțiune](../../../translated_images/ro/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)
+![date în acțiune](../../../translated_images/ro/data.48e22bb7617d8d92.webp)
 > Fotografie de <a href="https://unsplash.com/@dawson2406?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Stephen Dawson</a> pe <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 În aceste lecții, vei descoperi cum este definită Știința Datelor și vei învăța despre considerațiile etice pe care trebuie să le ia în calcul un specialist în date. De asemenea, vei învăța cum sunt definite datele și vei afla câte ceva despre statistică și probabilitate, domeniile academice de bază ale Științei Datelor.
diff --git a/translations/ro/2-Working-With-Data/07-python/README.md b/translations/ro/2-Working-With-Data/07-python/README.md
index f6a305a8..8ed6a37e 100644
--- a/translations/ro/2-Working-With-Data/07-python/README.md
+++ b/translations/ro/2-Working-With-Data/07-python/README.md
@@ -4,7 +4,7 @@
 | :-------------------------------------------------------------------------------------------------------: |
 |                 Lucrul cu Python - _Sketchnote de [@nitya](https://twitter.com/nitya)_                   |
 
-[![Video Introductiv](../../../../translated_images/ro/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y)
+[![Video Introductiv](../../../../translated_images/ro/video-ds-python.245247dc811db8e4.webp)](https://youtu.be/dZjWOGbsN4Y)
 
 Deși bazele de date oferă modalități foarte eficiente de stocare a datelor și de interogare a acestora folosind limbaje de interogare, cea mai flexibilă metodă de procesare a datelor este scrierea propriului program pentru manipularea datelor. În multe cazuri, o interogare SQL ar fi o metodă mai eficientă. Totuși, în unele situații, când este necesară o procesare mai complexă a datelor, aceasta nu poate fi realizată ușor folosind SQL. 
 Procesarea datelor poate fi programată în orice limbaj de programare, dar există anumite limbaje care sunt mai potrivite pentru lucrul cu date. De obicei, oamenii de știință în domeniul datelor preferă unul dintre următoarele limbaje:
@@ -66,7 +66,7 @@ print(f"Length of index is {len(idx)}")
 items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)
 items_sold.plot()
 ```
-![Grafic Serii Temporale](../../../../translated_images/ro/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png)
+![Grafic Serii Temporale](../../../../translated_images/ro/timeseries-1.80de678ab1cf727e.webp)
 
 Acum să presupunem că în fiecare săptămână organizăm o petrecere pentru prieteni și luăm 10 pachete suplimentare de înghețată pentru petrecere. Putem crea un alt series, indexat pe săptămână, pentru a demonstra acest lucru:
 ```python
@@ -77,7 +77,7 @@ Când adunăm cele două series, obținem numărul total:
 total_items = items_sold.add(additional_items,fill_value=0)
 total_items.plot()
 ```
-![Grafic Serii Temporale](../../../../translated_images/ro/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png)
+![Grafic Serii Temporale](../../../../translated_images/ro/timeseries-2.aae51d575c55181c.webp)
 
 > **Notă** că nu folosim sintaxa simplă `total_items+additional_items`. Dacă am fi făcut acest lucru, am fi obținut multe valori `NaN` (*Not a Number*) în series-ul rezultat. Acest lucru se întâmplă deoarece lipsesc valori pentru unele puncte de index în series-ul `additional_items`, iar adunarea `NaN` cu orice altceva rezultă în `NaN`. Astfel, trebuie să specificăm parametrul `fill_value` în timpul adunării.
 
@@ -86,7 +86,7 @@ Cu serii temporale, putem **resampla** series-ul cu intervale de timp diferite.
 monthly = total_items.resample("1M").mean()
 ax = monthly.plot(kind='bar')
 ```
-![Medii Lunare Serii Temporale](../../../../translated_images/ro/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png)
+![Medii Lunare Serii Temporale](../../../../translated_images/ro/timeseries-3.f3147cbc8c624881.webp)
 
 ### DataFrame
 
@@ -212,7 +212,7 @@ Prima problemă pe care ne vom concentra este modelarea răspândirii epidemiei
 
 Deoarece dorim să demonstrăm cum să lucrăm cu datele, vă invităm să deschideți [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) și să îl citiți de la început până la sfârșit. Puteți, de asemenea, să executați celulele și să rezolvați unele provocări pe care le-am lăsat pentru voi la final.
 
-![COVID Spread](../../../../translated_images/ro/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png)
+![COVID Spread](../../../../translated_images/ro/covidspread.f3d131c4f1d260ab.webp)
 
 > Dacă nu știți cum să rulați codul în Jupyter Notebook, consultați [acest articol](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
 
@@ -234,7 +234,7 @@ Un exemplu complet de analiză a acestui set de date folosind serviciul cognitiv
 
 Deschideți [`notebook-papers.ipynb`](notebook-papers.ipynb) și citiți-l de la început până la sfârșit. Puteți, de asemenea, să executați celulele și să rezolvați unele provocări pe care le-am lăsat pentru voi la final.
 
-![Covid Medical Treatment](../../../../translated_images/ro/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png)
+![Covid Medical Treatment](../../../../translated_images/ro/covidtreat.b2ba59f57ca45fbc.webp)
 
 ## Procesarea Datelor din Imagini
 
diff --git a/translations/ro/2-Working-With-Data/07-python/notebook-covidspread.ipynb b/translations/ro/2-Working-With-Data/07-python/notebook-covidspread.ipynb
index ce7b2553..de775329 100644
--- a/translations/ro/2-Working-With-Data/07-python/notebook-covidspread.ipynb
+++ b/translations/ro/2-Working-With-Data/07-python/notebook-covidspread.ipynb
@@ -4,11 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Estimarea pandemiei de COVID-19\n",
+    "# Estimarea pandemiei COVID-19\n",
     "\n",
     "## Încărcarea datelor\n",
     "\n",
-    "Vom utiliza date despre persoanele infectate cu COVID-19, furnizate de [Centrul pentru Știința și Ingineria Sistemelor](https://systems.jhu.edu/) (CSSE) de la [Universitatea Johns Hopkins](https://jhu.edu/). Setul de date este disponibil în [acest depozit GitHub](https://github.com/CSSEGISandData/COVID-19).\n"
+    "Vom folosi date despre persoanele infectate cu COVID-19, furnizate de [Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) de la [Johns Hopkins University](https://jhu.edu/). Setul de date este disponibil în [acest depozit GitHub](https://github.com/CSSEGISandData/COVID-19).\n"
    ]
   },
   {
@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Putem încărca cele mai recente date direct de pe GitHub folosind `pd.read_csv`. Dacă, dintr-un motiv oarecare, datele nu sunt disponibile, poți oricând să folosești copia disponibilă local în folderul `data` - doar decomentează linia de mai jos care definește `base_url`:\n"
+    "Putem încărca cele mai recente date direct de pe GitHub folosind `pd.read_csv`. Dacă dintr-un motiv oarecare datele nu sunt disponibile, poți întotdeauna să folosești copia disponibilă local în dosarul `data` - doar dezactivează comentariul de la linia de mai jos care definește `base_url`:\n"
    ]
   },
   {
@@ -47,7 +47,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Să încărcăm acum datele pentru persoanele infectate și să vedem cum arată datele:\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -280,9 +282,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Înțelegerea Datelor\n",
+    "## Înțelegerea datelor\n",
     "\n",
-    "Din tabelul de mai sus, rolul coloanei provincei nu este clar. Să vedem care sunt valorile diferite prezente în coloana `Province/State`:\n"
+    "Din tabelul de mai sus, rolul coloanei provincie nu este clar. Să vedem valorile diferite care sunt prezente în coloana `Province/State`:\n"
    ]
   },
   {
@@ -319,7 +321,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Din nume putem deduce că țări precum Australia și China au o descompunere mai detaliată pe provincii. Să căutăm informații despre China pentru a vedea exemplul:\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -1317,9 +1321,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Pre-procesarea datelor\n",
+    "## Pre-procesarea datelor \n",
     "\n",
-    "Nu ne interesează să împărțim țările în teritorii mai mici, așa că mai întâi vom elimina această împărțire și vom adăuga informațiile pentru toate teritoriile împreună, pentru a obține datele pentru întreaga țară. Acest lucru poate fi realizat folosind `groupby`:\n"
+    "Nu suntem interesați să detaliem țările în teritorii suplimentare, așadar mai întâi vom elimina această divizare și vom adăuga informațiile pentru toate teritoriile împreună, pentru a obține date pentru întreaga țară. Acest lucru se poate face folosind `groupby`:\n"
    ]
   },
   {
@@ -1574,7 +1578,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Puteți observa că, datorită utilizării `groupby`, toate DataFrame-urile sunt acum indexate după Țară/Regiune. Astfel, putem accesa datele pentru o anumită țară folosind `.loc`:\n"
+    "Se poate observa că datorită utilizării `groupby`, toate DataFrame-urile sunt acum indexate după Țară/Regiune. Astfel, putem accesa datele pentru o țară specifică folosind `.loc`:|\n"
    ]
   },
   {
@@ -1583,20 +1587,19 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
-    "infected.loc['US'][2:].plot()\n",
-    "recovered.loc['US'][2:].plot()\n",
+    "infected.loc['US'][3:].plot()\n",
+    "recovered.loc['US'][3:].plot()\n",
     "plt.show()"
    ]
   },
@@ -1604,7 +1607,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Notă** cum folosim `[2:]` pentru a elimina primele două elemente ale unei secvențe care conțin geolocația unei țări. Putem, de asemenea, să eliminăm complet acele două coloane:\n"
+    "> **Notă** cum folosim `[3:]` pentru a elimina primele trei elemente ale unei secvențe care conțin metadate non-date (coloanele Provincia/Stat și geolocație). De asemenea, putem elimina complet acele trei coloane:\n"
    ]
   },
   {
@@ -1613,18 +1616,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected.drop(columns=['Lat','Long'],inplace=True)\n",
-    "recovered.drop(columns=['Lat','Long'],inplace=True)\n",
-    "deaths.drop(columns=['Lat','Long'],inplace=True)"
+    "infected.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "recovered.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "deaths.drop(columns=['Lat','Long','Province/State'],inplace=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Investigarea Datelor\n",
+    "## Investigarea datelor\n",
     "\n",
-    "Să trecem acum la investigarea unei țări specifice. Să creăm un cadru care conține datele despre infecții indexate după dată:\n"
+    "Să trecem acum la investigarea unui anumită țară. Să creăm un cadru care conține datele despre infecții indexate după dată:\n"
    ]
   },
   {
@@ -1754,7 +1757,7 @@
    ],
    "source": [
     "def mkframe(country):\n",
-    "    df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n",
+    "    df = pd.DataFrame({ 'infected' : infected.loc[country],\n",
     "                        'recovered' : recovered.loc[country],\n",
     "                        'deaths' : deaths.loc[country]})\n",
     "    df.index = pd.to_datetime(df.index)\n",
@@ -1790,7 +1793,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Acum să calculăm numărul de persoane nou infectate în fiecare zi. Acest lucru ne va permite să vedem viteza cu care progresează pandemia. Cea mai simplă metodă de a face acest lucru este să folosim `diff`:\n"
+    "Acum să calculăm numărul de persoane nou infectate în fiecare zi. Acest lucru ne va permite să vedem viteza cu care progresează pandemia. Cea mai simplă metodă este să folosim `diff`:\n"
    ]
   },
   {
@@ -1820,7 +1823,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Putem observa fluctuații mari în date. Să analizăm mai îndeaproape una dintre luni:\n"
+    "Putem observa fluctuații mari în date. Să privim mai atent una dintre luni:\n"
    ]
   },
   {
@@ -1849,7 +1852,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Se pare clar că există fluctuații săptămânale în date. Deoarece dorim să putem observa tendințele, are sens să netezim curba prin calcularea mediei mobile (adică, pentru fiecare zi vom calcula valoarea medie a zilelor anterioare):\n"
+    "Este clar că există fluctuații săptămânale în date. Deoarece dorim să putem observa tendințele, are sens să netezim curba calculând media mobilă (adică pentru fiecare zi vom calcula valoarea medie a zilelor anterioare):\n"
    ]
   },
   {
@@ -1879,7 +1882,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pentru a putea compara mai multe țări, s-ar putea să dorim să luăm în considerare populația țării și să comparăm procentul de indivizi infectați în raport cu populația țării. Pentru a obține populația țării, să încărcăm setul de date al țărilor:\n"
+    "Pentru a putea compara mai multe țări, am putea dori să luăm în considerare populația țării și să comparăm procentajul persoanelor infectate în raport cu populația țării. Pentru a obține populația țării, să încărcăm setul de date al țărilor:\n"
    ]
   },
   {
@@ -2150,7 +2153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Deoarece acest set de date conține informații atât despre țări, cât și despre provincii, pentru a obține populația întregii țări trebuie să fim puțin mai ingenioși:\n"
+    "Deoarece acest set de date conține informații atât despre țări, cât și despre provincii, pentru a obține populația întregii țări trebuie să fim puțin isteți:\n"
    ]
   },
   {
@@ -2258,14 +2261,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Calcularea $R_t$\n",
+    "## Calcularea lui $R_t$\n",
     "\n",
-    "Pentru a evalua cât de contagioasă este boala, analizăm **numărul de reproducere de bază** $R_0$, care indică numărul de persoane pe care o persoană infectată le va infecta în continuare. Când $R_0$ este mai mare decât 1, epidemia are șanse să se răspândească.\n",
+    "Pentru a vedea cât de contagioasă este boala, ne uităm la **numărul de reproducere de bază** $R_0$, care indică numărul de persoane pe care o persoană infectată le-ar infecta mai departe. Când $R_0$ este mai mare de 1, epidemia este probabil să se răspândească.\n",
     "\n",
-    "$R_0$ este o caracteristică a bolii în sine și nu ia în considerare măsurile de protecție pe care oamenii le pot adopta pentru a încetini pandemia. Pe parcursul evoluției pandemiei, putem estima numărul de reproducere $R_t$ la orice moment dat $t$. S-a demonstrat că acest număr poate fi aproximat luând o fereastră de 8 zile și calculând $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
-    "unde $I_t$ reprezintă numărul de persoane nou infectate în ziua $t$.\n",
+    "$R_0$ este o proprietate a bolii în sine și nu ia în considerare unele măsuri de protecție pe care oamenii le pot lua pentru a încetini pandemia. Pe măsură ce pandemia progresează, putem estima numărul de reproducere $R_t$ la orice moment dat $t$. A fost demonstrat că acest număr poate fi estimat aproximativ prin luarea unei ferestre de 8 zile și calcularea $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
+    "unde $I_t$ este numărul de indivizi nou infectați în ziua $t$.\n",
     "\n",
-    "Haideți să calculăm $R_t$ pentru datele pandemiei noastre. Pentru a face acest lucru, vom lua o fereastră mobilă de 8 valori `ninfected` și vom aplica funcția pentru a calcula raportul de mai sus:\n"
+    "Să calculăm $R_t$ pentru datele noastre despre pandemie. Pentru a face acest lucru, vom lua o fereastră glisantă (rolling window) de 8 valori `ninfected` și vom aplica funcția pentru a calcula raportul de mai sus:\n"
    ]
   },
   {
@@ -2295,9 +2298,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Poți observa că există unele goluri în grafic. Acestea pot fi cauzate fie de `NaN`, fie de valori `inf` prezente în setul de date. Valorile `inf` pot apărea din cauza împărțirii la 0, iar `NaN` poate indica date lipsă sau absența datelor necesare pentru a calcula rezultatul (cum ar fi la începutul cadrului nostru, unde fereastra mobilă cu lățimea 8 nu este încă disponibilă). Pentru a face graficul mai plăcut, trebuie să completăm aceste valori folosind funcțiile `replace` și `fillna`.\n",
+    "Puteți observa că există unele goluri în grafic. Acestea pot fi cauzate fie de `NaN`, fie de prezența valorilor `inf` în setul de date. `inf` poate fi cauzat de împărțirea la 0, iar `NaN` poate indica date lipsă sau date indisponibile pentru a calcula rezultatul (precum la începutul cadranului nostru, unde fereastra mobilă cu lățimea 8 încă nu este disponibilă). Pentru a face graficul mai plăcut, trebuie să completăm acele valori folosind funcțiile `replace` și `fillna`.\n",
     "\n",
-    "Să analizăm mai departe începutul pandemiei. Vom limita, de asemenea, valorile de pe axa y pentru a afișa doar valori sub 6, astfel încât să vedem mai clar, și vom trasa o linie orizontală la 1.\n"
+    "Să analizăm mai departe începutul pandemiei. De asemenea, vom limita valorile de pe axa y pentru a afișa doar valorile sub 6, pentru a vedea mai bine, și vom desena o linie orizontală la 1.\n"
    ]
   },
   {
@@ -2327,7 +2330,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Un alt indicator interesant al pandemiei este **derivata**, sau **diferența zilnică** în cazurile noi. Aceasta ne permite să vedem clar când pandemia este în creștere sau în scădere.\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -2355,7 +2360,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Având în vedere faptul că există multe fluctuații în date cauzate de raportare, are sens să netezim curba aplicând o medie mobilă pentru a obține o imagine de ansamblu. Să ne concentrăm din nou pe primele luni ale pandemiei:\n"
+    "Având în vedere că există multe fluctuații în date cauzate de raportare, este logic să netezim curba prin aplicarea unei medii mobile pentru a obține o imagine de ansamblu. Să ne concentrăm din nou asupra primelor luni ale pandemiei:\n"
    ]
   },
   {
@@ -2387,12 +2392,12 @@
    "source": [
     "## Provocare\n",
     "\n",
-    "Acum este momentul să te joci mai mult cu codul și datele! Iată câteva sugestii pe care le poți experimenta:\n",
-    "* Analizează răspândirea pandemiei în diferite țări.\n",
-    "* Plotează grafice $R_t$ pentru mai multe țări pe un singur grafic pentru comparație sau creează mai multe grafice unul lângă altul.\n",
-    "* Observă cum se corelează numărul de decese și recuperări cu numărul de cazuri infectate.\n",
-    "* Încearcă să descoperi cât durează, în medie, o boală, corelând vizual rata de infectare cu rata de decese și căutând anomalii. Este posibil să fie nevoie să analizezi diferite țări pentru a afla acest lucru.\n",
-    "* Calculează rata de fatalitate și cum se modifică aceasta în timp. Poate fi util să iei în considerare durata bolii în zile pentru a deplasa o serie temporală înainte de a face calculele.\n"
+    "Acum este timpul să te joci mai mult cu codul și datele! Iată câteva sugestii cu care poți experimenta:\n",
+    "* Vezi răspândirea pandemiei în diferite țări.\n",
+    "* Realizează grafice $R_t$ pentru mai multe țări pe un singur grafic pentru comparație sau fă mai multe grafice alăturate.\n",
+    "* Vezi cum se corelează numărul de decese și recuperări cu numărul de cazuri infectate.\n",
+    "* Încearcă să afli cât durează o boală tipică prin corelarea vizuală a ratei de infectare și a ratei de decese, căutând unele anomalii. Este posibil să fie nevoie să te uiți la diferite țări pentru a afla asta.\n",
+    "* Calculează rata fatalității și cum se schimbă în timp. Poate vrei să ții cont de durata bolii în zile pentru a deplasa o serie temporală înainte de a face calculele.\n"
    ]
   },
   {
@@ -2401,9 +2406,9 @@
    "source": [
     "## Referințe\n",
     "\n",
-    "Puteți consulta studii suplimentare despre răspândirea epidemiei de COVID în următoarele publicații:\n",
-    "* [Modelul SIR Glisant pentru Estimarea Rt în timpul Pandemiei de COVID](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/), articol pe blog de [Dmitry Soshnikov](http://soshnikov.com)\n",
-    "* T.Petrova, D.Soshnikov, A.Grunin. [Estimarea Numărului de Reproducere Dependent de Timp pentru Epidemia Globală de COVID-19](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
+    "Puteți consulta studii suplimentare privind răspândirea epidemiei de COVID în următoarele publicații:\n",
+    "* [Modelul SIR glisant pentru estimarea Rt în timpul pandemiei COVID](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/), postare pe blog de [Dmitry Soshnikov](http://soshnikov.com)\n",
+    "* T.Petrova, D.Soshnikov, A.Grunin. [Estimarea numărului de reproducere dependent de timp pentru focarul global COVID-19](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
     "* [Codul pentru lucrarea de mai sus pe GitHub](https://github.com/shwars/SlidingSIR)\n"
    ]
   },
@@ -2418,7 +2423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus utilizând serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Declinare a responsabilității**:  \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim să asigurăm acuratețea, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original, în limba sa nativă, trebuie considerat sursa autorizată. Pentru informații critice, se recomandă o traducere profesională realizată de un specialist uman. Nu ne asumăm responsabilitatea pentru eventualele neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -2442,13 +2447,7 @@
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
   },
-  "orig_nbformat": 4,
-  "coopTranslator": {
-   "original_hash": "68a0ada1dae45c7d1d3519aa0a6d1a0f",
-   "translation_date": "2025-09-02T06:35:55+00:00",
-   "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
-   "language_code": "ro"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/translations/ro/2-Working-With-Data/README.md b/translations/ro/2-Working-With-Data/README.md
index 94f96773..089716c7 100644
--- a/translations/ro/2-Working-With-Data/README.md
+++ b/translations/ro/2-Working-With-Data/README.md
@@ -1,6 +1,6 @@
 # Lucrul cu Date
 
-![data love](../../../translated_images/ro/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)  
+![data love](../../../translated_images/ro/data-love.a22ef29e6742c852.webp)  
 > Fotografie de <a href="https://unsplash.com/@swimstaralex?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexander Sinn</a> pe <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 În aceste lecții, vei învăța câteva dintre modurile în care datele pot fi gestionate, manipulate și utilizate în aplicații. Vei învăța despre bazele de date relaționale și non-relaționale și despre cum pot fi stocate datele în acestea. De asemenea, vei învăța elementele de bază ale lucrului cu Python pentru a gestiona datele și vei descoperi câteva dintre numeroasele moduri în care poți folosi Python pentru a gestiona și analiza datele.
diff --git a/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md
index a19ad6cb..d122aabd 100644
--- a/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md
+++ b/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md
@@ -42,7 +42,7 @@ Creează o diagramă scatter de bază pentru a arăta relația dintre prețul pe
 ```python
 sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);
 ```
-![scatterplot 1](../../../../translated_images/ro/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png)
+![scatterplot 1](../../../../translated_images/ro/scatter1.5e1aa5fd6706c5d1.webp)
 
 Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentru a arăta cum evoluează prețul de-a lungul anilor. Poți face acest lucru adăugând un parametru 'hue' pentru a evidenția schimbările anuale:
 
@@ -51,7 +51,7 @@ Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentr
 ```python
 sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5);
 ```
-![scatterplot 2](../../../../translated_images/ro/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png)
+![scatterplot 2](../../../../translated_images/ro/scatter2.c0041a58621ca702.webp)
 
 Cu această schimbare de culori, poți observa clar o progresie puternică de-a lungul anilor în ceea ce privește prețul pe liră al mierii. De fapt, dacă verifici un set de date eșantion (de exemplu, statul Arizona), poți observa un model de creștere a prețului an de an, cu câteva excepții:
 
@@ -80,7 +80,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec
 ```
 Poți observa cum dimensiunea punctelor crește treptat.
 
-![scatterplot 3](../../../../translated_images/ro/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png)
+![scatterplot 3](../../../../translated_images/ro/scatter3.3c160a3d1dcb36b3.webp)
 
 Este acesta un caz simplu de cerere și ofertă? Din cauza unor factori precum schimbările climatice și colapsul coloniilor, există mai puțină miere disponibilă pentru cumpărare an de an, ceea ce duce la creșterea prețului?
 
@@ -95,7 +95,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey);
 ```
 Răspuns: Da, cu câteva excepții în jurul anului 2003:
 
-![line chart 1](../../../../translated_images/ro/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png)
+![line chart 1](../../../../translated_images/ro/line1.f36eb465229a3b1f.webp)
 
 ✅ Deoarece Seaborn agregă datele într-o singură linie, afișează "măsurătorile multiple pentru fiecare valoare x prin reprezentarea mediei și a intervalului de încredere de 95% în jurul mediei". [Sursa](https://seaborn.pydata.org/tutorial/relational.html). Acest comportament consumator de timp poate fi dezactivat adăugând `ci=None`.
 
@@ -105,7 +105,7 @@ Răspuns: Da, cu câteva excepții în jurul anului 2003:
 sns.relplot(x="year", y="totalprod", kind="line", data=honey);
 ```
 
-![line chart 2](../../../../translated_images/ro/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png)
+![line chart 2](../../../../translated_images/ro/line2.a5b3493dc01058af.webp)
 
 Răspuns: Nu chiar. Dacă te uiți la producția totală, aceasta pare să fi crescut în acel an, deși, în general, cantitatea de miere produsă este în scădere în acești ani.
 
@@ -130,7 +130,7 @@ sns.relplot(
 ```
 În această vizualizare, poți compara producția per colonie și numărul de colonii an de an, alăturat, cu o împărțire pe 3 coloane:
 
-![facet grid](../../../../translated_images/ro/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png)
+![facet grid](../../../../translated_images/ro/facet.6a34851dcd540050.webp)
 
 Pentru acest set de date, nimic nu iese în evidență în mod special în ceea ce privește numărul de colonii și producția lor, an de an și stat de stat. Există o altă modalitate de a analiza corelația dintre aceste două variabile?
 
@@ -153,7 +153,7 @@ sns.despine(right=False)
 plt.ylabel('colony yield')
 ax.figure.legend();
 ```
-![superimposed plots](../../../../translated_images/ro/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png)
+![superimposed plots](../../../../translated_images/ro/dual-line.a4c28ce659603fab.webp)
 
 Deși nimic nu sare în ochi în jurul anului 2003, acest grafic ne permite să încheiem lecția pe o notă mai optimistă: deși numărul coloniilor este în scădere, acesta pare să se stabilizeze, chiar dacă producția per colonie este în scădere.
 
diff --git a/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md
index 3e11deef..61ace110 100644
--- a/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md
+++ b/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md
@@ -57,7 +57,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 ```  
 Aici, instalezi pachetul `ggplot2` și apoi îl importi în spațiul de lucru folosind comanda `library("ggplot2")`. Pentru a crea orice grafic în ggplot, se folosește funcția `ggplot()` și specifici setul de date, variabilele x și y ca atribute. În acest caz, folosim funcția `geom_line()` deoarece dorim să creăm un grafic liniar.
 
-![MaxWingspan-lineplot](../../../../../translated_images/ro/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png)
+![MaxWingspan-lineplot](../../../../../translated_images/ro/MaxWingspan-lineplot.b12169f99d26fdd2.webp)
 
 Ce observi imediat? Pare să existe cel puțin un outlier - ce anvergură impresionantă! O anvergură de peste 2000 de centimetri înseamnă mai mult de 20 de metri - sunt Pterodactili care zboară prin Minnesota? Să investigăm.
 
@@ -75,7 +75,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 ```  
 Specificăm unghiul în `theme` și specificăm etichetele axelor x și y în `xlab()` și `ylab()` respectiv. Funcția `ggtitle()` oferă un nume graficului.
 
-![MaxWingspan-lineplot-improved](../../../../../translated_images/ro/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png)
+![MaxWingspan-lineplot-improved](../../../../../translated_images/ro/MaxWingspan-lineplot-improved.04b73b4d5a59552a.webp)
 
 Chiar și cu rotația etichetelor setată la 45 de grade, sunt prea multe pentru a fi citite. Să încercăm o strategie diferită: etichetează doar outlierii și setează etichetele în interiorul graficului. Poți folosi un grafic scatter pentru a face mai mult loc etichetării:
 
@@ -91,7 +91,7 @@ Ce se întâmplă aici? Ai folosit funcția `geom_point()` pentru a reprezenta p
 
 Ce descoperi?
 
-![MaxWingspan-scatterplot](../../../../../translated_images/ro/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png)
+![MaxWingspan-scatterplot](../../../../../translated_images/ro/MaxWingspan-scatterplot.60dc9e0e19d32700.webp)
 
 ## Filtrează datele tale
 
@@ -110,7 +110,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) +
 ```  
 Am creat un nou cadru de date `birds_filtered` și apoi am reprezentat un grafic scatter. Prin filtrarea outlierilor, datele tale sunt acum mai coerente și mai ușor de înțeles.
 
