diff --git a/translations/da/.co-op-translator.json b/translations/da/.co-op-translator.json
index 6393bd83..d501cb01 100644
--- a/translations/da/.co-op-translator.json
+++ b/translations/da/.co-op-translator.json
@@ -12,8 +12,8 @@
"language_code": "da"
},
"1-Introduction/01-defining-data-science/notebook.ipynb": {
- "original_hash": "8f5eb7b3f7cc89e6d98fb32e1de65dec",
- "translation_date": "2026-02-27T09:45:55+00:00",
+ "original_hash": "8eb07e3d899aaba7ea9c9ec9b5aa2850",
+ "translation_date": "2026-07-17T22:59:22+00:00",
"source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
"language_code": "da"
},
@@ -42,8 +42,8 @@
"language_code": "da"
},
"1-Introduction/03-defining-data/README.md": {
- "original_hash": "12339119c0165da569a93ddba05f9339",
- "translation_date": "2025-09-05T22:09:02+00:00",
+ "original_hash": "7217c6f9eb464d4ff4bae6c036ab53cb",
+ "translation_date": "2026-07-17T23:05:51+00:00",
"source_file": "1-Introduction/03-defining-data/README.md",
"language_code": "da"
},
@@ -65,6 +65,12 @@
"source_file": "1-Introduction/04-stats-and-probability/assignment.md",
"language_code": "da"
},
+ "1-Introduction/04-stats-and-probability/notebook.ipynb": {
+ "original_hash": "0c77e104b29c723287bf11b773fcc5d5",
+ "translation_date": "2026-07-17T23:00:23+00:00",
+ "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
+ "language_code": "da"
+ },
"1-Introduction/README.md": {
"original_hash": "696a8474a01054281704cbfb09148949",
"translation_date": "2025-08-26T21:14:00+00:00",
@@ -108,8 +114,8 @@
"language_code": "da"
},
"2-Working-With-Data/07-python/notebook-covidspread.ipynb": {
- "original_hash": "6335cccba01dc6ad7b15aba7a8c73f38",
- "translation_date": "2026-02-27T09:47:17+00:00",
+ "original_hash": "da97f98781c7cd271063f605fe8d71d0",
+ "translation_date": "2026-07-17T22:58:54+00:00",
"source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
"language_code": "da"
},
@@ -192,14 +198,14 @@
"language_code": "da"
},
"3-Data-Visualization/13-meaningful-visualizations/solution/README.md": {
- "original_hash": "5c51a54dd89075a7a362890117b7ed9e",
- "translation_date": "2025-08-26T22:47:11+00:00",
+ "original_hash": "2b3b2632bb02af608ca60be828e8097d",
+ "translation_date": "2026-07-17T23:05:58+00:00",
"source_file": "3-Data-Visualization/13-meaningful-visualizations/solution/README.md",
"language_code": "da"
},
"3-Data-Visualization/13-meaningful-visualizations/starter/README.md": {
- "original_hash": "5c51a54dd89075a7a362890117b7ed9e",
- "translation_date": "2025-08-26T22:46:36+00:00",
+ "original_hash": "2b3b2632bb02af608ca60be828e8097d",
+ "translation_date": "2026-07-17T23:05:55+00:00",
"source_file": "3-Data-Visualization/13-meaningful-visualizations/starter/README.md",
"language_code": "da"
},
diff --git a/translations/da/1-Introduction/01-defining-data-science/notebook.ipynb b/translations/da/1-Introduction/01-defining-data-science/notebook.ipynb
index 418953b3..5da0a686 100644
--- a/translations/da/1-Introduction/01-defining-data-science/notebook.ipynb
+++ b/translations/da/1-Introduction/01-defining-data-science/notebook.ipynb
@@ -2,420 +2,319 @@
"cells": [
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"# Udfordring: Analyse af tekst om datalogi\n",
"\n",
- "I dette eksempel laver vi en simpel øvelse, der dækker alle trin i en traditionel datalogiproces. Du behøver ikke at skrive nogen kode, du kan blot klikke på cellerne nedenfor for at køre dem og observere resultatet. Som en udfordring opfordres du til at prøve denne kode med forskellige data.\n",
+ "I dette eksempel laver vi en simpel øvelse, der dækker alle trin i en traditionel data science-proces. Du behøver ikke at skrive noget kode, du kan bare klikke på cellerne nedenfor for at udføre dem og observere resultatet. Som en udfordring opfordres du til at prøve denne kode med forskellige data.\n",
"\n",
"## Mål\n",
"\n",
- "I denne lektion har vi diskuteret forskellige begreber relateret til datalogi. Lad os prøve at opdage flere relaterede begreber ved at lave noget **tekstmining**. Vi starter med en tekst om datalogi, udtrækker nøgleord fra den og prøver derefter at visualisere resultatet.\n",
+ "I denne lektion har vi diskuteret forskellige begreber relateret til Data Science. Lad os prøve at opdage flere relaterede begreber ved at lave noget **tekstudvinding**. Vi starter med en tekst om Data Science, udtrækker nøgleord fra den, og prøver derefter at visualisere resultatet.\n",
"\n",
- "Som tekst vil jeg bruge siden om datalogi fra Wikipedia:\n"
- ],
- "metadata": {}
+ "Som tekst vil jeg bruge siden om Data Science fra Wikipedia:\n"
+ ]
},
{
"cell_type": "markdown",
- "source": [],
- "metadata": {}
+ "metadata": {},
+ "source": []
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"url = 'https://en.wikipedia.org/wiki/Data_science'"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "## Trin 1: Hent dataene\n",
+ "## Trin 1: Indhent dataene\n",
"\n",
- "Første trin i enhver data science-proces er at hente dataene. Vi vil bruge `requests` biblioteket til det:\n"
- ],
- "metadata": {}
+ "Det første trin i enhver datavidenskabsproces er at hente dataene. Vi vil bruge `requests` biblioteket til det:\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "import requests\r\n",
- "\r\n",
- "text = requests.get(url).content.decode('utf-8')\r\n",
- "print(text[:1000])"
- ],
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\n",
- "\n",
- "\n",
- "\n",
- "Data science - Wikipedia\n",
- "