-![MaxWingspan-scatterplot-improved](../../../../../translated_images/ro/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png)
+![MaxWingspan-scatterplot-improved](../../../../../translated_images/ro/MaxWingspan-scatterplot-improved.7d0af81658c65f3e.webp)
 
 Acum că avem un set de date mai curat, cel puțin în ceea ce privește anvergura aripilor, să descoperim mai multe despre aceste păsări.
 
@@ -151,7 +151,7 @@ birds_filtered %>% group_by(Category) %>%
 ```  
 În următorul fragment, instalăm pachetele [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) și [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) pentru a ajuta la manipularea și gruparea datelor în vederea creării unui grafic cu bare stivuite. Mai întâi, grupăm datele după `Category` și apoi sumarizăm coloanele `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Apoi, reprezentăm graficul cu bare folosind pachetul `ggplot2` și specificăm culorile pentru diferitele categorii și etichetele.  
 
-![Stacked bar chart](../../../../../translated_images/ro/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png)
+![Stacked bar chart](../../../../../translated_images/ro/stacked-bar-chart.0c92264e89da7b39.webp)
 
 Acest grafic cu bare, totuși, este greu de citit deoarece există prea multe date negrupate. Trebuie să selectezi doar datele pe care vrei să le reprezinți grafic, așa că să analizăm lungimea păsărilor în funcție de categoria lor.
 
@@ -166,7 +166,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip()
 ```  
 Mai întâi numeri valorile unice din coloana `Category` și apoi le sortezi într-un nou cadru de date `birds_count`. Aceste date sortate sunt apoi factorizate la același nivel pentru a fi reprezentate grafic în mod ordonat. Folosind `ggplot2`, reprezinți grafic datele într-un grafic cu bare. Funcția `coord_flip()` afișează barele orizontal.  
 
-![category-length](../../../../../translated_images/ro/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png)
+![category-length](../../../../../translated_images/ro/category-length.7e34c296690e85d6.webp)
 
 Acest grafic cu bare oferă o vedere bună asupra numărului de păsări din fiecare categorie. Dintr-o privire, vezi că cel mai mare număr de păsări din această regiune sunt în categoria Rațe/Gâște/Păsări acvatice. Minnesota este 'țara celor 10.000 de lacuri', așa că acest lucru nu este surprinzător!
 
@@ -189,7 +189,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl
 ```  
 Grupăm datele `birds_filtered` după `Category` și apoi reprezentăm grafic un grafic cu bare.  
 
-![comparing data](../../../../../translated_images/ro/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png)
+![comparing data](../../../../../translated_images/ro/comparingdata.f486a450d61c7ca5.webp)
 
 Nimic nu este surprinzător aici: colibrii au cea mai mică LungimeMaximă comparativ cu Pelicanii sau Gâștele. Este bine când datele au sens logic!
 
@@ -201,7 +201,7 @@ ggplot(data=birds_grouped, aes(x=Category)) +
   geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+
   coord_flip()
 ```  
-![super-imposed values](../../../../../translated_images/ro/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png)
+![super-imposed values](../../../../../translated_images/ro/superimposed-values.5363f0705a1da416.webp)
 
 ## 🚀 Provocare
 
diff --git a/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md
index e6ce8529..4c6dca0c 100644
--- a/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md
+++ b/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md
@@ -36,7 +36,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) +
   geom_point() +
   ggtitle("Max Length per order") + coord_flip()
 ```
-![lungime maximă per ordin](../../../../../translated_images/ro/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png)
+![lungime maximă per ordin](../../../../../translated_images/ro/max-length-per-order.e5b283d952c78c12.webp)
 
 Acest grafic oferă o privire de ansamblu asupra distribuției generale a lungimii corpului per Ordin de păsări, dar nu este cea mai bună modalitate de a afișa distribuțiile reale. Această sarcină este de obicei realizată prin crearea unui histogramă.
 ## Lucrul cu histograme
@@ -47,7 +47,7 @@ Acest grafic oferă o privire de ansamblu asupra distribuției generale a lungim
 ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + 
   geom_histogram(bins=10)+ylab('Frequency')
 ```
-![distribuție pe întregul set de date](../../../../../translated_images/ro/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png)
+![distribuție pe întregul set de date](../../../../../translated_images/ro/distribution-over-the-entire-dataset.d22afd3fa96be854.webp)
 
 După cum poți vedea, majoritatea celor 400+ păsări din acest set de date se încadrează în intervalul de sub 2000 pentru masa corporală maximă. Obține mai multe informații despre date modificând parametrul `bins` la un număr mai mare, cum ar fi 30:
 
@@ -55,7 +55,7 @@ După cum poți vedea, majoritatea celor 400+ păsări din acest set de date se
 ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency')
 ```
 
-![distribuție-30bins](../../../../../translated_images/ro/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png)
+![distribuție-30bins](../../../../../translated_images/ro/distribution-30bins.6a3921ea7a421bf7.webp)
 
 Acest grafic arată distribuția într-un mod puțin mai detaliat. Un grafic mai puțin înclinat spre stânga ar putea fi creat asigurându-te că selectezi doar datele dintr-un anumit interval:
 
@@ -67,7 +67,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) +
   geom_histogram(bins=30)+ylab('Frequency')
 ```
 
-![histogramă filtrată](../../../../../translated_images/ro/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png)
+![histogramă filtrată](../../../../../translated_images/ro/filtered-histogram.6bf5d2bfd8253322.webp)
 
 ✅ Încearcă alte filtre și puncte de date. Pentru a vedea distribuția completă a datelor, elimină filtrul `['MaxBodyMass']` pentru a afișa distribuțiile etichetate.
 
@@ -81,7 +81,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) +
 ```
 Pare să existe o corelație așteptată între aceste două elemente de-a lungul unui ax previzibil, cu un punct de convergență deosebit de puternic:
 
-![grafic 2d](../../../../../translated_images/ro/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png)
+![grafic 2d](../../../../../translated_images/ro/2d-plot.c504786f439bd7eb.webp)
 
 Histogramele funcționează bine în mod implicit pentru date numerice. Ce se întâmplă dacă trebuie să vezi distribuții în funcție de date text? 
 ## Explorează setul de date pentru distribuții utilizând date text 
@@ -112,7 +112,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) +
   scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern"))
 ```
 
-![anvergură și status conservare](../../../../../translated_images/ro/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png)
+![anvergură și status conservare](../../../../../translated_images/ro/wingspan-conservation-collation.4024e9aa6910866a.webp)
 
 Nu pare să existe o corelație bună între anvergura minimă a aripilor și statusul de conservare. Testează alte elemente ale setului de date utilizând această metodă. Poți încerca diferite filtre. Găsești vreo corelație?
 
@@ -126,7 +126,7 @@ Să lucrăm acum cu grafice de densitate!
 ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + 
   geom_density()
 ```
-![grafic de densitate](../../../../../translated_images/ro/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png)
+![grafic de densitate](../../../../../translated_images/ro/density-plot.675ccf865b76c690.webp)
 
 Poți vedea cum graficul reflectă cel anterior pentru datele despre anvergura minimă a aripilor; este doar puțin mai lin. Dacă ai vrea să revizitezi acea linie „în trepte” a masei corporale maxime din al doilea grafic pe care l-ai construit, ai putea să o netezești foarte bine recreând-o utilizând această metodă:
 
@@ -134,7 +134,7 @@ Poți vedea cum graficul reflectă cel anterior pentru datele despre anvergura m
 ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
   geom_density()
 ```
-![densitate masă corporală](../../../../../translated_images/ro/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png)
+![densitate masă corporală](../../../../../translated_images/ro/bodymass-smooth.d31ce526d82b0a1f.webp)
 
 Dacă ai vrea o linie netedă, dar nu prea netedă, editează parametrul `adjust`: 
 
@@ -142,7 +142,7 @@ Dacă ai vrea o linie netedă, dar nu prea netedă, editează parametrul `adjust
 ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
   geom_density(adjust = 1/5)
 ```
-![masă corporală mai puțin netedă](../../../../../translated_images/ro/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png)
+![masă corporală mai puțin netedă](../../../../../translated_images/ro/less-smooth-bodymass.10f4db8b683cc17d.webp)
 
 ✅ Citește despre parametrii disponibili pentru acest tip de grafic și experimentează!
 
@@ -152,7 +152,7 @@ Acest tip de grafic oferă vizualizări explicative frumoase. Cu câteva linii d
 ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) +
   geom_density(alpha=0.5)
 ```
-![masă corporală per ordin](../../../../../translated_images/ro/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png)
+![masă corporală per ordin](../../../../../translated_images/ro/bodymass-per-order.9d2b065dd931b928.webp)
 
 ## 🚀 Provocare
 
diff --git a/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md
index 2c726554..b49bd021 100644
--- a/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md
+++ b/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md
@@ -84,7 +84,7 @@ pie(grouped$count,grouped$class, main="Edible?")
 ```  
 Voila, un grafic de tip plăcintă care arată proporțiile acestor date conform celor două clase de ciuperci. Este foarte important să obții ordinea corectă a etichetelor, mai ales aici, așa că asigură-te că verifici ordinea în care este construită matricea de etichete!
 
-![grafic plăcintă](../../../../../translated_images/ro/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png)
+![grafic plăcintă](../../../../../translated_images/ro/pie1-wb.685df063673751f4.webp)
 
 ## Gogoși!
 
@@ -118,7 +118,7 @@ library(webr)
 PieDonut(habitat, aes(habitat, count=count))
 ```  
 
-![grafic gogoașă](../../../../../translated_images/ro/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png)
+![grafic gogoașă](../../../../../translated_images/ro/donut-wb.34e6fb275da9d834.webp)
 
 Acest cod folosește două biblioteci - ggplot2 și webr. Folosind funcția PieDonut din biblioteca webr, putem crea ușor un grafic de tip gogoașă!
 
@@ -156,7 +156,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual
 
 Folosind un grafic de tip vafă, poți vedea clar proporțiile culorilor pălăriilor din acest set de date despre ciuperci. Interesant, există multe ciuperci cu pălării verzi!
 
-![grafic vafă](../../../../../translated_images/ro/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png)
+![grafic vafă](../../../../../translated_images/ro/waffle.aaa75c5337735a6e.webp)
 
 În această lecție, ai învățat trei modalități de a vizualiza proporțiile. Mai întâi, trebuie să grupezi datele în categorii și apoi să decizi care este cea mai bună modalitate de a afișa datele - plăcintă, gogoașă sau vafă. Toate sunt delicioase și oferă utilizatorului o imagine instantanee a unui set de date.
 
diff --git a/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md
index de17b893..2851eedb 100644
--- a/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md
+++ b/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md
@@ -42,7 +42,7 @@ library(ggplot2)
 ggplot(honey, aes(x = priceperlb, y = state)) +
   geom_point(colour = "blue")
 ```
-![scatterplot 1](../../../../../translated_images/ro/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png)
+![scatterplot 1](../../../../../translated_images/ro/scatter1.86b8900674d88b26.webp)
 
 Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentru a arăta cum evoluează prețul de-a lungul anilor. Poți face acest lucru adăugând un parametru 'scale_color_gradientn' pentru a arăta schimbarea, an după an:
 
@@ -52,7 +52,7 @@ Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentr
 ggplot(honey, aes(x = priceperlb, y = state, color=year)) +
   geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7))
 ```
-![scatterplot 2](../../../../../translated_images/ro/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png)
+![scatterplot 2](../../../../../translated_images/ro/scatter2.4d1cbc693bad20e2.webp)
 
 Cu această schimbare de schemă de culori, poți observa clar o progresie puternică de-a lungul anilor în ceea ce privește prețul mierii per kilogram. De fapt, dacă analizezi un set de date exemplu pentru a verifica (alege un stat, Arizona, de exemplu), poți observa un model de creștere a prețului an după an, cu câteva excepții:
 
@@ -83,7 +83,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) +
 ```
 Poți observa cum dimensiunea punctelor crește treptat.
 
-![scatterplot 3](../../../../../translated_images/ro/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png)
+![scatterplot 3](../../../../../translated_images/ro/scatter3.722d21e6f20b3ea2.webp)
 
 Este acesta un caz simplu de cerere și ofertă? Din cauza unor factori precum schimbările climatice și colapsul coloniilor, există mai puțină miere disponibilă pentru cumpărare an după an, iar astfel prețul crește?
 
@@ -98,7 +98,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab
 ```
 Răspuns: Da, cu câteva excepții în jurul anului 2003:
 
-![line chart 1](../../../../../translated_images/ro/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png)
+![line chart 1](../../../../../translated_images/ro/line1.299b576fbb2a59e6.webp)
 
 Întrebare: Ei bine, în 2003 putem observa și o creștere a stocului de miere? Ce se întâmplă dacă analizezi producția totală an după an?
 
@@ -106,7 +106,7 @@ Răspuns: Da, cu câteva excepții în jurul anului 2003:
 qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod")
 ```
 
-![line chart 2](../../../../../translated_images/ro/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png)
+![line chart 2](../../../../../translated_images/ro/line2.3b18fcda7176ceba.webp)
 
 Răspuns: Nu chiar. Dacă te uiți la producția totală, pare să fi crescut în acel an, deși, în general, cantitatea de miere produsă este în scădere în acești ani.
 
@@ -126,7 +126,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) +
 ```
 În această vizualizare, poți compara producția per colonie și numărul de colonii an după an, alăturate, cu o grilă setată la 3 coloane:
 
-![facet grid](../../../../../translated_images/ro/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png)
+![facet grid](../../../../../translated_images/ro/facet.491ad90d61c2a7cc.webp)
 
 Pentru acest set de date, nimic nu pare să iasă în evidență în ceea ce privește numărul de colonii și producția lor, an după an și stat după stat. Există o altă modalitate de a găsi o corelație între aceste două variabile?
 
@@ -143,7 +143,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3,
 axis(side = 4, at = pretty(range(y2)))      
 mtext("colony yield", side = 4, line = 3)   
 ```
-![superimposed plots](../../../../../translated_images/ro/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png)
+![superimposed plots](../../../../../translated_images/ro/dual-line.fc4665f360a54018.webp)
 
 Deși nimic nu sare în ochi în jurul anului 2003, acest lucru ne permite să încheiem lecția pe o notă puțin mai optimistă: deși numărul de colonii este în scădere, acesta pare să se stabilizeze, chiar dacă producția per colonie este în scădere.
 
diff --git a/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
index 1d922b06..c589b30b 100644
--- a/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
+++ b/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
@@ -38,25 +38,25 @@ Una dintre abilitățile de bază ale unui specialist în date este capacitatea
 
 Chiar dacă un specialist în date este atent să aleagă graficul potrivit pentru datele potrivite, există multe moduri în care datele pot fi afișate pentru a susține un punct de vedere, adesea în detrimentul datelor în sine. Există numeroase exemple de grafice și infografice înșelătoare!
 
-[![Cum Mint Graficele de Alberto Cairo](../../../../../translated_images/ro/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Cum mint graficele")
+[![Cum Mint Graficele de Alberto Cairo](../../../../../translated_images/ro/tornado.2880ffc7f135f82b.webp)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Cum mint graficele")
 
 > 🎥 Fă clic pe imaginea de mai sus pentru o prezentare despre graficele înșelătoare
 
 Acest grafic inversează axa X pentru a arăta opusul adevărului, bazat pe date:
 
-![grafic prost 1](../../../../../translated_images/ro/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png)
+![grafic prost 1](../../../../../translated_images/ro/bad-chart-1.596bc93425a8ac30.webp)
 
 [Acest grafic](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) este chiar mai înșelător, deoarece privirea este atrasă spre dreapta pentru a concluziona că, în timp, cazurile de COVID au scăzut în diverse județe. De fapt, dacă te uiți atent la date, vei descoperi că acestea au fost rearanjate pentru a crea acea tendință descendentă înșelătoare.
 
-![grafic prost 2](../../../../../translated_images/ro/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg)
+![grafic prost 2](../../../../../translated_images/ro/bad-chart-2.62edf4d2f30f4e51.webp)
 
 Acest exemplu notoriu folosește culoarea ȘI o axă Y inversată pentru a înșela: în loc să concluzionezi că decesele cauzate de arme au crescut după adoptarea legislației favorabile armelor, privirea este păcălită să creadă că opusul este adevărat:
 
-![grafic prost 3](../../../../../translated_images/ro/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg)
+![grafic prost 3](../../../../../translated_images/ro/bad-chart-3.e201e2e915a230bc.webp)
 
 Acest grafic ciudat arată cum proporțiile pot fi manipulate, cu efecte hilare:
 
-![grafic prost 4](../../../../../translated_images/ro/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg)
+![grafic prost 4](../../../../../translated_images/ro/bad-chart-4.8872b2b881ffa96c.webp)
 
 Compararea incomparabilului este o altă tactică dubioasă. Există un [site web minunat](https://tylervigen.com/spurious-correlations) dedicat 'corelațiilor false', care afișează 'fapte' ce corelează lucruri precum rata divorțurilor din Maine și consumul de margarină. Un grup Reddit colectează, de asemenea, [utilizările urâte](https://www.reddit.com/r/dataisugly/top/?t=all) ale datelor.
 
@@ -91,13 +91,13 @@ Etichetează axele, oferă o legendă dacă este necesar și oferă tooltips pen
 
 Dacă datele tale sunt textuale și verbose pe axa X, poți înclina textul pentru o mai bună lizibilitate. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) oferă graficare 3D, dacă datele tale o susțin. Vizualizări sofisticate de date pot fi produse folosind această metodă.
 
-![grafice 3D](../../../../../translated_images/ro/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png)
+![grafice 3D](../../../../../translated_images/ro/3d.db1734c151eee87d.webp)
 
 ## Afișarea animată și graficarea 3D
 
 Unele dintre cele mai bune vizualizări de date de astăzi sunt animate. Shirley Wu are exemple uimitoare realizate cu D3, cum ar fi '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', unde fiecare floare este o vizualizare a unui film. Un alt exemplu pentru Guardian este 'bussed out', o experiență interactivă care combină vizualizările cu Greensock și D3 plus un format de articol narativ pentru a arăta cum NYC gestionează problema persoanelor fără adăpost, trimițându-le cu autobuzul în afara orașului.
 
-![busing](../../../../../translated_images/ro/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png)
+![busing](../../../../../translated_images/ro/busing.8157cf1bc89a3f65.webp)
 
 > "Bussed Out: Cum America își mută persoanele fără adăpost" de la [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Vizualizări de Nadieh Bremer & Shirley Wu
 
@@ -107,7 +107,7 @@ Deși această lecție nu este suficientă pentru a intra în detalii despre ace
 
 Vei finaliza o aplicație web care va afișa o vizualizare animată a acestei rețele sociale. Folosește o bibliotecă construită pentru a crea o [vizualizare a unei rețele](https://github.com/emiliorizzo/vue-d3-network) utilizând Vue.js și D3. Când aplicația rulează, poți trage nodurile pe ecran pentru a rearanja datele.
 
-![liaisons](../../../../../translated_images/ro/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png)
+![liaisons](../../../../../translated_images/ro/liaisons.90ce7360bcf84765.webp)
 
 ## Proiect: Construiește un grafic pentru a arăta o rețea folosind D3.js
 
diff --git a/translations/ro/3-Data-Visualization/README.md b/translations/ro/3-Data-Visualization/README.md
index cd52688a..ae491307 100644
--- a/translations/ro/3-Data-Visualization/README.md
+++ b/translations/ro/3-Data-Visualization/README.md
@@ -1,6 +1,6 @@
 # Vizualizări
 
-![o albină pe o floare de lavandă](../../../translated_images/ro/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)  
+![o albină pe o floare de lavandă](../../../translated_images/ro/bee.0aa1d91132b12e3a.webp)  
 > Fotografie de <a href="https://unsplash.com/@jenna2980?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Jenna Lee</a> pe <a href="https://unsplash.com/s/photos/bees-in-a-meadow?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 Vizualizarea datelor este una dintre cele mai importante sarcini ale unui specialist în date. Imaginile valorează cât 1000 de cuvinte, iar o vizualizare te poate ajuta să identifici tot felul de aspecte interesante ale datelor tale, cum ar fi vârfuri, valori extreme, grupări, tendințe și multe altele, care te pot ajuta să înțelegi povestea pe care datele tale încearcă să o spună.
diff --git a/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md
index 6413a2e4..63cdb8e7 100644
--- a/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md
+++ b/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md
@@ -16,7 +16,7 @@ Până în acest moment, probabil ai realizat că știința datelor este un proc
 
 Această lecție se concentrează pe 3 părți ale ciclului de viață: capturare, procesare și mentenanță.
 
-![Diagramă a ciclului de viață al științei datelor](../../../../translated_images/ro/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg)  
+![Diagramă a ciclului de viață al științei datelor](../../../../translated_images/ro/data-science-lifecycle.a1e362637503c4fb.webp)  
 > Fotografie de [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/)
 
 ## Capturare
@@ -92,7 +92,7 @@ Explorează [Ciclul de viață al Procesului de Știința Datelor al Echipei](ht
 
 |Procesul de Știința Datelor al Echipei (TDSP)|Procesul standard inter-industrial pentru mineritul datelor (CRISP-DM)|
 |--|--|
-|![Ciclul de viață al echipei de știința datelor](../../../../translated_images/ro/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imaginea Procesului de Știința Datelor](../../../../translated_images/ro/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) |
+|![Ciclul de viață al echipei de știința datelor](../../../../translated_images/ro/tdsp-lifecycle2.e19029d598e2e73d.webp) | ![Imaginea Procesului de Știința Datelor](../../../../translated_images/ro/CRISP-DM.8bad2b4c66e62aa7.webp) |
 | Imagine de [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Imagine de [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) |
 
 ## [Chestionar după lecție](https://ff-quizzes.netlify.app/en/ds/quiz/27)
diff --git a/translations/ro/4-Data-Science-Lifecycle/README.md b/translations/ro/4-Data-Science-Lifecycle/README.md
index 38e4a08c..fe5ea41d 100644
--- a/translations/ro/4-Data-Science-Lifecycle/README.md
+++ b/translations/ro/4-Data-Science-Lifecycle/README.md
@@ -1,6 +1,6 @@
 # Ciclu de Viață în Știința Datelor
 
-![communication](../../../translated_images/ro/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)
+![communication](../../../translated_images/ro/communication.06d8e2a88d30d168.webp)
 > Fotografie de <a href="https://unsplash.com/@headwayio?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Headway</a> pe <a href="https://unsplash.com/s/photos/communication?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 În aceste lecții, vei explora câteva aspecte ale ciclului de viață în Știința Datelor, inclusiv analiza și comunicarea legate de date.
diff --git a/translations/ro/5-Data-Science-In-Cloud/README.md b/translations/ro/5-Data-Science-In-Cloud/README.md
index 38b7e4b7..0e69a9e4 100644
--- a/translations/ro/5-Data-Science-In-Cloud/README.md
+++ b/translations/ro/5-Data-Science-In-Cloud/README.md
@@ -1,12 +1,12 @@
 # Știința Datelor în Cloud
 
-![cloud-picture](../../../translated_images/ro/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)
+![cloud-picture](../../../translated_images/ro/cloud-picture.f5526de3c6c6387b.webp)
 
 > Fotografie de [Jelleke Vanooteghem](https://unsplash.com/@ilumire) de pe [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)
 
 Când vine vorba de realizarea științei datelor cu seturi mari de date, cloud-ul poate fi o schimbare majoră. În următoarele trei lecții, vom vedea ce este cloud-ul și de ce poate fi foarte util. De asemenea, vom explora un set de date despre insuficiența cardiacă și vom construi un model pentru a ajuta la evaluarea probabilității ca cineva să sufere de insuficiență cardiacă. Vom folosi puterea cloud-ului pentru a antrena, implementa și utiliza un model în două moduri diferite. Un mod folosind doar interfața utilizatorului într-un stil Low code/No code, iar celălalt mod folosind Azure Machine Learning Software Developer Kit (Azure ML SDK).
 
-![project-schema](../../../translated_images/ro/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png)
+![project-schema](../../../translated_images/ro/project-schema.420e56d495624541.webp)
 
 ### Subiecte
 
diff --git a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
index b30170f1..239b6f47 100644
--- a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
+++ b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
@@ -32,7 +32,7 @@ Datorită democratizării AI, dezvoltatorii găsesc acum mai ușor să proiectez
 
  * [Știința datelor în sănătate](https://data-flair.training/blogs/data-science-in-healthcare/) - evidențiază aplicații precum imagistica medicală (de exemplu, RMN, radiografie, tomografie), genomica (secvențierea ADN-ului), dezvoltarea medicamentelor (evaluarea riscurilor, predicția succesului), analitica predictivă (îngrijirea pacienților și logistica aprovizionării), urmărirea și prevenirea bolilor etc.
 
-![Aplicații ale Științei Datelor în Lumea Reală](../../../../translated_images/ro/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Credit imagine: [Data Flair: 6 Aplicații Uimitoare ale Științei Datelor ](https://data-flair.training/blogs/data-science-applications/)
+![Aplicații ale Științei Datelor în Lumea Reală](../../../../translated_images/ro/data-science-applications.4e5019cd8790ebac.webp) Credit imagine: [Data Flair: 6 Aplicații Uimitoare ale Științei Datelor ](https://data-flair.training/blogs/data-science-applications/)
 
 Figura arată alte domenii și exemple de aplicare a tehnicilor de știința datelor. Vrei să explorezi alte aplicații? Consultă secțiunea [Revizuire & Studiu Individual](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) de mai jos.
 
diff --git a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
index a917dcfa..85adf372 100644
--- a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
+++ b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
@@ -13,7 +13,7 @@ Interfața Explorer (prezentată în captura de ecran de mai jos) îți permite
  2. Explorezi [Catalogul](https://planetarycomputer.microsoft.com/catalog) seturilor de date - află scopul fiecăruia.
  3. Utilizezi Explorer - alegi un set de date de interes, selectezi o interogare și o opțiune de redare relevante.
 
-![The Planetary Computer Explorer](../../../../translated_images/ro/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png)
+![The Planetary Computer Explorer](../../../../translated_images/ro/planetary-computer-explorer.c1e95a9b053167d6.webp)
 
 `Sarcina ta:`
 Acum studiază vizualizarea afișată în browser și răspunde la următoarele întrebări:
diff --git a/translations/ro/README.md b/translations/ro/README.md
index 52815b8c..297a6692 100644
--- a/translations/ro/README.md
+++ b/translations/ro/README.md
@@ -17,11 +17,11 @@
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
-Avocații Azure Cloud de la Microsoft sunt încântați să ofere un curriculum de 10 săptămâni, cu 20 de lecții, dedicat Științei Datelor. Fiecare lecție include chestionare înainte și după lecție, instrucțiuni scrise pentru completarea lecției, o soluție și o temă. Pedagogia noastră bazată pe proiecte vă permite să învățați în timp ce construiți, o metodă demonstrată pentru a face noile abilități să "rămână".
+Avocații Azure Cloud de la Microsoft sunt încântați să ofere un curriculum de 10 săptămâni, 20 de lecții, toate despre Data Science. Fiecare lecție include chestionare înainte și după lecție, instrucțiuni scrise pentru a finaliza lecția, o soluție și o sarcină. Pedagogia noastră bazată pe proiecte vă permite să învățați construind, o metodă dovedită pentru a face noile abilități să „rămână”.
 
 **Mulțumiri călduroase autorilor noștri:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer).
 
-**🙏 Mulțumiri speciale 🙏 autorilor, recenzorilor și contribuitorilor de conținut [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** în special Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
+**🙏 Mulțumiri speciale 🙏 autorilor, recenzorilor și contributorilor de conținut [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** în special Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
 [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
 
 |![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/ro/00-Title.8af36cd35da1ac55.webp)|
@@ -30,28 +30,38 @@ Avocații Azure Cloud de la Microsoft sunt încântați să ofere un curriculum
 
 ### 🌐 Suport Multilingv
 
-#### Suportat prin GitHub Action (Automatizat și Mereu Actualizat)
+#### Suportat prin GitHub Action (automat și întotdeauna actualizat)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
 [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](./README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
 
-> **Preferi să clonezi local?**
-
-> Acest depozit include peste 50 de traduceri în limbi diferite, ceea ce crește semnificativ dimensiunea descărcării. Pentru a clona fără traduceri, folosește sparse checkout:
+> **Preferi să Clonezi Local?**
+>
+> Acest depozit include traduceri în peste 50 de limbi, ceea ce crește semnificativ dimensiunea de descărcare. Pentru a clona fără traduceri, folosește sparse checkout:
+>
+> **Bash / macOS / Linux:**
 > ```bash
 > git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
 > cd Data-Science-For-Beginners
 > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
 > ```
-> Aceasta îți oferă tot ce ai nevoie pentru a finaliza cursul cu o descărcare mult mai rapidă.
+>
+> **CMD (Windows):**
+> ```cmd
+> git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
+> cd Data-Science-For-Beginners
+> git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"
+> ```
+>
+> Acest lucru îți oferă tot ce ai nevoie pentru a finaliza cursul cu o viteză de descărcare mult mai mare.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-**Dacă dorești să fie suportate limbi suplimentare de traducere, acestea sunt listate [aici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
+**Dacă dorești să se adauge limbi suplimentare de traducere, acestea sunt listate [aici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
 
-#### Alătură-te Comunității Noastre 
+#### Alătură-te Comunității Noastre  
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Avem o serie Discord “Învață cu AI” în desfășurare, află mai multe și alătură-te la [Learn with AI Series](https://aka.ms/learnwithai/discord) între 18 - 30 septembrie 2025. Vei primi trucuri și sfaturi pentru folosirea GitHub Copilot pentru Știința Datelor.
+Avem o serie Discord „învățăm cu AI” în desfășurare, află mai multe și alătură-te nouă la [Learn with AI Series](https://aka.ms/learnwithai/discord) în perioada 18 - 30 septembrie 2025. Vei primi sfaturi și trucuri despre cum să folosești GitHub Copilot pentru Data Science.
 
 ![Learn with AI series](../../translated_images/ro/1.2b28cdc6205e26fe.webp)
 
@@ -59,74 +69,74 @@ Avem o serie Discord “Învață cu AI” în desfășurare, află mai multe ș
 
 Începe cu următoarele resurse:
 
-- [Pagina Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Pe această pagină vei găsi resurse pentru începători, pachete pentru studenți și chiar modalități de a obține un voucher de certificare gratuit. Aceasta este o pagină pe care vrei să o adaugi la favorite și să o verifici din când în când, deoarece conținutul se schimbă cel puțin lunar.
-- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Alătură-te unei comunități globale de ambasadori studenți, aceasta poate fi calea ta spre Microsoft.
+- [Pagina Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Pe această pagină, vei găsi resurse pentru începători, pachete pentru studenți și chiar modalități de a obține un voucher gratuit pentru certificare. Aceasta este o pagină pe care vrei să o adaugi la favorite și să o verifici din când în când, deoarece conținutul este actualizat cel puțin lunar.
+- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Alătură-te unei comunități globale de ambasadori studenți, ar putea fi drumul tău către Microsoft.
 
-# Începutul
+# Începem
 
 ## 📚 Documentație
 
-- **[Ghid de Instalare](INSTALLATION.md)** - Instrucțiuni pas cu pas pentru configurarea mediului pentru începători
+- **[Ghid de Instalare](INSTALLATION.md)** - Instrucțiuni pas cu pas pentru setare pentru începători
 - **[Ghid de Utilizare](USAGE.md)** - Exemple și fluxuri de lucru comune
-- **[Depanare](TROUBLESHOOTING.md)** - Soluții pentru probleme frecvente
-- **[Ghid Contribuire](CONTRIBUTING.md)** - Cum să contribui la acest proiect
+- **[Depanare](TROUBLESHOOTING.md)** - Soluții pentru probleme comune
+- **[Ghid de Contribuire](CONTRIBUTING.md)** - Cum să contribui la acest proiect
 - **[Pentru Profesori](for-teachers.md)** - Ghid pentru predare și resurse pentru clasă
 
 ## 👨‍🎓 Pentru Studenți
-> **Începători Compleți**: Nou în știința datelor? Începe cu [exemplele noastre prietenoase pentru începători](examples/README.md)! Aceste exemple simple și bine comentate te vor ajuta să înțelegi elementele de bază înainte de a te adânci în curriculumul complet.
-> **[Studenți](https://aka.ms/student-page)**: pentru a folosi acest curriculum pe cont propriu, fă un fork la întregul repo și completează exercițiile singur, începând cu un chestionar pre-lectură. Apoi citește lecția și finalizează restul activităților. Încearcă să creezi proiectele înțelegând lecțiile, mai degrabă decât să copiezi codul soluției; cu toate acestea, acel cod este disponibil în folderele /solutions din fiecare lecție orientată spre proiect. O altă idee ar fi să formezi un grup de studiu cu prietenii și să parcurgeți împreună conținutul. Pentru studiu suplimentar, recomandăm [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
+> **Începători Compleți**: Ești nou în data science? Începe cu exemplele noastre [prietenoase pentru începători](examples/README.md)! Aceste exemple simple, bine comentate te vor ajuta să înțelegi bazele înainte să începi cursul complet.
+> **[Studenți](https://aka.ms/student-page)**: pentru a folosi acest curriculum pe cont propriu, forchează întregul repository și completează exercițiile singur, începând cu un quiz înainte de lecție. Apoi citește lecția și fă restul activităților. Încearcă să creezi proiectele înțelegând lecțiile, nu doar copiind codul soluției; totuși, acel cod este disponibil în folderele /solutions din fiecare lecție orientată pe proiecte. O altă idee ar fi să formezi un grup de studiu cu prietenii și să parcurgeți conținutul împreună. Pentru studiu suplimentar, recomandăm [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
 
-**Pornire Rapidă:**
-1. Verifică [Ghidul de Instalare](INSTALLATION.md) pentru a seta mediul
-2. Revizuiește [Ghidul de Utilizare](USAGE.md) pentru a învăța cum să folosești curriculumul
-3. Începe cu Lecția 1 și parcurge-le în ordine
-4. Alătură-te comunității noastre pe [Discord](https://aka.ms/ds4beginners/discord) pentru suport
+**Start rapid:**
+1. Verifică [Ghidul de Instalare](INSTALLATION.md) pentru a-ți configura mediul
+2. Consultă [Ghidul de Utilizare](USAGE.md) pentru a învăța cum să lucrezi cu curriculumul
+3. Începe cu Lecția 1 și urmează în ordine
+4. Alătură-te [comunității noastre Discord](https://aka.ms/ds4beginners/discord) pentru suport
 
 ## 👩‍🏫 Pentru Profesori
+> **Profesorilor**: am [inclus câteva sugestii](for-teachers.md) despre cum să folosiți acest curriculum. Ne-ar plăcea să ne oferiți feedback [în forumul nostru de discuții](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
 
-> **Profesori**: am inclus [unele sugestii](for-teachers.md) despre cum să folosiți acest curriculum. Am aprecia feedback-ul dumneavoastră [în forumul nostru de discuții](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
-## Cunoaște Echipa
+## Cunoașteți Echipa
 
-[![Video promo](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Video promo")
+[![Video promoțional](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Video promoțional")
 
 **Gif de** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
 
-> 🎥 Dă clic pe imaginea de mai sus pentru un video despre proiect și oamenii care l-au creat!
+> 🎥 Faceți click pe imaginea de mai sus pentru un videoclip despre proiect și persoanele care l-au creat!
 
 ## Pedagogie
 
-Am ales două principii pedagogice în construirea acestui curriculum: să fie bazat pe proiecte și să includă teste frecvente. La finalul acestei serii, studenții vor fi învățat principii de bază ale științei datelor, inclusiv concepte etice, pregătirea datelor, diferite moduri de a lucra cu date, vizualizarea datelor, analiza datelor, cazuri de folosire reale ale științei datelor și altele.
+Am ales două principii pedagogice în construirea acestui curriculum: asigurarea faptului că este bazat pe proiecte și că include teste frecvente. La finalul acestei serii, studenții vor fi învățat principii de bază ale științei datelor, inclusiv concepte etice, pregătirea datelor, diferite moduri de lucru cu datele, vizualizarea datelor, analiza datelor, cazuri de utilizare reală ale științei datelor și altele.
 
-În plus, un test cu miză redusă înaintea unei clase setează intenția studentului față de învățarea unui subiect, în timp ce un al doilea test după clasă asigură o retenție suplimentară. Acest curriculum a fost conceput să fie flexibil și distractiv și poate fi urmat în totalitate sau parțial. Proiectele încep mici și devin din ce în ce mai complexe până la finalul ciclului de 10 săptămâni.
+În plus, un test cu miză mică înainte de o lecție setează intenția studentului de a învăța un subiect, în timp ce un al doilea test după lecție asigură o retenție suplimentară. Acest curriculum a fost proiectat să fie flexibil și distractiv și poate fi parcurs integral sau parțial. Proiectele încep mici și devin din ce în ce mai complexe până la sfârșitul ciclului de 10 săptămâni.
 
-> Găsește [Codul nostru de conduită](CODE_OF_CONDUCT.md), [Contribuții](CONTRIBUTING.md), ghidurile de [Traduceri](TRANSLATIONS.md). Apreciem feedback-ul tău constructiv!
+> Găsiți [Codul nostru de conduită](CODE_OF_CONDUCT.md), [Contribuții](CONTRIBUTING.md), [Ghidurile de traducere](TRANSLATIONS.md). Așteptăm cu interes feedback-ul vostru constructiv!
 
 ## Fiecare lecție include:
 
 - Sketchnote opțional
 - Video suplimentar opțional
-- Test de încălzire înaintea lecției
+- Test de încălzire pre-lezare
 - Lecție scrisă
 - Pentru lecțiile bazate pe proiect, ghiduri pas cu pas pentru construirea proiectului
-- Verificări ale cunoștințelor
+- Verificări de cunoștințe
 - O provocare
 - Lectură suplimentară
 - Temă
 - [Test post-lectie](https://ff-quizzes.netlify.app/en/)
 
-> **Un comentariu despre teste**: Toate testele se găsesc în folderul Quiz-App, cu 40 de teste în total a câte trei întrebări fiecare. Sunt legate din lecții, dar aplicația de test poate fi rulată local sau implementată în Azure; urmează instrucțiunile din folderul `quiz-app`. Se traduc progresiv.
+> **O notă despre teste**: Toate testele se găsesc în folderul Quiz-App, în total 40 de teste cu câte trei întrebări fiecare. Sunt legate din interiorul lecțiilor, dar aplicația pentru teste poate fi rulată local sau implementată în Azure; urmați instrucțiunile din folderul `quiz-app`. Testele sunt treptat localizate.
 
-## 🎓 Exemple Prietenoase Pentru Începători
+## 🎓 Exemple prietenoase pentru începători
 
-**Ești nou în știința datelor?** Am creat un [director de exemple](examples/README.md) special cu cod simplu și bine comentat pentru a te ajuta să începi:
+**Ești nou în știința datelor?** Am creat un director special [exemple](examples/README.md) cu cod simplu, bine comentat pentru a te ajuta să începi:
 
-- 🌟 **Hello World** - Primul tău program de știința datelor
+- 🌟 **Hello World** - Primul tău program de știință a datelor
 - 📂 **Încărcarea datelor** - Învață să citești și să explorezi seturi de date
-- 📊 **Analiză simplă** - Calculează statistici și găsește tipare
-- 📈 **Vizualizare de bază** - Creează grafice și diagrame
-- 🔬 **Proiect real** - Flux complet de lucru de la început până la sfârșit
+- 📊 **Analiză simplă** - Calculează statistici și găsește modele
+- 📈 **Vizualizare de bază** - Creează diagrame și grafice
+- 🔬 **Proiect real** - Flux complet de lucru de la început până la final
 
-Fiecare exemplu include comentarii detaliate care explică fiecare pas, fiind perfect pentru începători absoluți!
+Fiecare exemplu include comentarii detaliate care explică fiecare pas, fiind perfect pentru începători absoluti!
 
 👉 **[Începe cu exemplele](examples/README.md)** 👈
 
@@ -138,64 +148,64 @@ Fiecare exemplu include comentarii detaliate care explică fiecare pas, fiind pe
 | Știința datelor pentru începători: Plan - _Sketchnote de [@nitya](https://twitter.com/nitya)_ |
 
 
-| Număr lecție | Subiect | Grupare lecție | Obiective de învățare | Lecție legată | Autor |
-| :----------: | :---------------------------------------: | :--------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
-| 01 | Definirea științei datelor | [Introducere](1-Introduction/README.md) | Învață conceptele de bază din spatele științei datelor și modul în care se leagă de inteligența artificială, învățarea automatizată și big data. | [lecție](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
-| 02 | Etica în știința datelor | [Introducere](1-Introduction/README.md) | Concepte, provocări și cadre ale eticii datelor. | [lecție](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
-| 03 | Definirea datelor | [Introducere](1-Introduction/README.md) | Cum sunt clasificate datele și sursele lor comune. | [lecție](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
-| 04 | Introducere în Statistică & Probabilitate | [Introducere](1-Introduction/README.md) | Tehnicile matematice ale probabilității și statisticii pentru înțelegerea datelor. | [lecție](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
-| 05 | Lucrul cu date relaționale | [Lucrul cu date](2-Working-With-Data/README.md) | Introducere în date relaționale și elemente de explorare și analiză a datelor relaționale folosind Structured Query Language, cunoscut și ca SQL (pronunțat „see-quell”). | [lecție](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) |
-| 06 | Lucrul cu date NoSQL | [Lucrul cu date](2-Working-With-Data/README.md) | Introducere în date non-relaționale, tipurile lor și elementele de explorare și analiză a bazelor de date de tip document. | [lecție](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique) |
-| 07 | Lucrul cu Python | [Lucrul cu date](2-Working-With-Data/README.md) | Bazele utilizării Python pentru explorarea datelor cu biblioteci precum Pandas. Este recomandată o înțelegere fundamentala a programării Python. | [lecție](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
-| 08 | Pregătirea datelor | [Lucrul cu date](2-Working-With-Data/README.md) | Obiecte despre tehnici de curățare și transformare a datelor pentru a gestiona provocările datelor lipsă, inexacte sau incomplete. | [lecție](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
-| 09 | Vizualizarea cantităților | [Vizualizarea datelor](3-Data-Visualization/README.md) | Învață să folosești Matplotlib pentru a vizualiza date despre păsări 🦆 | [lecție](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 10 | Vizualizarea distribuțiilor datelor | [Vizualizarea datelor](3-Data-Visualization/README.md) | Vizualizarea observațiilor și tendințelor într-un interval. | [lecție](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 11 | Vizualizarea proporțiilor | [Vizualizarea datelor](3-Data-Visualization/README.md) | Vizualizarea procentajelor discrete și grupate. | [lecție](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 12 | Vizualizarea relațiilor | [Vizualizarea datelor](3-Data-Visualization/README.md) | Vizualizarea conexiunilor și corelațiilor între seturi de date și variabilele lor. | [lecție](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 13 | Vizualizări semnificative | [Vizualizarea datelor](3-Data-Visualization/README.md) | Tehnici și îndrumări pentru a face vizualizările valoroase pentru rezolvarea eficientă a problemelor și obținerea de perspective. | [lecție](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 14 | Introducere în ciclul de viață al științei datelor | [Ciclu de viață](4-Data-Science-Lifecycle/README.md) | Introducere în ciclul de viață al științei datelor și prima etapă de achiziție și extragere a datelor. | [lecție](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
-| 15 | Analiza | [Ciclu de viață](4-Data-Science-Lifecycle/README.md) | Această fază a ciclului de viață al științei datelor se concentrează pe tehnici de analiză a datelor. | [lecție](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) |
-| 16 | Comunicare | [Ciclu de viață](4-Data-Science-Lifecycle/README.md) | Această fază a ciclului de viață al științei datelor se concentrează pe prezentarea perspectivelor din date într-un mod care să faciliteze înțelegerea decidenților. | [lecție](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) |
-| 17 | Știința datelor în cloud | [Date în cloud](5-Data-Science-In-Cloud/README.md) | Această serie de lecții introduce știința datelor în cloud și beneficiile sale. | [lecție](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) și [Maud](https://twitter.com/maudstweets) |
-| 18 | Știința datelor în cloud | [Date în cloud](5-Data-Science-In-Cloud/README.md) | Antrenarea modelelor folosind unelte Low Code. | [lecție](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) și [Maud](https://twitter.com/maudstweets) |
-| 19 | Știința datelor în cloud | [Date în cloud](5-Data-Science-In-Cloud/README.md) | Implementarea modelelor cu Azure Machine Learning Studio. | [lecție](5-Data-Science-In-Cloud/19-Azure/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) și [Maud](https://twitter.com/maudstweets) |
-| 20 | Știința datelor în teren | [În teren](6-Data-Science-In-Wild/README.md) | Proiecte bazate pe știința datelor în lumea reală. | [lecție](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
+| Numărul lecției | Subiect | Gruparea lecției | Obiective de învățare | Lecție legată | Autor |
+| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
+| 01 | Definirea științei datelor | [Introducere](1-Introduction/README.md) | Învățați conceptele de bază din spatele științei datelor și cum se raportează la inteligența artificială, învățarea automată și big data. | [lecția](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
+| 02 | Etica în știința datelor | [Introducere](1-Introduction/README.md) | Concepte, provocări și cadre pentru etica datelor. | [lecția](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
+| 03 | Definirea datelor | [Introducere](1-Introduction/README.md) | Cum sunt clasificate datele și sursele lor comune. | [lecția](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
+| 04 | Introducere în statistică și probabilitate | [Introducere](1-Introduction/README.md) | Tehnicile matematice ale probabilității și statisticii pentru a înțelege datele. | [lecția](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
+| 05 | Lucrul cu date relaționale | [Lucrul cu date](2-Working-With-Data/README.md) | Introducere în date relaționale și elementele de bază ale explorării și analizei datelor relaționale cu limbajul de interogare structurat, cunoscut și sub numele de SQL (pronunțat „see-quell”). | [lecția](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
+| 06 | Lucrul cu date NoSQL | [Lucrul cu date](2-Working-With-Data/README.md) | Introducere în date neraportate, tipurile lor variate și elementele de bază ale explorării și analizei bazelor de date document. | [lecția](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
+| 07 | Lucrul cu Python | [Lucrul cu date](2-Working-With-Data/README.md) | Bazele utilizării Python pentru explorarea datelor cu biblioteci precum Pandas. Se recomandă o înțelegere fundamentală a programării în Python. | [lecția](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
+| 08 | Pregătirea datelor | [Lucrul cu date](2-Working-With-Data/README.md) | Tematici privind tehnici de curățare și transformare a datelor pentru a face față provocărilor datelor lipsă, inexacte sau incomplete. | [lecția](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
+| 09 | Vizualizarea cantităților | [Vizualizarea datelor](3-Data-Visualization/README.md) | Învață cum să folosești Matplotlib pentru a vizualiza date despre păsări 🦆 | [lecția](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 10 | Vizualizarea distribuțiilor de date | [Vizualizarea datelor](3-Data-Visualization/README.md) | Vizualizarea observațiilor și tendințelor într-un interval. | [lecția](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 11 | Vizualizarea proporțiilor | [Vizualizarea datelor](3-Data-Visualization/README.md) | Vizualizarea procentajelor discrete și grupate. | [lecția](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 12 | Vizualizarea relațiilor | [Vizualizarea datelor](3-Data-Visualization/README.md) | Vizualizarea conexiunilor și corelațiilor între seturi de date și variabilele lor. | [lecția](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 13 | Vizualizări semnificative | [Vizualizarea datelor](3-Data-Visualization/README.md) | Tehnici și îndrumări pentru a face vizualizările valoroase pentru rezolvarea efectivă a problemelor și obținerea de insight-uri. | [lecția](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 14 | Introducere în ciclul de viață al științei datelor | [Ciclul de viață](4-Data-Science-Lifecycle/README.md) | Introducere în ciclul de viață al științei datelor și primul său pas de achiziție și extragere a datelor. | [lecția](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
+| 15 | Analiză | [Ciclul de viață](4-Data-Science-Lifecycle/README.md) | Această fază a ciclului de viață al științei datelor se concentrează pe tehnici de analiză a datelor. | [lecția](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
+| 16 | Comunicare | [Ciclul de viață](4-Data-Science-Lifecycle/README.md) | Această fază a ciclului de viață al științei datelor se concentrează pe prezentarea insight-urilor extrase din date într-un mod care să faciliteze înțelegerea de către factorii decidenți. | [lecția](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
+| 17 | Știința datelor în Cloud | [Date în cloud](5-Data-Science-In-Cloud/README.md) | Această serie de lecții introduce știința datelor în cloud și beneficiile ei. | [lecția](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) și [Maud](https://twitter.com/maudstweets) |
+| 18 | Știința datelor în Cloud | [Date în cloud](5-Data-Science-In-Cloud/README.md) | Antrenarea modelelor folosind unelte Low Code. |[lecția](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) și [Maud](https://twitter.com/maudstweets) |
+| 19 | Știința datelor în Cloud | [Date în cloud](5-Data-Science-In-Cloud/README.md) | Implementarea modelelor cu Azure Machine Learning Studio. | [lecția](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) și [Maud](https://twitter.com/maudstweets) |
+| 20 | Știința datelor în viața reală | [În sălbăticie](6-Data-Science-In-Wild/README.md) | Proiecte conduse de știința datelor în lumea reală. | [lecția](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
 
 ## GitHub Codespaces
 
-Urmează acești pași pentru a deschide acest exemplu într-un Codespace:
-1. Dă clic pe meniul derulant Code și selectează opțiunea Open with Codespaces.
-2. Selectează + New codespace în partea de jos a panoului.
-Pentru mai multe informații, consultă [documentația GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
+Urmați acești pași pentru a deschide acest exemplu într-un Codespace:
+1. Faceți click pe meniul derulant Code și selectați opțiunea Open with Codespaces.
+2. Selectați + New codespace în partea de jos a panoului.
+Pentru mai multe informații, consultați [documentația GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
 
-## VSCode Remote - Containere
-Urmează acești pași pentru a deschide acest depozit într-un container folosind mașina ta locală și VSCode cu extensia VS Code Remote - Containers:
+## VSCode Remote - Containers  
+Urmați acești pași pentru a deschide acest repo într-un container folosind mașina locală și VSCode cu extensia VS Code Remote - Containers:
 
-1. Dacă este prima dată când folosești un container de dezvoltare, asigură-te că sistemul tău îndeplinește cerințele prealabile (adică ai instalat Docker) din [documentația de început](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
+1. Dacă aceasta este prima dată când folosiți un container de dezvoltare, asigurați-vă că sistemul dvs. îndeplinește cerințele prealabile (de exemplu, să aveți Docker instalat) în [documentația pentru început](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
 
-Pentru a folosi acest depozit, poți deschide fie depozitul într-un volum Docker izolat:
+Pentru a folosi acest repository, puteți fie să deschideți repository-ul într-un volum izolat Docker:
 
-**Notă**: În fundal, se va folosi comanda Remote-Containers: **Clone Repository in Container Volume...** pentru a clona codul sursă într-un volum Docker în loc de sistemul local de fișiere. [Volumele](https://docs.docker.com/storage/volumes/) sunt mecanismul recomandat pentru persistența datelor containerului.
+**Notă**: Sub capotă, aceasta va folosi comanda Remote-Containers: **Clone Repository in Container Volume...** pentru a clona codul sursă într-un volum Docker în loc de sistemul local de fișiere. [Volumele](https://docs.docker.com/storage/volumes/) sunt mecanismul preferat pentru persistența datelor containerului.
 
-Sau deschide o copie clonată local sau descărcată a depozitului:
+Sau să deschideți o versiune clonată sau descărcată local a repository-ului:
 
-- Clonează acest depozit pe sistemul tău local.
-- Apasă F1 și selectează comanda **Remote-Containers: Open Folder in Container...**.
-- Selectează copia clonată a acestui folder, așteaptă să pornească containerul și încearcă.
+- Clonați acest repository în sistemul local de fișiere.
+- Apăsați F1 și selectați comanda **Remote-Containers: Open Folder in Container...**.
+- Selectați copia clonată a acestui folder, așteptați să pornească containerul și testați.
 
 ## Acces offline
 
-Poți rula această documentație offline folosind [Docsify](https://docsify.js.org/#/). Fă fork la acest repo, [instalează Docsify](https://docsify.js.org/#/quickstart) pe mașina ta locală, apoi în folderul rădăcină al acestui repo, tastează `docsify serve`. Site-ul va fi servit pe portul 3000 pe localhost-ul tău: `localhost:3000`.
+Puteți rula această documentație offline folosind [Docsify](https://docsify.js.org/#/). Faceți fork la acest repo, [instalați Docsify](https://docsify.js.org/#/quickstart) pe mașina locală, apoi în folderul rădăcină al acestui repo, tastați `docsify serve`. Site-ul va fi servit pe portul 3000 pe localhost: `localhost:3000`.
 
-> Notă, caietele Jupyter nu vor fi afișate prin Docsify, așa că atunci când trebuie să rulezi un caiet, fă-o separat în VS Code cu un kernel Python.
+> Atenție, notebook-urile nu vor fi randate prin Docsify, așa că atunci când trebuie să rulați un notebook, faceți asta separat în VS Code folosind un kernel Python.
 
 ## Alte Curricula
 
-Echipa noastră produce și alte curricula! Vezi:
+Echipa noastră produce și alte curriculum-uri! Verificați:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
-### LangChain
-[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
+### LangChain  
+[![LangChain4j pentru Începători](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
 [![LangChain.js pentru Începători](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
 [![LangChain pentru Începători](https://img.shields.io/badge/LangChain%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://github.com/microsoft/langchain-for-beginners?WT.mc_id=m365-94501-dwahlin)
 ---
@@ -208,18 +218,18 @@ Echipa noastră produce și alte curricula! Vezi:
 
 ---
  
-### Seria Inteligență Artificială Generativă
-[![Inteligență Artificială Generativă pentru Începători](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Inteligență Artificială Generativă (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![Inteligență Artificială Generativă (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![Inteligență Artificială Generativă (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+### Seria AI Generativ
+[![AI Generativ pentru Începători](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI Generativ (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
+[![AI Generativ (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![AI Generativ (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Învățare de bază
-[![Învățare Automată pentru Începători](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![Știința datelor pentru Începători](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![Inteligență Artificială pentru Începători](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![ML pentru Începători](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![Știința Datelor pentru Începători](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI pentru Începători](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
 [![Securitate Cibernetică pentru Începători](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
 [![Dezvoltare Web pentru Începători](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
 [![IoT pentru Începători](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
@@ -228,26 +238,26 @@ Echipa noastră produce și alte curricula! Vezi:
 ---
  
 ### Seria Copilot
-[![Copilot pentru Programare Asistată de AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot pentru programare AI în echipă](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
 [![Copilot pentru C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![Aventura Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Aventură Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
 ## Obținerea Ajutorului
 
-**Întâmpinați probleme?** Verificați [Ghidul de depanare](TROUBLESHOOTING.md) pentru soluții la probleme comune.
+**Întâmpinați probleme?** Consultați ghidul nostru de [Depanare](TROUBLESHOOTING.md) pentru soluții la probleme comune.
 
-Dacă vă blocați sau aveți întrebări despre construirea aplicațiilor AI, alăturați-vă altor cursanți și dezvoltatori experimentați în discuții despre MCP. Este o comunitate de suport unde întrebările sunt binevenite și cunoștințele sunt împărtășite liber.
+Dacă rămâneți blocat sau aveți întrebări despre construirea aplicațiilor AI. Alăturați-vă altor cursanți și dezvoltatori experimentați în discuții despre MCP. Este o comunitate suportivă unde întrebările sunt binevenite și cunoștințele sunt împărtășite liber.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Dacă aveți feedback despre produs sau întâmpinați erori în timpul dezvoltării, vizitați:
+Dacă aveți feedback despre produs sau întâmpinați erori în timpul dezvoltării vizitați:
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-**Declinare de responsabilitate**:
-Acest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim pentru acuratețe, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă trebuie considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesionistă realizată de un specialist uman. Nu ne asumăm nicio responsabilitate pentru eventualele neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri.
+**Declinarea responsabilității**:  
+Acest document a fost tradus utilizând serviciul de traducere automată AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim pentru acuratețe, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa de origine trebuie considerat sursa autorizată. Pentru informații critice, se recomandă traducerea profesională realizată de un traducător uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/ro/sketchnotes/README.md b/translations/ro/sketchnotes/README.md
index dd2b2bea..3d11a565 100644
--- a/translations/ro/sketchnotes/README.md
+++ b/translations/ro/sketchnotes/README.md
@@ -4,7 +4,7 @@ Găsește toate notițele schițate aici!
 
 Nitya Narasimhan, artist
 
-![notiță schițată cu planificare](../../../translated_images/ro/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)
+![notiță schițată cu planificare](../../../translated_images/ro/00-Roadmap.4905d6567dff4753.webp)
 
 ---
 
diff --git a/translations/sk/.co-op-translator.json b/translations/sk/.co-op-translator.json
index cd615203..399b35f5 100644
--- a/translations/sk/.co-op-translator.json
+++ b/translations/sk/.co-op-translator.json
@@ -11,12 +11,24 @@
     "source_file": "1-Introduction/01-defining-data-science/assignment.md",
     "language_code": "sk"
   },
+  "1-Introduction/01-defining-data-science/notebook.ipynb": {
+    "original_hash": "8f5eb7b3f7cc89e6d98fb32e1de65dec",
+    "translation_date": "2026-02-27T11:35:34+00:00",
+    "source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
+    "language_code": "sk"
+  },
   "1-Introduction/01-defining-data-science/solution/assignment.md": {
     "original_hash": "a8f79b9c0484c35b4f26e8aec7fc4d56",
     "translation_date": "2025-08-26T15:26:36+00:00",
     "source_file": "1-Introduction/01-defining-data-science/solution/assignment.md",
     "language_code": "sk"
   },
+  "1-Introduction/01-defining-data-science/solution/notebook.ipynb": {
+    "original_hash": "090bbfbfcb0c40d3d6e3236f836164ea",
+    "translation_date": "2026-02-27T11:36:07+00:00",
+    "source_file": "1-Introduction/01-defining-data-science/solution/notebook.ipynb",
+    "language_code": "sk"
+  },
   "1-Introduction/02-ethics/README.md": {
     "original_hash": "58860ce9a4b8a564003d2752f7c72851",
     "translation_date": "2025-10-03T16:56:19+00:00",
@@ -95,6 +107,12 @@
     "source_file": "2-Working-With-Data/07-python/assignment.md",
     "language_code": "sk"
   },
+  "2-Working-With-Data/07-python/notebook-covidspread.ipynb": {
+    "original_hash": "6335cccba01dc6ad7b15aba7a8c73f38",
+    "translation_date": "2026-02-27T11:37:01+00:00",
+    "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
+    "language_code": "sk"
+  },
   "2-Working-With-Data/08-data-preparation/README.md": {
     "original_hash": "1b560955ff39a2bcf2a049fce474a951",
     "translation_date": "2025-09-05T18:05:51+00:00",
@@ -360,8 +378,8 @@
     "language_code": "sk"
   },
   "README.md": {
-    "original_hash": "9204a2806964384a56f5cb0f22bbe953",
-    "translation_date": "2026-02-06T08:54:35+00:00",
+    "original_hash": "f671e295a294a2559fc59d1524e001b4",
+    "translation_date": "2026-02-27T11:43:15+00:00",
     "source_file": "README.md",
     "language_code": "sk"
   },
diff --git a/translations/sk/1-Introduction/01-defining-data-science/README.md b/translations/sk/1-Introduction/01-defining-data-science/README.md
index fc8f83ca..cb48cb91 100644
--- a/translations/sk/1-Introduction/01-defining-data-science/README.md
+++ b/translations/sk/1-Introduction/01-defining-data-science/README.md
@@ -6,7 +6,7 @@
 
 ---
 
-[![Video o definícii dátovej vedy](../../../../translated_images/sk/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I)
+[![Video o definícii dátovej vedy](../../../../translated_images/sk/video-def-ds.6623ee2392ef1abf.webp)](https://youtu.be/beZ7Mb_oz9I)
 
 ## [Kvíz pred prednáškou](https://ff-quizzes.netlify.app/en/ds/quiz/0)
 
@@ -142,7 +142,7 @@ Ak chceme ísť ešte ďalej, môžeme vykresliť čas potrebný na dokončenie
 
 V tejto výzve sa pokúsime nájsť koncepty relevantné pre oblasť dátovej vedy tým, že sa pozrieme na texty. Vezmeme článok z Wikipédie o dátovej vede, stiahneme a spracujeme text, a potom vytvoríme oblak slov, ako je tento:
 
-![Oblak slov pre dátovú vedu](../../../../translated_images/sk/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png)
+![Oblak slov pre dátovú vedu](../../../../translated_images/sk/ds_wordcloud.664a7c07dca57de0.webp)
 
 Navštívte [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), aby ste si prečítali kód. Môžete tiež spustiť kód a vidieť, ako vykonáva všetky transformácie dát v reálnom čase.
 
diff --git a/translations/sk/1-Introduction/01-defining-data-science/notebook.ipynb b/translations/sk/1-Introduction/01-defining-data-science/notebook.ipynb
index 4420e42b..ce8482e6 100644
--- a/translations/sk/1-Introduction/01-defining-data-science/notebook.ipynb
+++ b/translations/sk/1-Introduction/01-defining-data-science/notebook.ipynb
@@ -3,15 +3,15 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# Výzva: Analyzovanie textu o dátovej vede\n",
+    "# Výzva: Analýza textu o dátovej vede\n",
     "\n",
-    "V tomto príklade si urobíme jednoduché cvičenie, ktoré pokrýva všetky kroky tradičného procesu dátovej vedy. Nemusíte písať žiadny kód, stačí kliknúť na bunky nižšie, aby ste ich vykonali, a pozorovať výsledok. Ako výzvu vás povzbudzujeme, aby ste tento kód vyskúšali s rôznymi údajmi.\n",
+    "V tomto príklade si urobíme jednoduché cvičenie, ktoré pokrýva všetky kroky tradičného procesu dátovej vedy. Nemusíte písať žiadny kód, môžete jednoducho kliknúť na bunky nižšie, spustiť ich a pozorovať výsledok. Ako výzvu vám odporúčame vyskúšať tento kód s rôznymi údajmi.\n",
     "\n",
     "## Cieľ\n",
     "\n",
-    "V tejto lekcii sme diskutovali o rôznych konceptoch súvisiacich s dátovou vedou. Skúsme objaviť ďalšie súvisiace koncepty pomocou **textovej analýzy**. Začneme s textom o dátovej vede, extrahujeme z neho kľúčové slová a potom sa pokúsime vizualizovať výsledok.\n",
+    "V tejto lekcii sme diskutovali o rôznych konceptoch súvisiacich s dátovou vedou. Skúsme objaviť ďalšie súvisiace koncepty pomocou **text mining**. Začneme textom o dátovej vede, extrahujeme z neho kľúčové slová a potom sa pokúsime vizualizovať výsledok.\n",
     "\n",
-    "Ako text použijeme stránku o dátovej vede z Wikipédie:\n"
+    "Ako text použijem stránku o dátovej vede z Wikipédie:\n"
    ],
    "metadata": {}
   },
@@ -32,9 +32,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Krok 1: Získanie údajov\n",
+    "## Krok 1: Získavanie údajov\n",
     "\n",
-    "Prvým krokom v každom procese dátovej vedy je získanie údajov. Na tento účel použijeme knižnicu `requests`:\n"
+    "Prvým krokom v každom procese dátovej vedy je získavanie údajov. Na to použijeme knižnicu `requests`:\n"
    ],
    "metadata": {}
   },
@@ -68,43 +68,41 @@
    "source": [
     "## Krok 2: Transformácia údajov\n",
     "\n",
-    "Ďalším krokom je konverzia údajov do formy vhodnej na spracovanie. V našom prípade sme stiahli zdrojový kód HTML zo stránky a potrebujeme ho previesť na čistý text.\n",
+    "Ďalším krokom je previesť údaje do formy vhodnej na spracovanie. V našom prípade sme stiahli zdrojový kód HTML z stránky a potrebujeme ho previesť na čistý text.\n",
     "\n",
-    "Existuje mnoho spôsobov, ako to urobiť. Použijeme najjednoduchší zabudovaný objekt [HTMLParser](https://docs.python.org/3/library/html.parser.html) z Pythonu. Musíme zdediť triedu `HTMLParser` a definovať kód, ktorý bude zhromažďovať všetok text vo vnútri HTML značiek, okrem značiek `<script>` a `<style>`.\n"
+    "Existuje mnoho spôsobov, ako to dosiahnuť. Použijeme [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/), populárnu knižnicu v Pythone na parsovanie HTML. BeautifulSoup nám umožňuje cieliť na konkrétne HTML elementy, takže sa môžeme zamerať na hlavný obsah článku z Wikipédie a znížiť tak množstvo navigačných menu, bočných panelov, pätičiek a iného nerelevantného obsahu (aj keď niektorý štandardný text môže zostať).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Najskôr potrebujeme nainštalovať knižnicu BeautifulSoup pre analýzu HTML:\n"
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import sys\r\n",
+    "!{sys.executable} -m pip install beautifulsoup4"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 64,
    "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
+    "from bs4 import BeautifulSoup\r\n\r\n# Parse the HTML content\r\nsoup = BeautifulSoup(text, 'html.parser')\r\n\r\n# Extract only the main article content from Wikipedia\r\n# Wikipedia uses 'mw-parser-output' class for the main article content\r\ncontent = soup.find('div', class_='mw-parser-output')\r\n\r\ndef clean_wikipedia_content(content_node):\r\n    \"\"\"Remove common non-article elements from a Wikipedia content node.\"\"\"\r\n    # Strip jump links, navboxes, reference lists/superscripts, edit sections, TOC, sidebars, etc.\r\n    selectors = [\r\n        '.mw-jump-link',\r\n        '.navbox',\r\n        '.reflist',\r\n        'sup.reference',\r\n        '.mw-editsection',\r\n        '.hatnote',\r\n        '.metadata',\r\n        '.infobox',\r\n        '#toc',\r\n        '.toc',\r\n        '.sidebar',\r\n    ]\r\n    for selector in selectors:\r\n        for el in content_node.select(selector):\r\n            el.decompose()\r\n\r\nif content:\r\n    # Clean the content node to better approximate article text only.\r\n    clean_wikipedia_content(content)\r\n    text = content.get_text(separator=' ', strip=True)\r\n    print(text[:1000])\r\nelse:\r\n    print(\"Could not find main content. Using full page text.\")\r\n    text = soup.get_text(separator=' ', strip=True)\r\n    print(text[:1000])"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      " Data science - Wikipedia Data science From Wikipedia, the free encyclopedia Jump to navigation Jump to search Interdisciplinary field of study focused on deriving knowledge and insights from data Not to be confused with  information science . The existence of  Comet NEOWISE  (here depicted as a series of red dots) was discovered by analyzing  astronomical survey  data acquired by a  space telescope , the  Wide-field Infrared Survey Explorer . Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning ( classification  •  regression ) Decision trees Ensembles Bagging Boosting Random forest k -NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine \n"
+      "Data science From Wikipedia, the free encyclopedia Interdisciplinary field of study focused on deriving knowledge and insights from data Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain. Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.\n"
      ]
     }
    ],
@@ -115,9 +113,9 @@
    "source": [
     "## Krok 3: Získavanie poznatkov\n",
     "\n",
-    "Najdôležitejším krokom je premeniť naše dáta na formu, z ktorej môžeme získať poznatky. V našom prípade chceme extrahovať kľúčové slová z textu a zistiť, ktoré kľúčové slová majú väčší význam.\n",
+    "Najdôležitejším krokom je premeniť naše dáta na nejakú formu, z ktorej môžeme vyvodiť poznatky. V našom prípade chceme extrahovať kľúčové slová z textu a vidieť, ktoré kľúčové slová sú zmysluplnejšie.\n",
     "\n",
-    "Použijeme Python knižnicu s názvom [RAKE](https://github.com/aneesha/RAKE) na extrakciu kľúčových slov. Najprv si túto knižnicu nainštalujeme, ak ju ešte nemáme:\n"
+    "Použijeme knižnicu Python nazvanú [RAKE](https://github.com/aneesha/RAKE) na extrahovanie kľúčových slov. Najprv si túto knižnicu nainštalujeme, ak nie je prítomná: \n"
    ],
    "metadata": {}
   },
@@ -162,7 +160,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Hlavná funkcionalita je dostupná z objektu `Rake`, ktorý môžeme prispôsobiť pomocou niektorých parametrov. V našom prípade nastavíme minimálnu dĺžku kľúčového slova na 5 znakov, minimálnu frekvenciu kľúčového slova v dokumente na 3 a maximálny počet slov v kľúčovom slove na 2. Neváhajte experimentovať s inými hodnotami a pozorovať výsledok.\n"
+    "Hlavná funkčnosť je dostupná z objektu `Rake`, ktorý môžeme prispôsobiť pomocou niektorých parametrov. V našom prípade nastavíme minimálnu dĺžku kľúčového slova na 5 znakov, minimálnu frekvenciu kľúčového slova v dokumente na 3 a maximálny počet slov v kľúčovom slove na 2. Kľudne sa pohrajte s inými hodnotami a pozorujte výsledok.\n"
    ],
    "metadata": {}
   },
@@ -211,11 +209,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Získali sme zoznam pojmov spolu s ich priradenou mierou dôležitosti. Ako môžete vidieť, najrelevantnejšie disciplíny, ako sú strojové učenie a big data, sa nachádzajú na popredných miestach v zozname.\n",
+    "\n",
+    "Získali sme zoznam termínov spolu s priradenou mierou dôležitosti. Ako môžete vidieť, najrelevantnejšie disciplíny, ako strojové učenie a veľké dáta, sú v zozname na vrchných pozíciách.\n",
     "\n",
     "## Krok 4: Vizualizácia výsledku\n",
     "\n",
-    "Ľudia najlepšie interpretujú údaje vo vizuálnej podobe. Preto často dáva zmysel vizualizovať údaje, aby sme získali určité poznatky. Na jednoduché zobrazenie distribúcie kľúčových slov s ich relevanciou môžeme použiť knižnicu `matplotlib` v Pythone:\n"
+    "Ľudia dokážu najlepšie interpretovať dáta vo vizuálnej forme. Preto často dáva zmysel dáta vizualizovať, aby sme z nich mohli vyvodiť nejaké poznatky. Môžeme použiť knižnicu `matplotlib` v Pythone na zobrazenie jednoduchého rozdelenia kľúčových slov podľa ich dôležitosti:\n"
    ],
    "metadata": {}
   },
@@ -252,7 +251,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Existuje však ešte lepší spôsob, ako vizualizovať frekvencie slov - pomocou **Word Cloud**. Budeme musieť nainštalovať ďalšiu knižnicu na vykreslenie word cloudu z nášho zoznamu kľúčových slov.\n"
+    "Existuje však ešte lepší spôsob, ako vizualizovať frekvencie slov - pomocou **Word Cloud**. Budeme potrebovať nainštalovať ďalšiu knižnicu na vykreslenie word cloudu z nášho zoznamu kľúčových slov.\n"
    ],
    "metadata": {}
   },
@@ -268,7 +267,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Objekt `WordCloud` je zodpovedný za prijatie buď pôvodného textu, alebo vopred vypočítaného zoznamu slov s ich frekvenciami, a vráti obrázok, ktorý môže byť následne zobrazený pomocou `matplotlib`:\n"
+    "Objekt `WordCloud` je zodpovedný za prijatie buď pôvodného textu, alebo predpočítaného zoznamu slov s ich frekvenciami, a vracia obrázok, ktorý môže byť následne zobrazený pomocou `matplotlib`:\n"
    ],
    "metadata": {}
   },
@@ -312,7 +311,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Môžeme tiež odovzdať pôvodný text do `WordCloud` - pozrime sa, či dokážeme dosiahnuť podobný výsledok:\n"
+    "Môžeme tiež zadať pôvodný text do `WordCloud` - pozrime sa, či dokážeme dosiahnuť podobný výsledok:\n"
    ],
    "metadata": {}
   },
@@ -372,11 +371,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Môžete vidieť, že slovný mrak teraz vyzerá pôsobivejšie, ale obsahuje aj veľa šumu (napr. nesúvisiace slová ako `Retrieved on`). Taktiež získavame menej kľúčových slov, ktoré pozostávajú z dvoch slov, ako napríklad *data scientist* alebo *computer science*. Je to preto, že algoritmus RAKE odvádza oveľa lepšiu prácu pri výbere vhodných kľúčových slov z textu. Tento príklad ilustruje dôležitosť predspracovania a čistenia údajov, pretože jasný obraz na konci nám umožní robiť lepšie rozhodnutia.\n",
+    "Vidíte, že slovný mrak teraz vyzerá pôsobivejšie, ale tiež obsahuje veľa šumu (napr. nesúvisiace slová ako `Retrieved on`). Tiež dostávame menej kľúčových slov, ktoré pozostávajú z dvoch slov, ako napríklad *data scientist* alebo *computer science*. Je to preto, že algoritmus RAKE robí oveľa lepšiu prácu pri výbere dobrých kľúčových slov z textu. Tento príklad ilustruje dôležitosť predspracovania a čistenia údajov, pretože jasný obraz na konci nám umožní robiť lepšie rozhodnutia.\n",
     "\n",
-    "V tomto cvičení sme prešli jednoduchým procesom extrakcie významu z textu na Wikipédii vo forme kľúčových slov a slovného mraku. Tento príklad je pomerne jednoduchý, ale dobre demonštruje všetky typické kroky, ktoré dátový analytik podnikne pri práci s údajmi, od získavania údajov až po vizualizáciu.\n",
+    "V tomto cvičení sme prešli jednoduchým procesom extrahovania významu z textu Wikipédie vo forme kľúčových slov a slovného mraku. Tento príklad je pomerne jednoduchý, ale dobre ilustruje všetky typické kroky, ktoré dátový vedec podnikne pri práci s údajmi, počnúc získavaním údajov až po vizualizáciu.\n",
     "\n",
-    "V našom kurze si všetky tieto kroky podrobne rozoberieme.\n"
+    "V našom kurze budeme tieto kroky podrobne preberať.\n"
    ],
    "metadata": {}
   },
@@ -394,7 +393,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Zrieknutie sa zodpovednosti**:\nTento dokument bol preložený pomocou AI prekladateľskej služby [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, prosím, berte na vedomie, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Originálny dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z používania tohto prekladu nenesieme zodpovednosť.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -418,12 +417,6 @@
   },
   "interpreter": {
    "hash": "c28e7b6bf4e5b397b8288a85bf0a94ea8d3585ce2b01919feb195678ec71581b"
-  },
-  "coopTranslator": {
-   "original_hash": "50c0f0a5204a18405611cbe7e0fec56b",
-   "translation_date": "2025-09-02T10:06:11+00:00",
-   "source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
-   "language_code": "sk"
   }
  },
  "nbformat": 4,
diff --git a/translations/sk/1-Introduction/01-defining-data-science/solution/notebook.ipynb b/translations/sk/1-Introduction/01-defining-data-science/solution/notebook.ipynb
index 9582d0ec..27e0951b 100644
--- a/translations/sk/1-Introduction/01-defining-data-science/solution/notebook.ipynb
+++ b/translations/sk/1-Introduction/01-defining-data-science/solution/notebook.ipynb
@@ -5,15 +5,15 @@
    "source": [
     "# Výzva: Analýza textu o dátovej vede\n",
     "\n",
-    "> *V tomto notebooku experimentujeme s použitím rôznych URL - článok na Wikipédii o strojovom učení. Môžete si všimnúť, že na rozdiel od dátovej vedy tento článok obsahuje veľa odborných výrazov, čo robí analýzu problematickejšou. Musíme vymyslieť iný spôsob, ako vyčistiť dáta po extrakcii kľúčových slov, aby sme sa zbavili niektorých častých, ale bezvýznamných kombinácií slov.*\n",
+    "> *V tomto zošite experimentujeme s použitím rôznych URL - wikipedická stránka o strojovom učení. Vidíte, že na rozdiel od Dátovej vedy tento článok obsahuje veľa termínov, čo analýzu sťažuje. Potrebujeme prísť na iný spôsob, ako vyčistiť údaje po extrahovaní kľúčových slov, aby sme sa zbavili niektorých častých, ale bezvýznamných slovných spojení.*\n",
     "\n",
-    "V tomto príklade si urobíme jednoduché cvičenie, ktoré pokrýva všetky kroky tradičného procesu dátovej vedy. Nemusíte písať žiadny kód, stačí kliknúť na bunky nižšie, aby ste ich spustili a pozorovali výsledok. Ako výzvu vás povzbudzujeme, aby ste tento kód vyskúšali s rôznymi dátami.\n",
+    "V tomto príklade si urobíme jednoduché cvičenie, ktoré pokrýva všetky kroky tradičného procesu dátovej vedy. Nemusíte písať žiadny kód, môžete len kliknúť na bunky nižšie, spustiť ich a pozorovať výsledok. Ako výzvu vás povzbudzujeme vyskúšať tento kód s rôznymi dátami.\n",
     "\n",
     "## Cieľ\n",
     "\n",
-    "V tejto lekcii sme diskutovali o rôznych konceptoch súvisiacich s dátovou vedou. Skúsme objaviť viac súvisiacich konceptov pomocou **ťažby textu**. Začneme s textom o dátovej vede, extrahujeme z neho kľúčové slová a potom sa pokúsime vizualizovať výsledok.\n",
+    "V tejto lekcii sme diskutovali o rôznych konceptoch súvisiacich s Dátovou vedou. Skúsme objaviť viac súvisiacich konceptov pomocou **text mining**. Začneme s textom o Dátovej vede, extrahujeme z neho kľúčové slová a potom sa pokúsime výsledok vizualizovať.\n",
     "\n",
-    "Ako text použijem stránku o dátovej vede z Wikipédie:\n"
+    "Ako text použijem stránku o Dátovej vede z Wikipédie:\n"
    ],
    "metadata": {}
   },
@@ -35,9 +35,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Krok 1: Získanie údajov\n",
+    "## Krok 1: Získanie dát\n",
     "\n",
-    "Prvým krokom v každom procese dátovej vedy je získanie údajov. Na tento účel použijeme knižnicu `requests`:\n"
+    "Prvým krokom v každom procese dátovej vedy je získanie dát. Použijeme na to knižnicu `requests`:\n"
    ],
    "metadata": {}
   },
@@ -69,45 +69,43 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Krok 2: Transformácia údajov\n",
+    "## Krok 2: Transformácia dát\n",
     "\n",
-    "Ďalším krokom je konverzia údajov do formy vhodnej na spracovanie. V našom prípade sme stiahli zdrojový kód HTML zo stránky a potrebujeme ho previesť na čistý text.\n",
+    "Ďalším krokom je konvertovať dáta do formy vhodnej na spracovanie. V našom prípade sme stiahli HTML zdrojový kód zo stránky a potrebujeme ho previesť na jednoduchý text.\n",
     "\n",
-    "Existuje mnoho spôsobov, ako to dosiahnuť. My použijeme najjednoduchší vstavaný objekt [HTMLParser](https://docs.python.org/3/library/html.parser.html) z Pythonu. Musíme zdediť triedu `HTMLParser` a definovať kód, ktorý bude zhromažďovať všetok text vo vnútri HTML značiek, okrem značiek `<script>` a `<style>`.\n"
+    "Existuje mnoho spôsobov, ako to urobiť. Použijeme [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/), populárnu Python knižnicu na parsovanie HTML. BeautifulSoup nám umožňuje zamerať sa na konkrétne HTML prvky, takže sa môžeme sústrediť na hlavný obsah článku z Wikipédie a znížiť množstvo navigačných menu, postranných panelov, pätičiek a iného irelevantného obsahu (hoci niektoré štandardné texty môžu zostať).\n"
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Najprv potrebujeme nainštalovať knižnicu BeautifulSoup na analýzu HTML:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import sys\r\n",
+    "!{sys.executable} -m pip install beautifulsoup4"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
+    "from bs4 import BeautifulSoup\r\n\r\n# Parse the HTML content\r\nsoup = BeautifulSoup(text, 'html.parser')\r\n\r\n# Extract only the main article content from Wikipedia\r\n# Wikipedia uses 'mw-parser-output' class for the main article content\r\ncontent = soup.find('div', class_='mw-parser-output')\r\n\r\ndef clean_wikipedia_content(content_node):\r\n    \"\"\"Remove common non-article elements from a Wikipedia content node.\"\"\"\r\n    # Strip jump links, navboxes, reference lists/superscripts, edit sections, TOC, sidebars, etc.\r\n    selectors = [\r\n        '.mw-jump-link',\r\n        '.navbox',\r\n        '.reflist',\r\n        'sup.reference',\r\n        '.mw-editsection',\r\n        '.hatnote',\r\n        '.metadata',\r\n        '.infobox',\r\n        '#toc',\r\n        '.toc',\r\n        '.sidebar',\r\n    ]\r\n    for selector in selectors:\r\n        for el in content_node.select(selector):\r\n            el.decompose()\r\n\r\nif content:\r\n    # Clean the content node to better approximate article text only.\r\n    clean_wikipedia_content(content)\r\n    text = content.get_text(separator=' ', strip=True)\r\n    print(text[:1000])\r\nelse:\r\n    print(\"Could not find main content. Using full page text.\")\r\n    text = soup.get_text(separator=' ', strip=True)\r\n    print(text[:1000])"
    ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      " Machine learning - Wikipedia Machine learning From Wikipedia, the free encyclopedia Jump to navigation Jump to search Study of algorithms that improve automatically through experience For the journal, see  Machine Learning (journal) . \"Statistical learning\" redirects here. For statistical learning in linguistics, see  statistical learning in language acquisition . Part of a series on Artificial intelligence Major goals Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms Philosophy Ethics Existential risk Turing test Chinese room Control problem Friendly AI History Timeline Progress AI winter Technology Applications Projects Programming languages Glossary Glossary v t e Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection Data Cleaning AutoML Associ\n"
+      "Machine learning From Wikipedia, the free encyclopedia Study of algorithms that improve automatically through experience Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.\n"
      ]
     }
    ],
@@ -118,9 +116,9 @@
    "source": [
     "## Krok 3: Získavanie poznatkov\n",
     "\n",
-    "Najdôležitejším krokom je premeniť naše dáta na formu, z ktorej môžeme získať poznatky. V našom prípade chceme extrahovať kľúčové slová z textu a zistiť, ktoré kľúčové slová majú väčší význam.\n",
+    "Najdôležitejším krokom je premeniť naše dáta na nejakú formu, z ktorej môžeme čerpať poznatky. V našom prípade chceme z textu vyťažiť kľúčové slová a pozrieť sa, ktoré kľúčové slová majú väčší význam.\n",
     "\n",
-    "Použijeme Python knižnicu s názvom [RAKE](https://github.com/aneesha/RAKE) na extrakciu kľúčových slov. Najprv si túto knižnicu nainštalujeme, ak ešte nie je prítomná:\n"
+    "Použijeme knižnicu Python nazývanú [RAKE](https://github.com/aneesha/RAKE) na extrahovanie kľúčových slov. Najprv si nainštalujme túto knižnicu, pokiaľ nie je nainštalovaná: \n"
    ],
    "metadata": {}
   },
@@ -172,7 +170,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Hlavná funkcionalita je dostupná z objektu `Rake`, ktorý môžeme prispôsobiť pomocou niektorých parametrov. V našom prípade nastavíme minimálnu dĺžku kľúčového slova na 5 znakov, minimálnu frekvenciu kľúčového slova v dokumente na 3 a maximálny počet slov v kľúčovom slove na 2. Neváhajte experimentovať s inými hodnotami a pozorovať výsledok.\n"
+    "Hlavná funkcionalita je dostupná z objektu `Rake`, ktorý môžeme prispôsobiť pomocou niektorých parametrov. V našom prípade nastavíme minimálnu dĺžku kľúčového slova na 5 znakov, minimálnu frekvenciu kľúčového slova v dokumente na 3 a maximálny počet slov v kľúčovom slove na 2. Neváhajte si pohrať s ostatnými hodnotami a pozorovať výsledok.\n"
    ],
    "metadata": {}
   },
@@ -353,11 +351,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Získali sme zoznam pojmov spolu s ich priradenou mierou dôležitosti. Ako môžete vidieť, najrelevantnejšie disciplíny, ako sú strojové učenie a big data, sa nachádzajú na popredných miestach v zozname.\n",
+    "Získali sme zoznam termínov spolu s priradenou mierou dôležitosti. Ako vidíte, najrelevantnejšie disciplíny, ako napríklad strojové učenie a veľké dáta, sú prítomné v zozname na popredných pozíciách.\n",
     "\n",
     "## Krok 4: Vizualizácia výsledku\n",
     "\n",
-    "Ľudia najlepšie interpretujú údaje vo vizuálnej podobe. Preto často dáva zmysel vizualizovať údaje, aby sme získali určité poznatky. Na jednoduché zobrazenie distribúcie kľúčových slov s ich relevanciou môžeme použiť knižnicu `matplotlib` v Pythone:\n"
+    "Ľudia dokážu najlepšie interpretovať dáta vo vizuálnej podobe. Preto často dáva zmysel dáta vizualizovať, aby sme získali nejaké poznatky. Môžeme použiť knižnicu `matplotlib` v Pythone na zobrazenie jednoduchej distribúcie kľúčových slov s ich relevantnosťou:\n"
    ],
    "metadata": {}
   },
@@ -392,7 +390,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Existuje však ešte lepší spôsob, ako vizualizovať frekvencie slov - pomocou **Word Cloud**. Budeme musieť nainštalovať ďalšiu knižnicu na vykreslenie word cloudu z nášho zoznamu kľúčových slov.\n"
+    "Existuje však ešte lepší spôsob, ako vizualizovať frekvenciu slov - pomocou **Word Cloud** (slovného mraku). Na vykreslenie slovného mraku z nášho zoznamu kľúčových slov budeme potrebovať nainštalovať ďalšiu knižnicu.\n"
    ],
    "metadata": {}
   },
@@ -408,7 +406,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Objekt `WordCloud` je zodpovedný za prijatie buď pôvodného textu, alebo vopred vypočítaného zoznamu slov s ich frekvenciami, a vráti obrázok, ktorý môže byť následne zobrazený pomocou `matplotlib`:\n"
+    "Objekt `WordCloud` je zodpovedný za prijatie buď pôvodného textu, alebo predpočítaného zoznamu slov s ich frekvenciami, a vracia obrázok, ktorý je možné následne zobraziť pomocou `matplotlib`:\n"
    ],
    "metadata": {}
   },
@@ -441,7 +439,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Pôvodný text môžeme tiež zadať do `WordCloud` - pozrime sa, či dokážeme dosiahnuť podobný výsledok:\n"
+    "Môžeme tiež odovzdať pôvodný text do `WordCloud` - pozrime sa, či dokážeme získať podobný výsledok:\n"
    ],
    "metadata": {}
   },
@@ -490,11 +488,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Môžete vidieť, že slovný mrak teraz vyzerá pôsobivejšie, ale obsahuje aj veľa šumu (napr. nesúvisiace slová ako `Retrieved on`). Taktiež získavame menej kľúčových slov, ktoré pozostávajú z dvoch slov, ako napríklad *data scientist* alebo *computer science*. Je to preto, že algoritmus RAKE odvádza oveľa lepšiu prácu pri výbere kvalitných kľúčových slov z textu. Tento príklad ilustruje dôležitosť predspracovania a čistenia dát, pretože jasný obraz na konci nám umožní robiť lepšie rozhodnutia.\n",
+    "Vidíte, že slovný oblak teraz vyzerá pôsobivejšie, ale tiež obsahuje veľa šumu (napr. nesúvisiace slová ako `Retrieved on`). Tiež získavame menej kľúčových slov, ktoré pozostávajú z dvoch slov, ako napríklad *data scientist* alebo *computer science*. Je to preto, že algoritmus RAKE robí oveľa lepšiu prácu pri výbere dobrých kľúčových slov z textu. Tento príklad ilustruje význam predspracovania a čistenia dát, pretože jasný obraz na konci nám umožní robiť lepšie rozhodnutia.\n",
     "\n",
-    "V tomto cvičení sme prešli jednoduchým procesom extrakcie významu z textu na Wikipédii vo forme kľúčových slov a slovného mraku. Tento príklad je pomerne jednoduchý, ale dobre demonštruje všetky typické kroky, ktoré dátový vedec podnikne pri práci s dátami, od získavania dát až po vizualizáciu.\n",
+    "V tomto cvičení sme prešli jednoduchým procesom extrakcie významu z wikipediálneho textu vo forme kľúčových slov a slovného oblaku. Tento príklad je pomerne jednoduchý, ale dobre demonštruje všetky typické kroky, ktoré dátový analytik podnikne pri práci s dátami, počnúc získavaním dát až po vizualizáciu.\n",
     "\n",
-    "V našom kurze budeme diskutovať o všetkých týchto krokoch podrobne.\n"
+    "V našom kurze budeme všetky tieto kroky podrobne rozoberať.\n"
    ],
    "metadata": {}
   },
@@ -502,7 +500,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Upozornenie**:\nTento dokument bol preložený pomocou AI prekladateľskej služby [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, vezmite prosím na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne výklady vyplývajúce z použitia tohto prekladu.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -526,12 +524,6 @@
   },
   "interpreter": {
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  },
-  "coopTranslator": {
-   "original_hash": "dbdf34788efab64e5d817f1df38965a8",
-   "translation_date": "2025-09-02T10:20:20+00:00",
-   "source_file": "1-Introduction/01-defining-data-science/solution/notebook.ipynb",
-   "language_code": "sk"
   }
  },
  "nbformat": 4,
diff --git a/translations/sk/1-Introduction/04-stats-and-probability/README.md b/translations/sk/1-Introduction/04-stats-and-probability/README.md
index 7b648a68..ba8d3c02 100644
--- a/translations/sk/1-Introduction/04-stats-and-probability/README.md
+++ b/translations/sk/1-Introduction/04-stats-and-probability/README.md
@@ -6,7 +6,7 @@
 
 Teória štatistiky a pravdepodobnosti sú dve úzko prepojené oblasti matematiky, ktoré sú veľmi dôležité pre dátovú vedu. Dá sa pracovať s dátami aj bez hlbokých matematických znalostí, ale je lepšie poznať aspoň základné koncepty. Tu vám predstavíme krátky úvod, ktorý vám pomôže začať.
 
-[![Úvodné video](../../../../translated_images/sk/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw)
+[![Úvodné video](../../../../translated_images/sk/video-prob-and-stats.e4282e5efa2f2543.webp)](https://youtu.be/Z5Zy85g4Yjw)
 
 ## [Kvíz pred prednáškou](https://ff-quizzes.netlify.app/en/ds/quiz/6)
 
@@ -30,7 +30,7 @@ Je ťažšie opísať pravdepodobnostné rozdelenie spojitej premennej, ktorej h
 
 Môžeme hovoriť iba o pravdepodobnosti, že premenná spadne do daného intervalu hodnôt, napr. P(t<sub>1</sub>≤X<t<sub>2</sub>). V tomto prípade je pravdepodobnostné rozdelenie opísané pomocou **hustoty pravdepodobnosti** p(x), pričom
 
-![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/sk/probability-density.a8aad29f17a14afb519b407c7b6edeb9f3f9aa5f69c9e6d9445f604e5f8a2bf7.png)
+![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/sk/probability-density.a8aad29f17a14afb.webp)
 
 Spojitý analóg rovnomerného rozdelenia sa nazýva **spojité rovnomerné rozdelenie**, ktoré je definované na konečnom intervale. Pravdepodobnosť, že hodnota X spadne do intervalu dĺžky l, je úmerná l a stúpa až na 1.
 
@@ -73,11 +73,11 @@ Keď analyzujeme dáta z reálneho sveta, často nie sú náhodnými premennými
 
 Tu je boxplot zobrazujúci priemer, medián a kvartily pre naše dáta:
 
-![Boxplot váh](../../../../translated_images/sk/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png)
+![Boxplot váh](../../../../translated_images/sk/weight-boxplot.1dbab1c03af26f8a.webp)
 
 Keďže naše dáta obsahujú informácie o rôznych hráčskych **rolách**, môžeme tiež vytvoriť boxplot podľa rolí - umožní nám to získať predstavu o tom, ako sa hodnoty parametrov líšia medzi rolami. Tentokrát budeme uvažovať o výške:
 
-![Boxplot podľa rolí](../../../../translated_images/sk/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png)
+![Boxplot podľa rolí](../../../../translated_images/sk/boxplot_byrole.036b27a1c3f52d42.webp)
 
 Tento diagram naznačuje, že priemerná výška hráčov na prvej méte je vyššia ako výška hráčov na druhej méte. Neskôr v tejto lekcii sa naučíme, ako môžeme túto hypotézu formálnejšie otestovať a ako ukázať, že naše dáta sú štatisticky významné na jej preukázanie.
 
@@ -85,7 +85,7 @@ Tento diagram naznačuje, že priemerná výška hráčov na prvej méte je vyš
 
 Na zistenie, aké je rozdelenie našich dát, môžeme vytvoriť graf nazývaný **histogram**. X-os bude obsahovať počet rôznych intervalov váh (tzv. **binov**), a vertikálna os bude ukazovať počet prípadov, keď naša vzorka náhodnej premennej spadla do daného intervalu.
 
-![Histogram reálnych dát](../../../../translated_images/sk/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png)
+![Histogram reálnych dát](../../../../translated_images/sk/weight-histogram.bfd00caf7fc30b14.webp)
 
 Z tohto histogramu vidíte, že všetky hodnoty sú sústredené okolo určitého priemeru váhy a čím ďalej ideme od tejto váhy, tým menej hodnôt tejto váhy sa vyskytuje. T.j., je veľmi nepravdepodobné, že váha hráča baseballu bude veľmi odlišná od priemernej váhy. Rozptyl váh ukazuje, do akej miery sa váhy pravdepodobne líšia od priemeru.
 
@@ -102,7 +102,7 @@ samples = np.random.normal(mean,std,1000)
 
 Ak vykreslíme histogram vygenerovaných vzoriek, uvidíme obrázok veľmi podobný tomu, ktorý je uvedený vyššie. A ak zvýšime počet vzoriek a počet binov, môžeme vytvoriť obrázok normálneho rozdelenia, ktorý je bližší ideálu:
 
-![Normálne rozdelenie s priemerom=0 a smerodajnou odchýlkou=1](../../../../translated_images/sk/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png)
+![Normálne rozdelenie s priemerom=0 a smerodajnou odchýlkou=1](../../../../translated_images/sk/normal-histogram.dfae0d67c202137d.webp)
 
 *Normálne rozdelenie s priemerom=0 a smerodajnou odchýlkou=1*
 
@@ -224,7 +224,7 @@ array([[1.        , 0.52959196],
 
 V našom prípade hodnota 0.53 indikuje, že existuje určitá korelácia medzi hmotnosťou a výškou osoby. Môžeme tiež vytvoriť scatter plot jednej hodnoty proti druhej, aby sme videli vzťah vizuálne:
 
-![Vzťah medzi hmotnosťou a výškou](../../../../translated_images/sk/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png)
+![Vzťah medzi hmotnosťou a výškou](../../../../translated_images/sk/weight-height-relationship.3f06bde4ca2aba99.webp)
 
 > Viac príkladov korelácie a kovariancie nájdete v [sprievodnom notebooku](notebook.ipynb).
 
diff --git a/translations/sk/1-Introduction/README.md b/translations/sk/1-Introduction/README.md
index 6857561d..c255cb69 100644
--- a/translations/sk/1-Introduction/README.md
+++ b/translations/sk/1-Introduction/README.md
@@ -1,6 +1,6 @@
 # Úvod do dátovej vedy
 
-![dáta v akcii](../../../translated_images/sk/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)
+![dáta v akcii](../../../translated_images/sk/data.48e22bb7617d8d92.webp)
 > Fotografia od <a href="https://unsplash.com/@dawson2406?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Stephena Dawsona</a> na <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 V týchto lekciách objavíte, ako je definovaná dátová veda, a oboznámite sa s etickými otázkami, ktoré musí dátový vedec zohľadniť. Taktiež sa dozviete, ako sú definované dáta, a získate základné poznatky o štatistike a pravdepodobnosti, ktoré sú jadrom akademických oblastí dátovej vedy.
diff --git a/translations/sk/2-Working-With-Data/07-python/README.md b/translations/sk/2-Working-With-Data/07-python/README.md
index a6ee2f8c..26e6acad 100644
--- a/translations/sk/2-Working-With-Data/07-python/README.md
+++ b/translations/sk/2-Working-With-Data/07-python/README.md
@@ -4,7 +4,7 @@
 | :-------------------------------------------------------------------------------------------------------: |
 |                 Práca s Pythonom - _Sketchnote od [@nitya](https://twitter.com/nitya)_                   |
 
-[![Úvodné video](../../../../translated_images/sk/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y)
+[![Úvodné video](../../../../translated_images/sk/video-ds-python.245247dc811db8e4.webp)](https://youtu.be/dZjWOGbsN4Y)
 
 Databázy ponúkajú veľmi efektívne spôsoby ukladania dát a ich dotazovania pomocou dotazovacích jazykov, no najflexibilnejším spôsobom spracovania dát je napísanie vlastného programu na manipuláciu s dátami. V mnohých prípadoch by bolo efektívnejšie použiť dotaz do databázy. Avšak v prípadoch, keď je potrebné zložitejšie spracovanie dát, to nie je možné jednoducho vykonať pomocou SQL. 
 Spracovanie dát je možné programovať v akomkoľvek programovacom jazyku, ale existujú určité jazyky, ktoré sú na prácu s dátami na vyššej úrovni. Dátoví vedci zvyčajne preferujú jeden z nasledujúcich jazykov:
@@ -64,7 +64,7 @@ print(f"Length of index is {len(idx)}")
 items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)
 items_sold.plot()
 ```
-![Graf časovej série](../../../../translated_images/sk/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png)
+![Graf časovej série](../../../../translated_images/sk/timeseries-1.80de678ab1cf727e.webp)
 
 Teraz predpokladajme, že každý týždeň organizujeme párty pre priateľov a berieme na párty ďalších 10 balení zmrzliny. Môžeme vytvoriť ďalšiu sériu, indexovanú podľa týždňov, aby sme to ukázali:
 ```python
@@ -75,7 +75,7 @@ Keď sčítame dve série, dostaneme celkový počet:
 total_items = items_sold.add(additional_items,fill_value=0)
 total_items.plot()
 ```
-![Graf časovej série](../../../../translated_images/sk/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png)
+![Graf časovej série](../../../../translated_images/sk/timeseries-2.aae51d575c55181c.webp)
 
 > **Poznámka**: Nepoužívame jednoduchú syntax `total_items+additional_items`. Ak by sme to urobili, dostali by sme veľa hodnôt `NaN` (*Not a Number*) v výslednej sérii. Je to preto, že pre niektoré indexové body v sérii `additional_items` chýbajú hodnoty, a sčítanie `NaN` s čímkoľvek vedie k `NaN`. Preto musíme počas sčítania špecifikovať parameter `fill_value`.
 
@@ -84,7 +84,7 @@ Pri časových sériách môžeme tiež **preukladať** sériu s rôznymi časov
 monthly = total_items.resample("1M").mean()
 ax = monthly.plot(kind='bar')
 ```
-![Mesačné priemery časovej série](../../../../translated_images/sk/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png)
+![Mesačné priemery časovej série](../../../../translated_images/sk/timeseries-3.f3147cbc8c624881.webp)
 
 ### DataFrame
 
@@ -210,7 +210,7 @@ Prvým problémom, na ktorý sa zameriame, je modelovanie epidémie šírenia CO
 
 Keďže chceme demonštrovať, ako pracovať s dátami, pozývame vás otvoriť [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) a prečítať si ho od začiatku do konca. Môžete tiež spustiť bunky a splniť niektoré výzvy, ktoré sme pre vás nechali na konci.
 
-![COVID Spread](../../../../translated_images/sk/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png)
+![COVID Spread](../../../../translated_images/sk/covidspread.f3d131c4f1d260ab.webp)
 
 > Ak neviete, ako spustiť kód v Jupyter Notebooku, pozrite si [tento článok](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
 
@@ -232,7 +232,7 @@ Kompletný príklad analýzy tohto datasetu pomocou [Text Analytics for Health](
 
 Otvorte [`notebook-papers.ipynb`](notebook-papers.ipynb) a prečítajte si ho od začiatku do konca. Môžete tiež spustiť bunky a splniť niektoré výzvy, ktoré sme pre vás nechali na konci.
 
-![Covid Medical Treatment](../../../../translated_images/sk/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png)
+![Covid Medical Treatment](../../../../translated_images/sk/covidtreat.b2ba59f57ca45fbc.webp)
 
 ## Spracovanie obrazových dát
 
diff --git a/translations/sk/2-Working-With-Data/07-python/notebook-covidspread.ipynb b/translations/sk/2-Working-With-Data/07-python/notebook-covidspread.ipynb
index a01dd65f..d693256e 100644
--- a/translations/sk/2-Working-With-Data/07-python/notebook-covidspread.ipynb
+++ b/translations/sk/2-Working-With-Data/07-python/notebook-covidspread.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "## Načítanie údajov\n",
     "\n",
-    "Budeme používať údaje o osobách nakazených COVID-19, ktoré poskytuje [Centrum pre systémy vedy a inžinierstva](https://systems.jhu.edu/) (CSSE) na [Univerzite Johnsa Hopkinsa](https://jhu.edu/). Dataset je dostupný v [tomto GitHub repozitári](https://github.com/CSSEGISandData/COVID-19).\n"
+    "Použijeme údaje o infikovaných COVID-19, ktoré poskytuje [Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) na [Johns Hopkins University](https://jhu.edu/). Dataset je dostupný v [tomto GitHub repozitári](https://github.com/CSSEGISandData/COVID-19).\n"
    ]
   },
   {
@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Môžeme načítať najnovšie údaje priamo z GitHubu pomocou `pd.read_csv`. Ak z nejakého dôvodu údaje nie sú dostupné, vždy môžete použiť kópiu dostupnú lokálne v priečinku `data` - stačí odkomentovať riadok nižšie, ktorý definuje `base_url`:\n"
+    "Môžeme načítať najnovšie údaje priamo z GitHubu pomocou `pd.read_csv`. Ak z nejakého dôvodu údaje nie sú dostupné, môžete vždy použiť kópiu dostupnú lokálne v priečinku `data` - stačí odkomentovať riadok nižšie, ktorý definuje `base_url`:\n"
    ]
   },
   {
@@ -47,7 +47,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Teraz načítajme údaje o infikovaných jednotlivcoch a pozrime sa, ako údaje vyzerajú:\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -263,7 +265,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Môžeme vidieť, že každý riadok tabuľky definuje počet infikovaných osôb pre každú krajinu a/alebo provinciu a stĺpce zodpovedajú dátumom. Podobné tabuľky je možné načítať pre iné údaje, ako napríklad počet vyliečených a počet úmrtí.\n"
+    "Môžeme vidieť, že každý riadok tabuľky definuje počet infikovaných jednotlivcov pre každú krajinu a/alebo provinciu a stĺpce zodpovedajú dátumom. Podobné tabuľky je možné načítať pre ďalšie údaje, ako je počet vyliečených a počet úmrtí.\n"
    ]
   },
   {
@@ -282,7 +284,7 @@
    "source": [
     "## Pochopenie údajov\n",
     "\n",
-    "Z vyššie uvedenej tabuľky nie je úloha stĺpca provincie jasná. Pozrime sa na rôzne hodnoty, ktoré sa nachádzajú v stĺpci `Province/State`:\n"
+    "Z vyššie uvedenej tabuľky nie je jasná úloha stĺpca provincia. Pozrime sa na rôzne hodnoty, ktoré sa nachádzajú v stĺpci `Province/State`:\n"
    ]
   },
   {
@@ -319,7 +321,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Z názvov môžeme vyvodiť, že krajiny ako Austrália a Čína majú podrobnejšie rozdelenie podľa provincií. Pozrime sa na informácie o Číne, aby sme videli príklad:\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -1317,9 +1321,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Predspracovanie údajov\n",
+    "## Predspracovanie dát\n",
     "\n",
-    "Nemáme záujem rozdeľovať krajiny na menšie územia, preto najskôr odstránime toto rozdelenie a spojíme informácie o všetkých územiach dohromady, aby sme získali údaje pre celú krajinu. Toto je možné dosiahnuť pomocou `groupby`:\n"
+    "Nemáme záujem rozdeľovať krajiny na ďalšie územia, preto sa najprv zbavíme tohto rozdelenia a pridáme informácie pre všetky územia spolu, aby sme získali údaje za celú krajinu. Toto je možné urobiť pomocou `groupby`:\n"
    ]
   },
   {
@@ -1574,7 +1578,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Môžete vidieť, že vďaka použitiu `groupby` sú teraz všetky DataFrame indexované podľa Country/Region. Môžeme teda pristupovať k údajom pre konkrétnu krajinu pomocou `.loc`:|\n"
+    "Vidíte, že vďaka použitiu `groupby` sú teraz všetky DataFrame indexované podľa Krajiny/Regiónu. Môžeme teda pristupovať k údajom pre konkrétnu krajinu pomocou `.loc`:|\n"
    ]
   },
   {
@@ -1583,20 +1587,19 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
-    "infected.loc['US'][2:].plot()\n",
-    "recovered.loc['US'][2:].plot()\n",
+    "infected.loc['US'][3:].plot()\n",
+    "recovered.loc['US'][3:].plot()\n",
     "plt.show()"
    ]
   },
@@ -1604,7 +1607,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Poznámka** ako používame `[2:]` na odstránenie prvých dvoch prvkov sekvencie, ktoré obsahujú geolokáciu krajiny. Tieto dva stĺpce môžeme tiež úplne vynechať:\n"
+    "> **Poznámka** ako používame `[3:]` na odstránenie prvých troch prvkov sekvencie, ktoré obsahujú nemetadáta dátumu (stĺpce Provincia/Štát a geolokácia). Tieto tri stĺpce môžeme tiež úplne odstrániť:\n"
    ]
   },
   {
@@ -1613,18 +1616,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "infected.drop(columns=['Lat','Long'],inplace=True)\n",
-    "recovered.drop(columns=['Lat','Long'],inplace=True)\n",
-    "deaths.drop(columns=['Lat','Long'],inplace=True)"
+    "infected.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "recovered.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
+    "deaths.drop(columns=['Lat','Long','Province/State'],inplace=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Skúmanie údajov\n",
+    "## Skúmanie dát\n",
     "\n",
-    "Poďme sa teraz zamerať na skúmanie konkrétnej krajiny. Vytvorme rámec, ktorý obsahuje údaje o infekciách zoradené podľa dátumu:\n"
+    "Teraz sa pozrime na skúmanie konkrétnej krajiny. Vytvorme rámec, ktorý obsahuje údaje o infekciách zoradené podľa dátumu:\n"
    ]
   },
   {
@@ -1754,7 +1757,7 @@
    ],
    "source": [
     "def mkframe(country):\n",
-    "    df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n",
+    "    df = pd.DataFrame({ 'infected' : infected.loc[country],\n",
     "                        'recovered' : recovered.loc[country],\n",
     "                        'deaths' : deaths.loc[country]})\n",
     "    df.index = pd.to_datetime(df.index)\n",
@@ -1790,7 +1793,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Poďme teraz vypočítať počet nových nakazených ľudí každý deň. To nám umožní vidieť rýchlosť, akou pandémia postupuje. Najjednoduchší spôsob, ako to urobiť, je použiť `diff`:\n"
+    "Teraz vypočítajme počet nových nakazených ľudí každý deň. To nám umožní vidieť rýchlosť, akou sa pandémia šíri. Najjednoduchší spôsob, ako to urobiť, je použiť `diff`:\n"
    ]
   },
   {
@@ -1820,7 +1823,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Môžeme vidieť vysoké výkyvy v údajoch. Pozrime sa bližšie na jeden z mesiacov:\n"
+    "Môžeme vidieť veľké výkyvy v údajoch. Pozrime sa bližšie na jeden z mesiacov:\n"
    ]
   },
   {
@@ -1849,7 +1852,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Je zrejmé, že v údajoch sú týždenné výkyvy. Keďže chceme vidieť trendy, dáva zmysel vyhladiť krivku výpočtom kĺzavého priemeru (t. j. pre každý deň vypočítame priemernú hodnotu za predchádzajúcich niekoľko dní):\n"
+    "Je zrejmé, že v údajoch sú týždenné výkyvy. Keďže chceme vidieť trendy, dáva zmysel vyhladiť krivku výpočtom kĺzavého priemeru (t. j. pre každý deň vypočítame priemernú hodnotu z niekoľkých predchádzajúcich dní):\n"
    ]
   },
   {
@@ -1879,7 +1882,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Aby sme mohli porovnať niekoľko krajín, mohli by sme zohľadniť populáciu krajiny a porovnať percento infikovaných osôb vzhľadom na populáciu krajiny. Aby sme získali populáciu krajiny, načítajme dataset krajín:\n"
+    "Aby sme mohli porovnať niekoľko krajín, môžeme chcieť zohľadniť populáciu krajiny a porovnať percento nakazených osôb vzhľadom na populáciu krajiny. Na získanie populácie krajiny načítajme dataset krajín:\n"
    ]
   },
   {
@@ -2150,7 +2153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pretože tento súbor údajov obsahuje informácie o krajinách aj provinciách, na získanie populácie celej krajiny musíme byť trochu šikovnejší:\n"
+    "Keďže táto databáza obsahuje informácie o krajinách aj provinciách, aby sme získali populáciu celej krajiny, musíme byť trochu šikovní:\n"
    ]
   },
   {
@@ -2260,12 +2263,12 @@
    "source": [
     "## Výpočet $R_t$\n",
     "\n",
-    "Aby sme zistili, aká nákazlivá je choroba, pozrieme sa na **základné reprodukčné číslo** $R_0$, ktoré udáva počet ľudí, ktorých by nakazená osoba ďalej nakazila. Ak je $R_0$ väčšie ako 1, epidémia sa pravdepodobne rozšíri.\n",
+    "Aby sme videli, ako infekčná je choroba, pozrieme sa na **základné reprodukčné číslo** $R_0$, ktoré udáva počet ľudí, ktorých nakazí jedna infikovaná osoba. Keď je $R_0$ väčšie ako 1, epidémia sa pravdepodobne rozšíri.\n",
     "\n",
-    "$R_0$ je vlastnosť samotnej choroby a nezohľadňuje niektoré ochranné opatrenia, ktoré môžu ľudia prijať na spomalenie pandémie. Počas priebehu pandémie môžeme odhadnúť reprodukčné číslo $R_t$ v ľubovoľnom čase $t$. Ukázalo sa, že toto číslo možno približne odhadnúť tak, že vezmeme 8-dňové okno a vypočítame $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
-    "kde $I_t$ je počet novonakazených osôb v deň $t$.\n",
+    "$R_0$ je vlastnosť samotnej choroby a nezohľadňuje niektoré ochranné opatrenia, ktoré môžu ľudia prijať na spomalenie pandémie. Počas priebehu pandémie môžeme odhadnúť reprodukčné číslo $R_t$ v ľubovoľnom čase $t$. Bolo preukázané, že toto číslo možno približne odhadnúť tak, že vezmeme okno o dĺžke 8 dní a vypočítame $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
+    "kde $I_t$ je počet novo infikovaných jedincov v deň $t$.\n",
     "\n",
-    "Poďme vypočítať $R_t$ pre naše pandemické údaje. Na tento účel vezmeme posuvné okno 8 hodnôt `ninfected` a použijeme funkciu na výpočet vyššie uvedeného pomeru:\n"
+    "Poďme vypočítať $R_t$ pre naše dáta o pandémii. Na to použijeme posuvné okno s 8 hodnotami `ninfected` a aplikujeme funkciu na výpočet vyššie uvedeného pomeru:\n"
    ]
   },
   {
@@ -2295,9 +2298,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Môžete si všimnúť, že v grafe sú niektoré medzery. Tie môžu byť spôsobené buď hodnotami `NaN`, alebo prítomnosťou hodnôt `inf` v dátovej sade. `inf` môže vzniknúť delením nulou a `NaN` môže naznačovať chýbajúce údaje alebo nedostupnosť údajov na výpočet výsledku (napríklad na úplnom začiatku nášho rámca, kde ešte nie je dostupné kĺzavé okno so šírkou 8). Aby bol graf krajší, musíme tieto hodnoty vyplniť pomocou funkcií `replace` a `fillna`.\n",
+    "Môžete vidieť, že v grafe sú niektoré medzery. Tie môžu byť spôsobené buď hodnotami `NaN`, alebo prítomnosťou hodnôt `inf` v datasetu. Hodnoty `inf` môžu byť spôsobené delením nulou a `NaN` môže indikovať chýbajúce údaje alebo nedostupné údaje na výpočet výsledku (ako na úplnom začiatku nášho rámca, kde ešte nie je k dispozícii kĺzavé okno s šírkou 8). Aby bol graf krajší, potrebujeme tieto hodnoty vyplniť pomocou funkcií `replace` a `fillna`.\n",
     "\n",
-    "Pozrime sa ďalej na začiatok pandémie. Taktiež obmedzíme hodnoty osi y tak, aby zobrazovali iba hodnoty pod 6, aby sme dosiahli lepšiu prehľadnosť, a nakreslíme horizontálnu čiaru na hodnote 1.\n"
+    "Pozrime sa bližšie na začiatok pandémie. Tiež obmedzíme hodnoty na y-osi len na hodnoty pod 6, aby sme lepšie videli, a nakreslíme horizontálnu čiaru na úrovni 1.\n"
    ]
   },
   {
@@ -2327,7 +2330,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Ďalším zaujímavým ukazovateľom pandémie je **derivát**, alebo **denný rozdiel** v nových prípadoch. Umožňuje nám jasne vidieť, kedy pandémia rastie alebo klesá.\n"
+   ]
   },
   {
    "cell_type": "code",
@@ -2355,7 +2360,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vzhľadom na skutočnosť, že v údajoch dochádza k mnohým výkyvom spôsobeným hlásením, dáva zmysel vyhladiť krivku použitím kĺzavého priemeru, aby sme získali celkový obraz. Zamerajme sa opäť na prvé mesiace pandémie:\n"
+    "Vzhľadom na to, že existuje veľa výkyvov v údajoch spôsobených vykazovaním, má zmysel vyhladiť krivku spustením kĺzavého priemeru, aby sme získali celkový obraz. Zamerajme sa opäť na prvé mesiace pandémie:\n"
    ]
   },
   {
@@ -2387,23 +2392,23 @@
    "source": [
     "## Výzva\n",
     "\n",
-    "Teraz je čas, aby ste sa viac pohrali s kódom a dátami! Tu je niekoľko návrhov, s ktorými môžete experimentovať:\n",
-    "* Pozrite sa na šírenie pandémie v rôznych krajinách.\n",
-    "* Vykreslite grafy $R_t$ pre niekoľko krajín na jeden graf na porovnanie, alebo vytvorte viacero grafov vedľa seba.\n",
-    "* Skúmajte, ako počet úmrtí a uzdravení koreluje s počtom nakazených prípadov.\n",
-    "* Pokúste sa zistiť, ako dlho typické ochorenie trvá, vizuálnym porovnaním miery infekcie a miery úmrtí a hľadaním určitých anomálií. Možno budete musieť preskúmať rôzne krajiny, aby ste to zistili.\n",
-    "* Vypočítajte mieru úmrtnosti a ako sa mení v priebehu času. Možno budete chcieť zohľadniť dĺžku ochorenia v dňoch a posunúť jednu časovú sériu pred vykonaním výpočtov.\n"
+    "Teraz je čas, aby ste si viac zahrali s kódom a dátami! Tu je niekoľko návrhov, s ktorými môžete experimentovať:  \n",
+    "* Pozrite sa na šírenie pandémie v rôznych krajinách.  \n",
+    "* Vykreslite grafy $R_t$ pre niekoľko krajín na jednom grafe pre porovnanie alebo vytvorte niekoľko grafov vedľa seba.  \n",
+    "* Pozrite sa, ako počet úmrtí a uzdravení koreluje s počtom infikovaných prípadov.  \n",
+    "* Pokúste sa zistiť, ako dlho typická choroba trvá, vizuálne korelujúc mieru infekcie a mieru úmrtí a hľadajte nejaké anomálie. Možno bude potrebné pozrieť sa na rôzne krajiny, aby ste to zistili.  \n",
+    "* Vypočítajte mieru úmrtnosti a ako sa mení v čase. Môžete chcieť zohľadniť dĺžku trvania choroby v dňoch, aby ste posunuli jednu časovú radu pred vykonaním výpočtov.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Referencie\n",
+    "## References\n",
     "\n",
-    "Môžete si pozrieť ďalšie štúdie o šírení epidémie COVID v nasledujúcich publikáciách:\n",
-    "* [Sliding SIR Model for Rt Estimation during COVID Pandemic](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/), blogový príspevok od [Dmitry Soshnikov](http://soshnikov.com)\n",
-    "* T.Petrova, D.Soshnikov, A.Grunin. [Estimation of Time-Dependent Reproduction Number for Global COVID-19 Outbreak](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
+    "You may look at further studies of COVID epidemic spread in the following publications:\n",
+    "* [Sliding SIR Model for Rt Estimation during COVID Pandemic](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/), blog post by [Dmitry Soshnikov](http://soshnikov.com)\n",
+    "* T.Petrova, D.Soshnikov, A.Grunin. [Odhad časovo závislého reprodukčného čísla pre globálny výskyt COVID-19](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
     "* [Kód k vyššie uvedenému článku na GitHub](https://github.com/shwars/SlidingSIR)\n"
    ]
   },
@@ -2418,7 +2423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Vyhlásenie o zodpovednosti**:  \nTento dokument bol preložený pomocou AI prekladateľskej služby [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, berte prosím na vedomie, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Originálny dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Za akékoľvek nedorozumenia alebo nesprávne výklady vyplývajúce z použitia tohto prekladu nenesieme zodpovednosť.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
    ]
   }
  ],
@@ -2442,13 +2447,7 @@
    "pygments_lexer": "ipython3",
    "version": "3.8.8"
   },
-  "orig_nbformat": 4,
-  "coopTranslator": {
-   "original_hash": "68a0ada1dae45c7d1d3519aa0a6d1a0f",
-   "translation_date": "2025-09-02T06:37:46+00:00",
-   "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
-   "language_code": "sk"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/translations/sk/2-Working-With-Data/README.md b/translations/sk/2-Working-With-Data/README.md
index ff6f40fa..64a867d3 100644
--- a/translations/sk/2-Working-With-Data/README.md
+++ b/translations/sk/2-Working-With-Data/README.md
@@ -1,6 +1,6 @@
 # Práca s dátami
 
-![data love](../../../translated_images/sk/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)
+![data love](../../../translated_images/sk/data-love.a22ef29e6742c852.webp)
 > Fotografia od <a href="https://unsplash.com/@swimstaralex?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexandra Sinna</a> na <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 V týchto lekciách sa naučíte niektoré spôsoby, ako je možné dáta spravovať, manipulovať s nimi a používať ich v aplikáciách. Dozviete sa o relačných a nerelačných databázach a o tom, ako v nich môžu byť dáta uložené. Naučíte sa základy práce s Pythonom na správu dát a objavíte niektoré z mnohých spôsobov, ako môžete s Pythonom pracovať na správe a analýze dát.
diff --git a/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md b/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md
index 1f5caa1d..de7a99d8 100644
--- a/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md
+++ b/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md
@@ -42,7 +42,7 @@ Vytvorte základný bodový graf na zobrazenie vzťahu medzi cenou za libru medu
 ```python
 sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);
 ```
-![bodový graf 1](../../../../translated_images/sk/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png)
+![bodový graf 1](../../../../translated_images/sk/scatter1.5e1aa5fd6706c5d1.webp)
 
 Teraz zobrazte tie isté údaje s farebnou schémou pripomínajúcou med, aby ste ukázali, ako sa cena vyvíja v priebehu rokov. Môžete to dosiahnuť pridaním parametra 'hue', ktorý ukáže zmenu rok čo rok:
 
@@ -51,7 +51,7 @@ Teraz zobrazte tie isté údaje s farebnou schémou pripomínajúcou med, aby st
 ```python
 sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5);
 ```
-![bodový graf 2](../../../../translated_images/sk/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png)
+![bodový graf 2](../../../../translated_images/sk/scatter2.c0041a58621ca702.webp)
 
 S touto zmenou farebnej schémy môžete vidieť, že v priebehu rokov existuje zjavný silný progres v cene medu za libru. Ak sa pozriete na vzorku údajov na overenie (napríklad vyberte štát Arizona), môžete vidieť vzor zvyšovania cien rok čo rok, s niekoľkými výnimkami:
 
@@ -80,7 +80,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec
 ```
 Vidíte, že veľkosť bodov sa postupne zväčšuje.
 
-![bodový graf 3](../../../../translated_images/sk/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png)
+![bodový graf 3](../../../../translated_images/sk/scatter3.3c160a3d1dcb36b3.webp)
 
 Je to jednoduchý prípad ponuky a dopytu? Kvôli faktorom, ako je klimatická zmena a kolaps kolónií, je k dispozícii menej medu na predaj rok čo rok, a preto cena rastie?
 
@@ -95,7 +95,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey);
 ```
 Odpoveď: Áno, s niekoľkými výnimkami okolo roku 2003:
 
-![čiarový graf 1](../../../../translated_images/sk/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png)
+![čiarový graf 1](../../../../translated_images/sk/line1.f36eb465229a3b1f.webp)
 
 ✅ Pretože Seaborn agreguje údaje okolo jednej čiary, zobrazuje "viacero meraní pri každej hodnote x tým, že vykresľuje priemer a 95% interval spoľahlivosti okolo priemeru". [Zdroj](https://seaborn.pydata.org/tutorial/relational.html). Toto časovo náročné správanie môžete vypnúť pridaním `ci=None`.
 
@@ -105,7 +105,7 @@ Otázka: No, v roku 2003 môžeme tiež vidieť nárast v zásobách medu? Čo a
 sns.relplot(x="year", y="totalprod", kind="line", data=honey);
 ```
 
-![čiarový graf 2](../../../../translated_images/sk/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png)
+![čiarový graf 2](../../../../translated_images/sk/line2.a5b3493dc01058af.webp)
 
 Odpoveď: Nie celkom. Ak sa pozriete na celkovú produkciu, zdá sa, že v tom konkrétnom roku skutočne vzrástla, aj keď všeobecne množstvo vyprodukovaného medu v týchto rokoch klesá.
 
@@ -130,7 +130,7 @@ sns.relplot(
 ```
 V tejto vizualizácii môžete porovnať výnos na kolóniu a počet kolónií rok čo rok, vedľa seba, s nastavením wrap na 3 pre stĺpce:
 
-![mriežka aspektov](../../../../translated_images/sk/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png)
+![mriežka aspektov](../../../../translated_images/sk/facet.6a34851dcd540050.webp)
 
 Pre tento dataset nič zvlášť nevyniká, pokiaľ ide o počet kolónií a ich výnos, rok čo rok a štát po štáte. Existuje iný spôsob, ako nájsť koreláciu medzi týmito dvoma premennými?
 
@@ -153,7 +153,7 @@ sns.despine(right=False)
 plt.ylabel('colony yield')
 ax.figure.legend();
 ```
-![prekryté grafy](../../../../translated_images/sk/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png)
+![prekryté grafy](../../../../translated_images/sk/dual-line.a4c28ce659603fab.webp)
 
 Aj keď okolo roku 2003 nič zvlášť nevyniká, umožňuje nám to ukončiť túto lekciu na trochu pozitívnejšiu nôtu: aj keď počet kolónií celkovo klesá, ich počet sa stabilizuje, aj keď ich výnos na kolóniu klesá.
 
diff --git a/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md
index faeec508..da77d00b 100644
--- a/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md
+++ b/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md
@@ -58,7 +58,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 ```
 Tu nainštalujete balík `ggplot2` a potom ho importujete do pracovného priestoru pomocou príkazu `library("ggplot2")`. Na vykreslenie akéhokoľvek grafu v ggplot sa používa funkcia `ggplot()` a špecifikujete dataset, premenné x a y ako atribúty. V tomto prípade používame funkciu `geom_line()`, pretože chceme vykresliť čiarový graf.
 
-![MaxRozpätie-čiarový graf](../../../../../translated_images/sk/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png)
+![MaxRozpätie-čiarový graf](../../../../../translated_images/sk/MaxWingspan-lineplot.b12169f99d26fdd2.webp)
 
 Čo si všimnete okamžite? Zdá sa, že existuje aspoň jeden extrémny údaj - to je poriadne rozpätie krídel! Rozpätie krídel viac ako 2000 centimetrov znamená viac ako 20 metrov - potulujú sa v Minnesote pterodaktyly? Poďme to preskúmať.
 
@@ -76,7 +76,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 ```
 Špecifikujeme uhol v `theme` a špecifikujeme štítky osí x a y v `xlab()` a `ylab()` respektíve. Funkcia `ggtitle()` dáva grafu názov.
 
-![MaxRozpätie-čiarový graf-vylepšený](../../../../../translated_images/sk/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png)
+![MaxRozpätie-čiarový graf-vylepšený](../../../../../translated_images/sk/MaxWingspan-lineplot-improved.04b73b4d5a59552a.webp)
 
 Aj s rotáciou štítkov nastavenou na 45 stupňov je ich príliš veľa na čítanie. Skúsme inú stratégiu: označme iba tie extrémne hodnoty a nastavme štítky priamo v grafe. Môžete použiť bodový graf, aby ste získali viac priestoru na označovanie:
 
@@ -92,7 +92,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
 
 Čo objavíte?
 
-![MaxRozpätie-bodový graf](../../../../../translated_images/sk/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png)
+![MaxRozpätie-bodový graf](../../../../../translated_images/sk/MaxWingspan-scatterplot.60dc9e0e19d32700.webp)
 
 ## Filtrovanie údajov
 
@@ -111,7 +111,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) +
 ```
 Vytvorili sme nový dataframe `birds_filtered` a potom vykreslili bodový graf. Filtrovaním extrémnych hodnôt sú vaše údaje teraz súdržnejšie a zrozumiteľnejšie.
 
-![MaxRozpätie-bodový graf-vylepšený](../../../../../translated_images/sk/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png)
+![MaxRozpätie-bodový graf-vylepšený](../../../../../translated_images/sk/MaxWingspan-scatterplot-improved.7d0af81658c65f3e.webp)
 
 Teraz, keď máme čistejší dataset aspoň z hľadiska rozpätia krídel, poďme objaviť viac o týchto vtákoch.
 
@@ -153,7 +153,7 @@ birds_filtered %>% group_by(Category) %>%
 ```
 V nasledujúcom úryvku inštalujeme balíky [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) a [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0), ktoré pomáhajú manipulovať a zoskupovať údaje na vykreslenie zoskupeného stĺpcového grafu. Najprv zoskupíte údaje podľa `Category` vtákov a potom sumarizujete stĺpce `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Potom vykreslíte stĺpcový graf pomocou balíka `ggplot2` a špecifikujete farby pre rôzne kategórie a štítky.
 
-![Zoskupený stĺpcový graf](../../../../../translated_images/sk/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png)
+![Zoskupený stĺpcový graf](../../../../../translated_images/sk/stacked-bar-chart.0c92264e89da7b39.webp)
 
 Tento stĺpcový graf je však nečitateľný, pretože obsahuje príliš veľa nezoskupených údajov. Musíte vybrať iba údaje, ktoré chcete vykresliť, takže sa pozrime na dĺžku vtákov podľa ich kategórie.
 
@@ -168,7 +168,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip()
 ```
 Najprv spočítate unikátne hodnoty v stĺpci `Category` a potom ich zoradíte do nového dataframe `birds_count`. Tieto zoradené údaje sú potom faktorizované na rovnakej úrovni, aby boli vykreslené v zoradenom poradí. Pomocou `ggplot2` potom vykreslíte údaje v stĺpcovom grafe. Funkcia `coord_flip()` vykreslí horizontálne stĺpce.
 
-![kategória-dĺžka](../../../../../translated_images/sk/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png)
+![kategória-dĺžka](../../../../../translated_images/sk/category-length.7e34c296690e85d6.webp)
 
 Tento stĺpcový graf poskytuje dobrý prehľad o počte vtákov v každej kategórii. Na prvý pohľad vidíte, že najväčší počet vtákov v tomto regióne patrí do kategórie Kačice/Husi/Vodné vtáky. Minnesota je "krajina 10 000 jazier", takže to nie je prekvapujúce!
 
@@ -191,7 +191,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl
 ```
 Zoskupíme údaje `birds_filtered` podľa `Category` a potom vykreslíme stĺpcový graf.
 
-![porovnávanie údajov](../../../../../translated_images/sk/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png)
+![porovnávanie údajov](../../../../../translated_images/sk/comparingdata.f486a450d61c7ca5.webp)
 
 Tu nie je nič prekvapujúce: kolibríky majú najmenšiu MaxDĺžku v porovnaní s pelikánmi alebo husami. Je dobré, keď údaje dávajú logický zmysel!
 
@@ -203,7 +203,7 @@ ggplot(data=birds_grouped, aes(x=Category)) +
   geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+
   coord_flip()
 ```
-![prekryté hodnoty](../../../../../translated_images/sk/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png)
+![prekryté hodnoty](../../../../../translated_images/sk/superimposed-values.5363f0705a1da416.webp)
 
 ## 🚀 Výzva
 
diff --git a/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md
index 0f238169..ed8e4c54 100644
--- a/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md
+++ b/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md
@@ -36,7 +36,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) +
   geom_point() +
   ggtitle("Max Length per order") + coord_flip()
 ```
-![max dĺžka podľa radu](../../../../../translated_images/sk/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png)
+![max dĺžka podľa radu](../../../../../translated_images/sk/max-length-per-order.e5b283d952c78c12.webp)
 
 Toto poskytuje prehľad o všeobecnej distribúcii dĺžky tela podľa radu vtákov, ale nie je to optimálny spôsob zobrazenia skutočných distribúcií. Táto úloha sa zvyčajne rieši vytvorením histogramu.
 
@@ -48,7 +48,7 @@ Toto poskytuje prehľad o všeobecnej distribúcii dĺžky tela podľa radu vtá
 ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + 
   geom_histogram(bins=10)+ylab('Frequency')
 ```
-![distribúcia pre celý dataset](../../../../../translated_images/sk/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png)
+![distribúcia pre celý dataset](../../../../../translated_images/sk/distribution-over-the-entire-dataset.d22afd3fa96be854.webp)
 
 Ako vidíte, väčšina z viac ako 400 vtákov v tomto datasete spadá do rozsahu pod 2000 pre ich maximálnu telesnú hmotnosť. Získajte viac informácií o údajoch zmenou parametra `bins` na vyššie číslo, napríklad 30:
 
@@ -56,7 +56,7 @@ Ako vidíte, väčšina z viac ako 400 vtákov v tomto datasete spadá do rozsah
 ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency')
 ```
 
-![distribúcia-30bins](../../../../../translated_images/sk/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png)
+![distribúcia-30bins](../../../../../translated_images/sk/distribution-30bins.6a3921ea7a421bf7.webp)
 
 Tento graf ukazuje distribúciu o niečo podrobnejšie. Graf menej posunutý doľava by sa dal vytvoriť zabezpečením, že vyberiete iba údaje v danom rozsahu:
 
@@ -68,7 +68,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) +
   geom_histogram(bins=30)+ylab('Frequency')
 ```
 
-![filtrovaný histogram](../../../../../translated_images/sk/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png)
+![filtrovaný histogram](../../../../../translated_images/sk/filtered-histogram.6bf5d2bfd8253322.webp)
 
 ✅ Vyskúšajte iné filtre a dátové body. Ak chcete vidieť úplnú distribúciu údajov, odstráňte filter `['MaxBodyMass']`, aby sa zobrazili označené distribúcie.
 
@@ -82,7 +82,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) +
 ```
 Zdá sa, že medzi týmito dvoma prvkami existuje očakávaná korelácia pozdĺž očakávanej osi, s jedným obzvlášť silným bodom konvergencie:
 
-![2d graf](../../../../../translated_images/sk/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png)
+![2d graf](../../../../../translated_images/sk/2d-plot.c504786f439bd7eb.webp)
 
 Histogramy fungujú dobre predvolene pre číselné údaje. Čo ak potrebujete vidieť distribúcie podľa textových údajov? 
 ## Preskúmajte dataset pre distribúcie pomocou textových údajov 
@@ -113,7 +113,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) +
   scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern"))
 ```
 
-![rozpätie krídel a stav ochrany](../../../../../translated_images/sk/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png)
+![rozpätie krídel a stav ochrany](../../../../../translated_images/sk/wingspan-conservation-collation.4024e9aa6910866a.webp)
 
 Zdá sa, že medzi minimálnym rozpätím krídel a stavom ochrany neexistuje dobrá korelácia. Otestujte iné prvky datasetu pomocou tejto metódy. Môžete vyskúšať aj rôzne filtre. Nájdete nejakú koreláciu?
 
@@ -127,7 +127,7 @@ Poďme teraz pracovať s hustotnými grafmi!
 ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + 
   geom_density()
 ```
-![hustotný graf](../../../../../translated_images/sk/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png)
+![hustotný graf](../../../../../translated_images/sk/density-plot.675ccf865b76c690.webp)
 
 Vidíte, ako graf odráža ten predchádzajúci pre údaje o minimálnom rozpätí krídel; je len o niečo plynulejší. Ak by ste chceli znovu navštíviť tú zubatú čiaru MaxBodyMass v druhom grafe, ktorý ste vytvorili, mohli by ste ju veľmi dobre vyhladiť opätovným vytvorením pomocou tejto metódy:
 
@@ -135,7 +135,7 @@ Vidíte, ako graf odráža ten predchádzajúci pre údaje o minimálnom rozpät
 ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
   geom_density()
 ```
-![hustota telesnej hmotnosti](../../../../../translated_images/sk/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png)
+![hustota telesnej hmotnosti](../../../../../translated_images/sk/bodymass-smooth.d31ce526d82b0a1f.webp)
 
 Ak by ste chceli hladkú, ale nie príliš hladkú čiaru, upravte parameter `adjust`: 
 
@@ -143,7 +143,7 @@ Ak by ste chceli hladkú, ale nie príliš hladkú čiaru, upravte parameter `ad
 ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
   geom_density(adjust = 1/5)
 ```
-![menej hladká telesná hmotnosť](../../../../../translated_images/sk/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png)
+![menej hladká telesná hmotnosť](../../../../../translated_images/sk/less-smooth-bodymass.10f4db8b683cc17d.webp)
 
 ✅ Prečítajte si o parametroch dostupných pre tento typ grafu a experimentujte!
 
@@ -153,7 +153,7 @@ Tento typ grafu ponúka krásne vysvetľujúce vizualizácie. S niekoľkými ria
 ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) +
   geom_density(alpha=0.5)
 ```
-![telesná hmotnosť podľa radu](../../../../../translated_images/sk/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png)
+![telesná hmotnosť podľa radu](../../../../../translated_images/sk/bodymass-per-order.9d2b065dd931b928.webp)
 
 ## 🚀 Výzva
 
diff --git a/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md
index d449ec3f..b4c2e5d7 100644
--- a/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md
+++ b/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md
@@ -88,7 +88,7 @@ pie(grouped$count,grouped$class, main="Edible?")
 ```
 Voila, koláčový graf zobrazujúci proporcie týchto údajov podľa dvoch tried húb. Je veľmi dôležité správne zoradiť poradie štítkov, najmä tu, preto si overte poradie, v akom je pole štítkov vytvorené!
 
-![koláčový graf](../../../../../translated_images/sk/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png)
+![koláčový graf](../../../../../translated_images/sk/pie1-wb.685df063673751f4.webp)
 
 ## Donuty!
 
@@ -123,7 +123,7 @@ library(webr)
 PieDonut(habitat, aes(habitat, count=count))
 ```
 
-![donutový graf](../../../../../translated_images/sk/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png)
+![donutový graf](../../../../../translated_images/sk/donut-wb.34e6fb275da9d834.webp)
 
 Tento kód používa dve knižnice - ggplot2 a webr. Pomocou funkcie PieDonut z knižnice webr môžeme ľahko vytvoriť donutový graf!
 
@@ -160,7 +160,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual
 
 Pomocou waflového grafu môžete jasne vidieť proporcie farieb klobúkov v tomto datasete húb. Zaujímavé je, že existuje veľa húb so zelenými klobúkmi!
 
-![waflový graf](../../../../../translated_images/sk/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png)
+![waflový graf](../../../../../translated_images/sk/waffle.aaa75c5337735a6e.webp)
 
 V tejto lekcii ste sa naučili tri spôsoby vizualizácie proporcií. Najprv musíte zoskupiť svoje údaje do kategórií a potom sa rozhodnúť, ktorý spôsob zobrazenia údajov je najlepší - koláč, donut alebo waffle. Všetky sú chutné a poskytujú používateľovi okamžitý prehľad o datasete.
 
diff --git a/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md
index 6396aa84..587e58ba 100644
--- a/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md
+++ b/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md
@@ -42,7 +42,7 @@ library(ggplot2)
 ggplot(honey, aes(x = priceperlb, y = state)) +
   geom_point(colour = "blue")
 ```
-![bodový graf 1](../../../../../translated_images/sk/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png)
+![bodový graf 1](../../../../../translated_images/sk/scatter1.86b8900674d88b26.webp)
 
 Teraz zobrazte tie isté údaje s farebnou schémou medu, aby ste ukázali, ako sa cena vyvíja v priebehu rokov. Môžete to urobiť pridaním parametra 'scale_color_gradientn', ktorý ukazuje zmenu rok po roku:
 
@@ -52,7 +52,7 @@ Teraz zobrazte tie isté údaje s farebnou schémou medu, aby ste ukázali, ako
 ggplot(honey, aes(x = priceperlb, y = state, color=year)) +
   geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7))
 ```
-![bodový graf 2](../../../../../translated_images/sk/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png)
+![bodový graf 2](../../../../../translated_images/sk/scatter2.4d1cbc693bad20e2.webp)
 
 S touto zmenou farebnej schémy môžete vidieť, že v priebehu rokov existuje zjavný silný nárast ceny za libru medu. Ak si overíte vzorku údajov (napríklad pre štát Arizona), môžete vidieť vzor zvyšovania cien rok po roku, s niekoľkými výnimkami:
 
@@ -83,7 +83,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) +
 ```
 Vidíte, že veľkosť bodov sa postupne zväčšuje.
 
-![bodový graf 3](../../../../../translated_images/sk/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png)
+![bodový graf 3](../../../../../translated_images/sk/scatter3.722d21e6f20b3ea2.webp)
 
 Je to jednoduchý prípad ponuky a dopytu? Kvôli faktorom, ako je zmena klímy a kolaps kolónií, je k dispozícii menej medu na predaj rok po roku, a preto cena stúpa?
 
@@ -98,7 +98,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab
 ```
 Odpoveď: Áno, s niektorými výnimkami okolo roku 2003:
 
-![čiarový graf 1](../../../../../translated_images/sk/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png)
+![čiarový graf 1](../../../../../translated_images/sk/line1.299b576fbb2a59e6.webp)
 
 Otázka: No, v roku 2003 môžeme tiež vidieť nárast v zásobách medu? Čo ak sa pozriete na celkovú produkciu rok po roku?
 
@@ -106,7 +106,7 @@ Otázka: No, v roku 2003 môžeme tiež vidieť nárast v zásobách medu? Čo a
 qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod")
 ```
 
-![čiarový graf 2](../../../../../translated_images/sk/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png)
+![čiarový graf 2](../../../../../translated_images/sk/line2.3b18fcda7176ceba.webp)
 
 Odpoveď: Nie celkom. Ak sa pozriete na celkovú produkciu, zdá sa, že v tomto konkrétnom roku skutočne vzrástla, aj keď všeobecne produkcia medu v týchto rokoch klesá.
 
@@ -126,7 +126,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) +
 ```
 V tejto vizualizácii môžete porovnať výnos na kolóniu a počet kolónií rok po roku, vedľa seba, s nastavením wrap na 3 pre stĺpce:
 
-![mriežka s viacerými grafmi](../../../../../translated_images/sk/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png)
+![mriežka s viacerými grafmi](../../../../../translated_images/sk/facet.491ad90d61c2a7cc.webp)
 
 Pre tento dataset nič konkrétne nevyniká, pokiaľ ide o počet kolónií a ich výnos rok po roku a štát po štáte. Existuje iný spôsob, ako nájsť koreláciu medzi týmito dvoma premennými?
 
@@ -143,7 +143,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3,
 axis(side = 4, at = pretty(range(y2)))      
 mtext("colony yield", side = 4, line = 3)   
 ```
-![prekryté grafy](../../../../../translated_images/sk/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png)
+![prekryté grafy](../../../../../translated_images/sk/dual-line.fc4665f360a54018.webp)
 
 Aj keď nič výrazné nevyniká okolo roku 2003, umožňuje nám to ukončiť túto lekciu na trochu pozitívnejšiu nôtu: aj keď celkový počet kolónií klesá, počet kolónií sa stabilizuje, aj keď ich výnos na kolóniu klesá.
 
diff --git a/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
index 15fc98fd..5957a20e 100644
--- a/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
+++ b/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
@@ -38,25 +38,25 @@ V predchádzajúcich lekciách ste experimentovali s vytváraním rôznych zauj
 
 Aj keď dátový vedec starostlivo vyberie správny graf pre správne dáta, existuje mnoho spôsobov, ako môžu byť dáta prezentované tak, aby podporili určitý názor, často na úkor samotných dát. Existuje veľa príkladov zavádzajúcich grafov a infografík!
 
-[![Ako grafy klamú od Alberta Caira](../../../../../translated_images/sk/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Ako grafy klamú")
+[![Ako grafy klamú od Alberta Caira](../../../../../translated_images/sk/tornado.2880ffc7f135f82b.webp)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Ako grafy klamú")
 
 > 🎥 Kliknite na obrázok vyššie pre konferenčný prejav o zavádzajúcich grafoch
 
 Tento graf prevracia os X, aby ukázal opak pravdy na základe dátumu:
 
-![zlý graf 1](../../../../../translated_images/sk/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png)
+![zlý graf 1](../../../../../translated_images/sk/bad-chart-1.596bc93425a8ac30.webp)
 
 [Tento graf](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) je ešte zavádzajúcejší, pretože oko je vedené k záveru, že počet prípadov COVID klesal v rôznych okresoch. Ak sa však pozriete bližšie na dátumy, zistíte, že boli preusporiadané, aby vytvorili tento zavádzajúci klesajúci trend.
 
-![zlý graf 2](../../../../../translated_images/sk/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg)
+![zlý graf 2](../../../../../translated_images/sk/bad-chart-2.62edf4d2f30f4e51.webp)
 
 Tento notoricky známy príklad používa farbu A prevrátenú os Y na zavádzanie: namiesto záveru, že počet úmrtí na strelné zbrane vzrástol po prijatí legislatívy podporujúcej zbrane, oko je oklamané, aby si myslelo opak:
 
-![zlý graf 3](../../../../../translated_images/sk/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg)
+![zlý graf 3](../../../../../translated_images/sk/bad-chart-3.e201e2e915a230bc.webp)
 
 Tento zvláštny graf ukazuje, ako môže byť proporcia manipulovaná, a to až do komického efektu:
 
-![zlý graf 4](../../../../../translated_images/sk/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg)
+![zlý graf 4](../../../../../translated_images/sk/bad-chart-4.8872b2b881ffa96c.webp)
 
 Porovnávanie neporovnateľného je ďalší pochybný trik. Existuje [úžasná webová stránka](https://tylervigen.com/spurious-correlations) venovaná 'falošným koreláciám', ktorá zobrazuje 'fakty' korelujúce veci ako rozvodovosť v Maine a spotrebu margarínu. Skupina na Reddite tiež zbiera [škaredé použitia](https://www.reddit.com/r/dataisugly/top/?t=all) dát.
 
@@ -91,13 +91,13 @@ Označte svoje osi, poskytnite legendu, ak je to potrebné, a ponúknite tooltip
 
 Ak sú vaše dáta textové a na osi X príliš dlhé, môžete text nakloniť pre lepšiu čitateľnosť. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) ponúka 3D vykresľovanie, ak to vaše dáta podporujú. Pomocou neho je možné vytvoriť sofistikované vizualizácie dát.
 
-![3D grafy](../../../../../translated_images/sk/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png)
+![3D grafy](../../../../../translated_images/sk/3d.db1734c151eee87d.webp)
 
 ## Animácia a 3D zobrazenie grafov
 
 Niektoré z najlepších vizualizácií dát sú dnes animované. Shirley Wu vytvorila úžasné vizualizácie pomocou D3, ako napríklad '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', kde každý kvet predstavuje vizualizáciu filmu. Ďalším príkladom pre Guardian je 'bussed out', interaktívny zážitok kombinujúci vizualizácie s Greensock a D3 spolu s článkom vo formáte scrollytelling, ktorý ukazuje, ako NYC rieši problém bezdomovcov tým, že ich presúva mimo mesta.
 
-![busing](../../../../../translated_images/sk/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png)
+![busing](../../../../../translated_images/sk/busing.8157cf1bc89a3f65.webp)
 
 > "Bussed Out: Ako Amerika presúva svojich bezdomovcov" od [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Vizualizácie od Nadieh Bremer & Shirley Wu
 
@@ -107,7 +107,7 @@ Hoci táto lekcia nie je dostatočne podrobná na to, aby vás naučila tieto v
 
 Dokončíte webovú aplikáciu, ktorá zobrazí animovaný pohľad na túto sociálnu sieť. Používa knižnicu, ktorá bola vytvorená na [vizualizáciu siete](https://github.com/emiliorizzo/vue-d3-network) pomocou Vue.js a D3. Keď aplikácia beží, môžete uzly na obrazovke presúvať a meniť usporiadanie dát.
 
-![liaisons](../../../../../translated_images/sk/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png)
+![liaisons](../../../../../translated_images/sk/liaisons.90ce7360bcf84765.webp)
 
 ## Projekt: Vytvorte graf na zobrazenie siete pomocou D3.js
 
diff --git a/translations/sk/3-Data-Visualization/README.md b/translations/sk/3-Data-Visualization/README.md
index c8e99c8d..3bbd2465 100644
--- a/translations/sk/3-Data-Visualization/README.md
+++ b/translations/sk/3-Data-Visualization/README.md
@@ -1,6 +1,6 @@
 # Vizualizácie
 
-![včela na kvete levandule](../../../translated_images/sk/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)
+![včela na kvete levandule](../../../translated_images/sk/bee.0aa1d91132b12e3a.webp)
 > Fotografia od <a href="https://unsplash.com/@jenna2980?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Jenna Lee</a> na <a href="https://unsplash.com/s/photos/bees-in-a-meadow?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 Vizualizácia dát je jednou z najdôležitejších úloh dátového vedca. Obrázky majú hodnotu tisíc slov a vizualizácia vám môže pomôcť identifikovať rôzne zaujímavé časti vašich dát, ako sú výkyvy, odľahlé hodnoty, zoskupenia, tendencie a ďalšie, ktoré vám pomôžu pochopiť príbeh, ktorý vaše dáta rozprávajú.
diff --git a/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md
index f6384f3f..08fa36a2 100644
--- a/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md
+++ b/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md
@@ -16,7 +16,7 @@ V tomto bode ste si pravdepodobne uvedomili, že dátová veda je proces. Tento
 
 Táto lekcia sa zameriava na 3 časti životného cyklu: získavanie, spracovanie a údržbu.
 
-![Diagram životného cyklu dátovej vedy](../../../../translated_images/sk/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg)  
+![Diagram životného cyklu dátovej vedy](../../../../translated_images/sk/data-science-lifecycle.a1e362637503c4fb.webp)  
 > Foto od [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/)
 
 ## Získavanie
@@ -92,7 +92,7 @@ Preskúmajte [životný cyklus procesu tímovej dátovej vedy](https://docs.micr
 
 |Proces tímovej dátovej vedy (TDSP)|Štandardný proces pre dolovanie dát naprieč odvetviami (CRISP-DM)|
 |--|--|
-|![Životný cyklus tímovej dátovej vedy](../../../../translated_images/sk/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Obrázok od Data Science Process Alliance](../../../../translated_images/sk/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) |
+|![Životný cyklus tímovej dátovej vedy](../../../../translated_images/sk/tdsp-lifecycle2.e19029d598e2e73d.webp) | ![Obrázok od Data Science Process Alliance](../../../../translated_images/sk/CRISP-DM.8bad2b4c66e62aa7.webp) |
 | Obrázok od [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Obrázok od [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) |
 
 ## [Kvíz po prednáške](https://ff-quizzes.netlify.app/en/ds/quiz/27)
diff --git a/translations/sk/4-Data-Science-Lifecycle/README.md b/translations/sk/4-Data-Science-Lifecycle/README.md
index d31587de..ddcd827e 100644
--- a/translations/sk/4-Data-Science-Lifecycle/README.md
+++ b/translations/sk/4-Data-Science-Lifecycle/README.md
@@ -1,6 +1,6 @@
 # Životný cyklus dátovej vedy
 
-![communication](../../../translated_images/sk/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)
+![communication](../../../translated_images/sk/communication.06d8e2a88d30d168.webp)
 > Foto od <a href="https://unsplash.com/@headwayio?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Headway</a> na <a href="https://unsplash.com/s/photos/communication?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 V týchto lekciách preskúmate niektoré aspekty životného cyklu dátovej vedy, vrátane analýzy a komunikácie o dátach.
diff --git a/translations/sk/5-Data-Science-In-Cloud/README.md b/translations/sk/5-Data-Science-In-Cloud/README.md
index 2fe5f44e..c17e3698 100644
--- a/translations/sk/5-Data-Science-In-Cloud/README.md
+++ b/translations/sk/5-Data-Science-In-Cloud/README.md
@@ -1,12 +1,12 @@
 # Data Science v cloude
 
-![cloud-picture](../../../translated_images/sk/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)
+![cloud-picture](../../../translated_images/sk/cloud-picture.f5526de3c6c6387b.webp)
 
 > Foto od [Jelleke Vanooteghem](https://unsplash.com/@ilumire) z [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)
 
 Keď ide o prácu s veľkými dátami v oblasti dátovej vedy, cloud môže byť prelomovým riešením. V nasledujúcich troch lekciách si vysvetlíme, čo je to cloud a prečo môže byť veľmi užitočný. Taktiež preskúmame dataset o zlyhaní srdca a vytvoríme model, ktorý pomôže odhadnúť pravdepodobnosť, že niekto zažije zlyhanie srdca. Využijeme silu cloudu na trénovanie, nasadenie a používanie modelu dvoma rôznymi spôsobmi. Jeden spôsob bude využívať iba používateľské rozhranie v štýle Low code/No code, druhý spôsob bude využívať Azure Machine Learning Software Developer Kit (Azure ML SDK).
 
-![project-schema](../../../translated_images/sk/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png)
+![project-schema](../../../translated_images/sk/project-schema.420e56d495624541.webp)
 
 ### Témy
 
diff --git a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
index 42cf4356..79c8d837 100644
--- a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
+++ b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
@@ -32,7 +32,7 @@ Vďaka demokratizácii AI je pre vývojárov teraz jednoduchšie navrhovať a in
 
  * [Dátová veda v zdravotníctve](https://data-flair.training/blogs/data-science-in-healthcare/) - zdôrazňuje aplikácie ako medicínske zobrazovanie (napr. MRI, röntgen, CT-sken), genomika (sekvenovanie DNA), vývoj liekov (hodnotenie rizík, predikcia úspechu), prediktívna analytika (starostlivosť o pacientov a logistika zásob), sledovanie a prevencia chorôb atď.
 
-![Aplikácie dátovej vedy v reálnom svete](../../../../translated_images/sk/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Zdroj obrázku: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/)
+![Aplikácie dátovej vedy v reálnom svete](../../../../translated_images/sk/data-science-applications.4e5019cd8790ebac.webp) Zdroj obrázku: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/)
 
 Obrázok ukazuje ďalšie oblasti a príklady aplikácie techník dátovej vedy. Chcete preskúmať ďalšie aplikácie? Pozrite si sekciu [Recenzia a samostatné štúdium](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) nižšie.
 
diff --git a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
index 0401d4dc..e6480c11 100644
--- a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
+++ b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
@@ -13,7 +13,7 @@ Rozhranie Explorer (zobrazené na snímke obrazovky nižšie) vám umožňuje vy
  2. Preskúmajte dataset [Katalóg](https://planetarycomputer.microsoft.com/catalog) – zistite účel každého datasetu.
  3. Použite Explorer – vyberte dataset, ktorý vás zaujíma, zvoľte relevantný dotaz a možnosť vykreslenia.
 
-![Explorer Planetary Computer](../../../../translated_images/sk/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png)
+![Explorer Planetary Computer](../../../../translated_images/sk/planetary-computer-explorer.c1e95a9b053167d6.webp)
 
 `Vaša úloha:`
 Teraz preskúmajte vizualizáciu, ktorá sa zobrazí v prehliadači, a odpovedzte na nasledujúce otázky:
diff --git a/translations/sk/README.md b/translations/sk/README.md
index f49ebe91..e8fd355f 100644
--- a/translations/sk/README.md
+++ b/translations/sk/README.md
@@ -1,30 +1,30 @@
-# Data Science pre začiatočníkov - osnovy
+# Data Science pre začiatočníkov - Učebný plán
 
-[![Otvoriť v GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
 
-[![GitHub licencia](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
-[![GitHub prispievatelia](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
+[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
 [![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
 [![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![GitHub sledovatelia](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/)
 [![GitHub forks](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
-[![GitHub hviezdy](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
 
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
-Advokáti Azure Cloud v Microsoft s radosťou ponúkajú 10-týždňový, 20-lekčný kurz venovaný Data Science. Každá lekcia obsahuje pred-lekčné a po-lekčné kvízy, písané pokyny na dokončenie lekcie, riešenie a úlohu. Naša projektovo orientovaná pedagogika umožňuje učiť sa pri budovaní, čo je osvedčený spôsob, ako sa nové zručnosti dobre uchytia.
+Azure Cloud Advocates v Microsoft s radosťou ponúkajú 10-týždňový, 20-lekciový učebný plán všetkého o dátovej vede. Každá lekcia obsahuje kvízy pred a po lekcii, písané inštrukcie na dokončenie lekcie, riešenie a zadanie. Náš projektový prístup k výučbe vám umožňuje učiť sa počas tvorby, čo je osvedčený spôsob, ako nové schopnosti „uložiť“.
 
-**Veľká vďaka našim autorom:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer).
+**Úprimné poďakovanie našim autorom:** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer).
 
-**🙏 Špeciálne poďakovanie 🙏 našim autorom, recenzentom a prispievateľom obsahu z [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** najmä Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
+**🙏 Špeciálne poďakovanie 🙏 našim autorom, recenzentom a prispievateľom obsahu z [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** predovšetkým Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
 [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
 
-|![Sketchnote od @sketchthedocs https://sketchthedocs.dev](../../translated_images/sk/00-Title.8af36cd35da1ac55.webp)|
+|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/sk/00-Title.8af36cd35da1ac55.webp)|
 |:---:|
 | Data Science pre začiatočníkov - _Sketchnote od [@nitya](https://twitter.com/nitya)_ |
 
@@ -33,25 +33,35 @@ Advokáti Azure Cloud v Microsoft s radosťou ponúkajú 10-týždňový, 20-lek
 #### Podporované cez GitHub Action (automatizované a vždy aktuálne)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Čínština (zjednodušená)](../zh-CN/README.md) | [Čínština (tradičná, Hong Kong)](../zh-HK/README.md) | [Čínština (tradičná, Macau)](../zh-MO/README.md) | [Čínština (tradičná, Taiwan)](../zh-TW/README.md) | [Chorvátčina](../hr/README.md) | [Čeština](../cs/README.md) | [Dánčina](../da/README.md) | [Holandčina](../nl/README.md) | [Estónčina](../et/README.md) | [Fínčina](../fi/README.md) | [Francúzština](../fr/README.md) | [Nemčina](../de/README.md) | [Gréčtina](../el/README.md) | [Hebrejčina](../he/README.md) | [Hindi](../hi/README.md) | [Maďarčina](../hu/README.md) | [Indonézština](../id/README.md) | [Taliančina](../it/README.md) | [Japončina](../ja/README.md) | [Kannada](../kn/README.md) | [Kórejčina](../ko/README.md) | [Litovčina](../lt/README.md) | [Malajčina](../ms/README.md) | [Malajálamčina](../ml/README.md) | [Maráthčina](../mr/README.md) | [Nepálčina](../ne/README.md) | [Nigérijský pidžin](../pcm/README.md) | [Nórčina](../no/README.md) | [Perzština (Farsi)](../fa/README.md) | [Poľština](../pl/README.md) | [Portugalčina (Brazília)](../pt-BR/README.md) | [Portugalčina (Portugalsko)](../pt-PT/README.md) | [Paňdžábčina (Gurmukhí)](../pa/README.md) | [Rumunčina](../ro/README.md) | [Ruština](../ru/README.md) | [Srbčina (cyrilika)](../sr/README.md) | [Slovenčina](./README.md) | [Slovinčina](../sl/README.md) | [Španielčina](../es/README.md) | [Swahilčina](../sw/README.md) | [Švédčina](../sv/README.md) | [Tagalog (Filipínčina)](../tl/README.md) | [Tamilčina](../ta/README.md) | [Telugu](../te/README.md) | [Thajčina](../th/README.md) | [Turečtina](../tr/README.md) | [Ukrajinčina](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamsčina](../vi/README.md)
-
-> **Radšej klonovať lokálne?**
+[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](./README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
 
+> **Preferujete klonovať lokálne?**
+>
 > Tento repozitár obsahuje viac ako 50 jazykových prekladov, čo výrazne zvyšuje veľkosť sťahovania. Ak chcete klonovať bez prekladov, použite sparse checkout:
+>
+> **Bash / macOS / Linux:**
 > ```bash
 > git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
 > cd Data-Science-For-Beginners
 > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
 > ```
-> Toto vám dá všetko, čo potrebujete na dokončenie kurzu s oveľa rýchlejším sťahovaním.
+>
+> **CMD (Windows):**
+> ```cmd
+> git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
+> cd Data-Science-For-Beginners
+> git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"
+> ```
+>
+> Toto vám poskytne všetko potrebné na dokončenie kurzu s omnoho rýchlejším stiahnutím.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-**Ak si prajete podporu ďalších jazykov prekladov, sú uvedené [tu](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
+**Ak chcete podporovať ďalšie jazyky prekladov, sú uvedené [tu](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
 
-#### Pridajte sa k našej komunite
+#### Pridajte sa k našej komunite 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Máme prebiehajúcu sériu Learn with AI na Discorde, dozviete sa viac a pridajte sa k nám na [Learn with AI Series](https://aka.ms/learnwithai/discord) od 18. do 30. septembra 2025. Získate tipy a triky, ako využívať GitHub Copilot pre Data Science.
+Prebieha séria "Learn with AI" na Discorde, dozviete sa viac a pripojte sa k nám na [Learn with AI Series](https://aka.ms/learnwithai/discord) od 18. do 30. septembra 2025. Získate tipy a triky, ako používať GitHub Copilot pre dátovú vedu.
 
 ![Learn with AI series](../../translated_images/sk/1.2b28cdc6205e26fe.webp)
 
@@ -59,156 +69,156 @@ Máme prebiehajúcu sériu Learn with AI na Discorde, dozviete sa viac a pridajt
 
 Začnite s nasledujúcimi zdrojmi:
 
-- [Stránka Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Na tejto stránke nájdete zdroje pre začiatočníkov, študentské balíčky a dokonca aj spôsoby, ako získať bezplatný certifikačný poukaz. Túto stránku si chcete uložiť medzi záložky a občas skontrolovať, pretože obsah meníme aspoň raz za mesiac.
-- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Pridajte sa ku globálnej komunite študentských veľvyslancov, toto by mohla byť vaša cesta do Microsoftu.
+- [Stránka Student Hub](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) Na tejto stránke nájdete zdroje pre začiatočníkov, študentské balíčky a dokonca aj spôsoby, ako získať bezplatný certifikačný kupón. Toto je stránka, ktorú si chcete uložiť do záložiek a sledovať ju pravidelne, pretože obsah meníme aspoň raz mesačne.
+- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) Pridajte sa k globálnej komunite študentských ambasádorov, toto môže byť vaša vstupenka do Microsoftu.
 
 # Začíname
 
 ## 📚 Dokumentácia
 
-- **[Inštalačný sprievodca](INSTALLATION.md)** - krok za krokom inštrukcie pre začiatočníkov
-- **[Používateľský sprievodca](USAGE.md)** - príklady a bežné pracovné postupy
-- **[Riešenie problémov](TROUBLESHOOTING.md)** - riešenia bežných problémov
-- **[Sprievodca prispievaním](CONTRIBUTING.md)** - ako prispieť do tohto projektu
-- **[Pre učiteľov](for-teachers.md)** - usmernenia pre výučbu a zdroje pre triedu
+- **[Inštalačný návod](INSTALLATION.md)** - Krok za krokom inštrukcie pre začiatočníkov
+- **[Používateľský návod](USAGE.md)** - Príklady a bežné workflowy
+- **[Riešenie problémov](TROUBLESHOOTING.md)** - Riešenia bežných problémov
+- **[Návod na príspevky](CONTRIBUTING.md)** - Ako prispieť do tohto projektu
+- **[Pre učiteľov](for-teachers.md)** - Pokyny na výučbu a materiály do triedy
 
 ## 👨‍🎓 Pre študentov
-> **Úplní začiatočníci**: Ste nováčik vo vede o dátach? Začnite s našimi [príkladmi vhodnými pre začiatočníkov](examples/README.md)! Tieto jednoduché, dobre komentované príklady vám pomôžu pochopiť základy predtým, než sa pustíte do celej osnovy.
-> **[Študenti](https://aka.ms/student-page)**: ak chcete používať tento kurz sami, vytvorte si forka celého repozitára a dokončujte cvičenia samostatne, začnite pred-lekčným kvízom. Potom si prečítajte lekciu a dokončite ostatné aktivity. Snažte sa projekty vytvárať pochopením lekcií namiesto kopírovania riešenia; kód s riešením je však dostupný v priečinkoch /solutions v každej lekcii orientovanej na projekt. Ďalšou možnosťou je vytvoriť si študijnú skupinu s priateľmi a prejsť obsah spolu. Pre ďalšie štúdium odporúčame [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
+> **Celkom začiatočníci**: Noví v dátovej vede? Začnite s našimi [príkladmi vhodnými pre začiatočníkov](examples/README.md)! Tieto jednoduché, dobre okomentované príklady vám pomôžu pochopiť základy predtým, ako sa pustíte do plného učebného plánu.
+> **[Študenti](https://aka.ms/student-page)**: ak chcete tento učebný plán používať samostatne, forknete celý repozitár a dokončujte cvičenia postupne, začínajúc prednáškovým kvízom. Prečítajte si prednášku a dokončite ostatné aktivity. Pokúste sa vytvoriť projekty porozumením lekcií namiesto kopírovania kódu riešenia; avšak tento kód je k dispozícii v priečinkoch /solutions v každej projektovo orientovanej lekcii. Ďalšou možnosťou je vytvoriť študijnú skupinu s priateľmi a prechádzať obsah spolu. Na ďalšie štúdium odporúčame [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum).
 
 **Rýchly štart:**
-1. Prezrite si [Inštalačný sprievodca](INSTALLATION.md) a nastavte svoje prostredie
-2. Prezrite si [Používateľský sprievodca](USAGE.md), aby ste sa naučili pracovať s osnovou
-3. Začnite s Lekciou 1 a pokračujte postupne
+1. Prezrite si [Inštalačný návod](INSTALLATION.md) a nastavte si prostredie
+2. Preštudujte si [Používateľský návod](USAGE.md) a naučte sa pracovať s učebným plánom
+3. Začnite s Lekciou 1 a pracujte postupne
 4. Pridajte sa k našej [Discord komunite](https://aka.ms/ds4beginners/discord) pre podporu
 
 ## 👩‍🏫 Pre učiteľov
+> **Učitelia**: zahrnuli sme [niekoľko návrhov](for-teachers.md) na použitie tohto kurikula. Radi by sme dostali vašu spätnú väzbu [v našom diskusnom fóre](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
 
-> **Učitelia**: pridali sme [niekoľko návrhov](for-teachers.md), ako používať túto osnovu. Radi privítame vašu spätnú väzbu [v našom diskusnom fóre](https://github.com/microsoft/Data-Science-For-Beginners/discussions)!
-## Stretnite tím
+## Zoznámte sa s tímom
 
-[![Propagačné video](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Propagačné video")
+[![Promo video](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Promo video")
 
 **Gif od** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
 
-> 🎥 Kliknite na vyššie uvedený obrázok pre video o projekte a ľuďoch, ktorí ho vytvorili!
+> 🎥 Kliknite na obrázok vyššie pre video o projekte a ľuďoch, ktorí ho vytvorili!
 
-## Pedagógia
+## Pedagogika
 
-Pri tvorbe tohto kurikula sme si vybrali dva pedagogické princípy: zabezpečiť, aby bolo založené na projektoch a aby obsahovalo časté kvízy. Na konci tejto série sa študenti naučia základné princípy dátovej vedy vrátane etických konceptov, prípravy dát, rôznych spôsobov práce s dátami, vizualizácie dát, analýzy dát, reálnych prípadov použitia dátovej vedy a ďalšie.
+Pri tvorbe tohto kurikula sme zvolili dve pedagogické zásady: zabezpečiť, aby bolo projektovo orientované a aby obsahovalo časté kvízy. Na konci tejto série sa študenti naučia základné princípy dátovej vedy vrátane etických konceptov, prípravy dát, rôznych spôsobov práce s dátami, vizualizácie dát, analýzy dát, reálnych prípadov použitia dátovej vedy a ďalšie.
 
-Okrem toho kvíz s nízkou záťažou pred hodinou nastavuje študentovi zámer učiť sa danú tému, zatiaľ čo druhý kvíz po hodine zabezpečuje ďalšie uchovanie informácií. Toto kurikulum bolo navrhnuté tak, aby bolo flexibilné a zábavné a môže byť absolvované celé alebo čiastočne. Projekty začínajú malé a ku koncu desaťtýždňového cyklu sa stávajú čoraz komplexnejšími.
+Okrem toho nízkonákladový kvíz pred vyučovaním nastavuje študentovi zameranie na učenie témy, zatiaľ čo druhý kvíz po vyučovaní zabezpečuje ďalšie zafixovanie vedomostí. Toto kurikulum bolo navrhnuté ako flexibilné a zábavné a môže byť absolvované celé alebo čiastočne. Projekty začínajú malé a postupne sa počas 10-týždňového cyklu stávajú náročnejšími.
 
-> Nájdete u nás [Kódex správania](CODE_OF_CONDUCT.md), [Prispievanie](CONTRIBUTING.md), [Pokyny na preklady](TRANSLATIONS.md). Privítame vaše konštruktívne pripomienky!
+> Nájdete tu náš [Kódex správania](CODE_OF_CONDUCT.md), [Príspevky](CONTRIBUTING.md), [Pokyny k prekladu](TRANSLATIONS.md). Radi prijmeme vaše konštruktívne pripomienky!
 
-## Každá lekcia zahŕňa:
+## Každá lekcia obsahuje:
 
-- Nepovinnú sketchnotu
-- Nepovinné doplnkové video
-- Rozohrevný kvíz pred lekciou
+- Voliteľnú skicovačku
+- Voliteľné doplnkové video
+- Úvodný rozcvičovací kvíz
 - Písanú lekciu
-- Pre projektové lekcie krok za krokom návody na vytvorenie projektu
+- Pre projektové lekcie príručky krok za krokom, ako vytvoriť projekt
 - Kontroly vedomostí
 - Výzvu
-- Doplnkovú literatúru
+- Doplnkové čítanie
 - Zadanie
 - [Kvíz po lekcii](https://ff-quizzes.netlify.app/en/)
 
-> **Poznámka o kvízoch**: Všetky kvízy sú v priečinku Quiz-App, dohromady 40 kvízov po tri otázky. Sú prepojené v lekciách, ale kvízová aplikácia sa dá spustiť lokálne alebo nasadiť do Azure; postupujte podľa inštrukcií v priečinku `quiz-app`. Postupne prebieha lokalizácia.
+> **Poznámka o kvízoch**: Všetky kvízy sú obsiahnuté v priečinku Quiz-App, celkovo je k dispozícii 40 kvízov so 3 otázkami v každom. Sú prepojené z lekcií, ale kvízová aplikácia môže byť spustená lokálne alebo nasadená do Azure; riaďte sa inštrukciami v priečinku `quiz-app`. Postupne sa lokalizujú.
 
-## 🎓 Príklady priateľské pre začiatočníkov
+## 🎓 Príklady priateľské k začiatočníkom
 
-**Ste nový v dátovej vede?** Vytvorili sme špeciálny [adresár príkladov](examples/README.md) s jednoduchým, dobre komentovaným kódom, ktorý vám pomôže začať:
+**Nový v dátovej vede?** Vytvorili sme špeciálny [adresár príkladov](examples/README.md) s jednoduchým, dobre komentovaným kódom, ktorý vám pomôže začať:
 
-- 🌟 **Hello World** - Váš prvý program v dátovej vede
-- 📂 **Nahrávanie dát** - Naučte sa čítať a skúmať dátové súbory
+- 🌟 **Hello World** - Váš prvý program dátovej vedy
+- 📂 **Načítanie dát** - Naučte sa čítať a skúmať datasety
 - 📊 **Jednoduchá analýza** - Vypočítajte štatistiky a nájdite vzory
-- 📈 **Základná vizualizácia** - Vytvárajte grafy a diagramy
-- 🔬 **Reálny projekt** - Kompletný pracovný tok od začiatku do konca
+- 📈 **Základná vizualizácia** - Vytvorte grafy a diagramy
+- 🔬 **Projekt z praxe** - Kompletný pracovný tok od začiatku do konca
 
-Každý príklad obsahuje podrobné komentáre vysvetľujúce každý krok, čo je ideálne pre absolútnych začiatočníkov!
+Každý príklad obsahuje detailné komentáre vysvetľujúce každý krok, čo ho robí ideálnym pre absolútnych začiatočníkov!
 
 👉 **[Začnite s príkladmi](examples/README.md)** 👈
 
 ## Lekcie
 
 
-|![ Sketchnota od @sketchthedocs https://sketchthedocs.dev](../../translated_images/sk/00-Roadmap.4905d6567dff4753.webp)|
+|![ Skicovačka od @sketchthedocs https://sketchthedocs.dev](../../translated_images/sk/00-Roadmap.4905d6567dff4753.webp)|
 |:---:|
-| Dátová veda pre začiatočníkov: Plán - _Sketchnota od [@nitya](https://twitter.com/nitya)_ |
+| Dátová veda pre začiatočníkov: Plán cesty - _Skicovačka od [@nitya](https://twitter.com/nitya)_ |
 
 
-| Číslo lekcie | Téma | Zoskupenie lekcie | Učebné ciele | Prepojená lekcia | Autor |
+| Číslo lekcie | Téma | Zoskupenie lekcie | Ciele učenia | Prepojená lekcia | Autor |
 | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
-| 01 | Definovanie dátovej vedy | [Úvod](1-Introduction/README.md) | Naučte sa základné koncepty dátovej vedy a jej súvislosť s umelou inteligenciou, strojovým učením a veľkými dátami. | [lekcia](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
-| 02 | Etika dátovej vedy | [Úvod](1-Introduction/README.md) | Koncepty, výzvy a rámce etiky dát. | [lekcia](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
+| 01 | Definovanie dátovej vedy | [Úvod](1-Introduction/README.md) | Naučte sa základné koncepty dátovej vedy a ako súvisí s umelou inteligenciou, strojovým učením a veľkými dátami. | [lekcia](1-Introduction/01-defining-data-science/README.md) [video](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
+| 02 | Etika dátovej vedy | [Úvod](1-Introduction/README.md) | Koncepty, výzvy a rámce dátovej etiky. | [lekcia](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
 | 03 | Definovanie dát | [Úvod](1-Introduction/README.md) | Ako sa dáta klasifikujú a ich bežné zdroje. | [lekcia](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
 | 04 | Úvod do štatistiky a pravdepodobnosti | [Úvod](1-Introduction/README.md) | Matematické techniky pravdepodobnosti a štatistiky na pochopenie dát. | [lekcia](1-Introduction/04-stats-and-probability/README.md) [video](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
-| 05 | Práca s relačnými dátami | [Práca s dátami](2-Working-With-Data/README.md) | Úvod do relačných dát a základy skúmania a analýzy relačných dát pomocou Structured Query Language, známeho ako SQL (vyslovuje sa „see-quell“). | [lekcia](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
+| 05 | Práca s relačnými dátami | [Práca s dátami](2-Working-With-Data/README.md) | Úvod do relačných dát a základy skúmania a analýzy relačných dát pomocou jazyka Structured Query Language, známeho tiež ako SQL (vyslovuje sa „si-kwel“). | [lekcia](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
 | 06 | Práca s NoSQL dátami | [Práca s dátami](2-Working-With-Data/README.md) | Úvod do nerelačných dát, ich rôznych typov a základy skúmania a analýzy dokumentových databáz. | [lekcia](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique) |
-| 07 | Práca s Python | [Práca s dátami](2-Working-With-Data/README.md) | Základy používania Pythonu na skúmanie dát s knižnicami ako Pandas. Odporúča sa základné porozumenie programovaniu v Pythone. | [lekcia](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
-| 08 | Príprava dát | [Práca s dátami](2-Working-With-Data/README.md) | Témy techník úpravy a transformácie dát na riešenie problémov s chýbajúcimi, nesprávnymi alebo neúplnými dátami. | [lekcia](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
+| 07 | Práca s Pythonom | [Práca s dátami](2-Working-With-Data/README.md) | Základy používania Pythonu na prieskum dát s knižnicami ako Pandas. Odporúča sa základné porozumenie programovaniu v Pythone. | [lekcia](2-Working-With-Data/07-python/README.md) [video](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
+| 08 | Príprava dát | [Práca s dátami](2-Working-With-Data/README.md) | Témy týkajúce sa techník čistenia a transformácie dát na zvládanie problémov s chýbajúcimi, nepresnými alebo neúplnými dátami. | [lekcia](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
 | 09 | Vizualizácia množstiev | [Vizualizácia dát](3-Data-Visualization/README.md) | Naučte sa používať Matplotlib na vizualizáciu dát o vtákoch 🦆 | [lekcia](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 10 | Vizualizácia rozdelení dát | [Vizualizácia dát](3-Data-Visualization/README.md) | Vizualizácia pozorovaní a trendov v intervale. | [lekcia](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 11 | Vizualizácia podielov | [Vizualizácia dát](3-Data-Visualization/README.md) | Vizualizácia diskrétnych a zoskupených percent. | [lekcia](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 12 | Vizualizácia vzťahov | [Vizualizácia dát](3-Data-Visualization/README.md) | Vizualizácia spojení a korelácií medzi súbormi dát a ich premennými. | [lekcia](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 13 | Významné vizualizácie | [Vizualizácia dát](3-Data-Visualization/README.md) | Techniky a odporúčania na tvorbu vizualizácií hodnotných pre efektívne riešenie problémov a získavanie informácií. | [lekcia](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 14 | Úvod do životného cyklu dátovej vedy | [Životný cyklus](4-Data-Science-Lifecycle/README.md) | Úvod do životného cyklu dátovej vedy a jeho prvého kroku získavania a extrakcie dát. | [lekcia](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
+| 10 | Vizualizácia rozdelení dát | [Vizualizácia dát](3-Data-Visualization/README.md) | Vizualizovanie pozorovaní a trendov v rámci intervalu. | [lekcia](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 11 | Vizualizácia podielov | [Vizualizácia dát](3-Data-Visualization/README.md) | Vizualizácia diskrétnych a zoskupených percentuálnych podielov. | [lekcia](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 12 | Vizualizácia vzťahov | [Vizualizácia dát](3-Data-Visualization/README.md) | Vizualizácia spojení a korelácií medzi množinami dát a ich premennými. | [lekcia](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 13 | Významné vizualizácie | [Vizualizácia dát](3-Data-Visualization/README.md) | Techniky a rady na to, aby vaše vizualizácie boli cenné pre efektívne riešenie problémov a získavanie poznatkov. | [lekcia](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
+| 14 | Úvod do životného cyklu dátovej vedy | [Životný cyklus](4-Data-Science-Lifecycle/README.md) | Úvod do životného cyklu dátovej vedy a jeho prvý krok – získavanie a extrakcia dát. | [lekcia](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
 | 15 | Analýza | [Životný cyklus](4-Data-Science-Lifecycle/README.md) | Táto fáza životného cyklu dátovej vedy sa zameriava na techniky analýzy dát. | [lekcia](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
-| 16 | Komunikácia | [Životný cyklus](4-Data-Science-Lifecycle/README.md) | Táto fáza životného cyklu dátovej vedy sa zameriava na prezentáciu poznatkov z dát spôsobom, ktorý uľahčuje ich pochopenie pre rozhodovacích pracovníkov. | [lekcia](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
-| 17 | Dátová veda v cloude | [Cloudové dáta](5-Data-Science-In-Cloud/README.md) | Táto séria lekcií predstavuje dátovú vedu v cloude a jej výhody. | [lekcia](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) |
-| 18 | Dátová veda v cloude | [Cloudové dáta](5-Data-Science-In-Cloud/README.md) | Tréning modelov pomocou nástrojov Low Code. |[lekcia](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) |
-| 19 | Dátová veda v cloude | [Cloudové dáta](5-Data-Science-In-Cloud/README.md) | Nasadzovanie modelov pomocou Azure Machine Learning Studio. | [lekcia](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) |
-| 20 | Dátová veda v praxi | [V teréne](6-Data-Science-In-Wild/README.md) | Projekty riadené dátovou vedou v reálnom svete. | [lekcia](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
+| 16 | Komunikácia | [Životný cyklus](4-Data-Science-Lifecycle/README.md) | Táto fáza životného cyklu dátovej vedy sa zameriava na prezentáciu poznatkov z dát tak, aby boli pre rozhodovateľov ľahko pochopiteľné. | [lekcia](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
+| 17 | Dátová veda v cloude | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Táto séria lekcií predstavuje dátovú vedu v cloude a jej výhody. | [lekcia](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) |
+| 18 | Dátová veda v cloude | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Tréning modelov pomocou nástrojov Low Code. |[lekcia](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) |
+| 19 | Dátová veda v cloude | [Cloud Data](5-Data-Science-In-Cloud/README.md) | Nasadzovanie modelov pomocou Azure Machine Learning Studio. | [lekcia](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) a [Maud](https://twitter.com/maudstweets) |
+| 20 | Dátová veda v praxi | [V teréne](6-Data-Science-In-Wild/README.md) | Projekty založené na dátovej vede v reálnom svete. | [lekcia](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
 
 ## GitHub Codespaces
 
-Postupujte podľa týchto krokov na otvorenie tejto ukážky v Codespace:
-1. Kliknite na rozbaľovacie menu Code a vyberte možnosť Open with Codespaces.
+Postupujte podľa týchto krokov na otvorenie tohto ukážkového projektu v Codespace:
+1. Kliknite na rozbaľovaciu ponuku Kód a vyberte možnosť Open with Codespaces.
 2. Vyberte + New codespace v spodnej časti panela.
-Pre viac informácií si pozrite [dokumentáciu GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
+Pre viac informácií si pozrite [dokumentáciu GitHubu](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace).
 
-## VSCode Remote - Containers
-Postupujte podľa týchto krokov na otvorenie tohto repozitára v kontajneri na vašom lokálnom zariadení pomocou VSCode a rozšírenia VS Code Remote - Containers:
+## VSCode Remote - Kontajnery
+Postupujte podľa týchto krokov na otvorenie tohto repozitára v kontajneri pomocou vášho lokálneho počítača a VSCode s rozšírením VS Code Remote - Containery:
 
-1. Ak používate vývojový kontajner prvýkrát, uistite sa, že váš systém spĺňa požiadavky (napr. máte nainštalovaný Docker) v [dokumentácii "Začíname"](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
+1. Ak používate vývojový kontajner prvýkrát, uistite sa, že váš systém spĺňa požiadavky (napr. je nainštalovaný Docker) podľa [dokumentácie pre začiatočníkov](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
 
-Na používanie tohto repozitára ho môžete buď otvoriť v izolovanom Docker volume:
+Na používanie tohto repozitára môžete buď otvoriť repozitár v izolovanom Docker volume:
 
-**Poznámka**: Pod kapotou použije príkaz Remote-Containers: **Clone Repository in Container Volume...** na naklonovanie zdrojového kódu do Docker volume namiesto lokálneho súborového systému. [Volume](https://docs.docker.com/storage/volumes/) sú preferovaný mechanizmus pre trvalé ukladanie dát kontajnera.
+**Poznámka**: Pod kapotou sa použije príkaz Remote-Containers: **Clone Repository in Container Volume...** na klonovanie zdrojového kódu do Docker volume namiesto lokálneho súborového systému. [Volumes](https://docs.docker.com/storage/volumes/) sú preferovaný mechanizmus na uchovávanie dát kontajnerov.
 
-Alebo otvorte lokálne naklonovanú alebo stiahnutú verziu repozitára:
+Alebo otvorte lokálnu sklonovanú alebo stiahnutú verziu repozitára:
 
-- Naklonujte tento repozitár na váš lokálny súborový systém.
+- Sklonujte tento repozitár na váš lokálny súborový systém.
 - Stlačte F1 a vyberte príkaz **Remote-Containers: Open Folder in Container...**.
-- Vyberte naklonovanú kópiu tohto priečinka, počkajte, kým kontajner spustí, a začnite skúmať.
+- Vyberte sklonovanú kópiu tohto priečinka, počkajte na spustenie kontajnera a vyskúšajte si to.
 
 ## Prístup offline
 
-Túto dokumentáciu môžete používať offline pomocou [Docsify](https://docsify.js.org/#/). Vytvorte si fork tohto repozitára, [nainštalujte Docsify](https://docsify.js.org/#/quickstart) na vašom lokálnom zariadení, potom v koreňovom priečinku repozitára zadajte príkaz `docsify serve`. Webstránka vám bude servírovaná na porte 3000 na localhoste: `localhost:3000`.
+Túto dokumentáciu môžete spustiť offline pomocou [Docsify](https://docsify.js.org/#/). Zforkujte tento repozitár, [nainštalujte Docsify](https://docsify.js.org/#/quickstart) na váš lokálny počítač, potom v koreňovom priečinku tohto repozitára zadajte príkaz `docsify serve`. Webová stránka bude dostupná na porte 3000 na vašom localhoste: `localhost:3000`.
 
-> Poznámka: Notebooky nebudú renderované cez Docsify, takže keď potrebujete spustiť notebook, robte to samostatne vo VS Code s bežiacim Python kernelom.
+> Poznámka, notebooky nebudú cez Docsify renderované, takže ak potrebujete spustiť notebook, urobte to samostatne vo VS Code s bežiacim Python kernelom.
 
 ## Iné kurikuly
 
-Náš tím tvorí aj ďalšie kurikuly! Pozrite si:
+Náš tím vytvára aj iné kurikuly! Pozrite si:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j pre začiatočníkov](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
+[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
 [![LangChain.js pre začiatočníkov](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
 [![LangChain pre začiatočníkov](https://img.shields.io/badge/LangChain%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://github.com/microsoft/langchain-for-beginners?WT.mc_id=m365-94501-dwahlin)
 ---
 
-### Azure / Edge / MCP / Agenti
+### Azure / Edge / MCP / Agentúry
 [![AZD pre začiatočníkov](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Edge AI pre začiatočníkov](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![MCP pre začiatočníkov](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![AI Agentov pre začiatočníkov](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI agenti pre začiatočníkov](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### Séria generatívnej AI
+### Séria generatívnej umelej inteligencie
 [![Generatívna AI pre začiatočníkov](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Generatívna AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
 [![Generatívna AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
@@ -220,34 +230,34 @@ Náš tím tvorí aj ďalšie kurikuly! Pozrite si:
 [![ML pre začiatočníkov](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
 [![Dátová veda pre začiatočníkov](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
 [![AI pre začiatočníkov](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![Kyberbezpečnosť pre začiatočníkov](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![Web vývoj pre začiatočníkov](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![Kybernetická bezpečnosť pre začiatočníkov](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![Webový vývoj pre začiatočníkov](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
 [![IoT pre začiatočníkov](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![Vývoj XR pre začiatočníkov](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR vývoj pre začiatočníkov](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Séria Copilot
-[![Copilot pre AI párované programovanie](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot pre programovanie v pároch s AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
 [![Copilot pre C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
 [![Copilot dobrodružstvo](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## Získavanie pomoci
+## Získanie pomoci
 
-**Máte problémy?** Pozrite si náš [Sprievodca riešením problémov](TROUBLESHOOTING.md) pre riešenia bežných problémov.
+**Máte problémy?** Prezrite si náš [Príručka riešenia problémov](TROUBLESHOOTING.md) pre riešenia bežných problémov.
 
-Ak budete mať zádrhel alebo otázky o vývoji AI aplikácií, pridajte sa k ostatným študentom a skúseným vývojárom v diskusiách o MCP. Je to podporná komunita, kde sú otázky vítané a vedomosti sa zdieľajú bezplatne.
+Ak sa zaseknete alebo máte akékoľvek otázky týkajúce sa tvorby AI aplikácií, pripojte sa k ďalším študentom a skúseným vývojárom v diskusiách o MCP. Je to podporujúca komunita, kde sú otázky vítané a zdieľanie vedomostí je slobodné.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Ak máte spätnú väzbu k produktu alebo chyby počas vývoja, navštívte:
+Ak máte spätnú väzbu k produktu alebo pri tvorbe narazíte na chyby, navštívte:
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-**Zrieknutie sa zodpovednosti**:  
-Tento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, uvedomte si, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Originálny dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nepreberáme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.
+**Vyhlásenie o zodpovednosti**:  
+Tento dokument bol preložený pomocou AI prekladateľskej služby [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, uvedomte si, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Originálny dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/sk/sketchnotes/README.md b/translations/sk/sketchnotes/README.md
index f55e768f..295e2378 100644
--- a/translations/sk/sketchnotes/README.md
+++ b/translations/sk/sketchnotes/README.md
@@ -4,7 +4,7 @@ Nájdite všetky sketchnoty tu!
 
 Nitya Narasimhan, umelkyňa
 
-![sketchnota cestovnej mapy](../../../translated_images/sk/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)
+![sketchnota cestovnej mapy](../../../translated_images/sk/00-Roadmap.4905d6567dff4753.webp)
 
 ---