diff --git a/1-Introduction/01-defining-data-science/README.md b/1-Introduction/01-defining-data-science/README.md
index 99ce3463..40e0d843 100644
--- a/1-Introduction/01-defining-data-science/README.md
+++ b/1-Introduction/01-defining-data-science/README.md
@@ -107,7 +107,7 @@ The first step is to collect the data.  While in many cases it can be a straight
 Storing data can be challenging, especially if we are talking about big data.  When deciding how to store data, it makes sense to anticipate the way you would to query the data in the future.  There are several ways data can be stored:
 <ul>
 <li>A relational database stores a collection of tables, and uses a special language called SQL to query them.  Typically, tables are organized into different groups called schemas.  In many cases we need to convert the data from original form to fit the schema.</li>
-<li><a href="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, such as <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, does not enforce schemas on data, and allows storing more complex data, for example, hierarchical JSON documents or graphs. However, NoSQL databases do not have the rich querying capabilities of SQL, and cannot enforce referential integrity, i.e. rules on how the data is structured in tables and governing the relationships between tables.</li>
+<li><a href="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, such as <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, does not enforce schemas on data, and allows storing more complex data, for example, hierarchical JSON documents or graphs. However, NoSQL databases do not have the rich querying capabilities of SQL, and cannot enforce referential integrity, i.e. rules on how the data is structured in tables and governing the relationships between tables.</li>
 <li><a href="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> storage is used for large collections of data in raw, unstructured form. Data lakes are often used with big data, where all data cannot fit on one machine, and has to be stored and processed by a cluster of servers. <a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> is the data format that is often used in conjunction with big data.</li> 
 </ul>
 </dd>
diff --git a/1-Introduction/01-defining-data-science/translations/README.es.md b/1-Introduction/01-defining-data-science/translations/README.es.md
index 5def22c2..86f80986 100644
--- a/1-Introduction/01-defining-data-science/translations/README.es.md
+++ b/1-Introduction/01-defining-data-science/translations/README.es.md
@@ -113,7 +113,7 @@ El primer paso es recoger los datos.  Aunque en muchos casos puede ser un proces
 El almacenamiento de datos puede ser un reto, especialmente si hablamos de big data. A la hora de decidir cómo almacenar los datos, tiene sentido anticiparse a la forma en que se consultarán los datos en el futuro.  Hay varias formas de almacenar los datos:
 <ul>
 <li>Una base de datos relacional almacena una colección de tablas y utiliza un lenguaje especial llamado SQL para consultarlas.  Normalmente, las tablas se organizan en diferentes grupos llamados esquemas.  En muchos casos hay que convertir los datos de la forma original para que se ajusten al esquema.</li>
-<li><a href="https://en.wikipedia.org/wiki/NoSQL">una base de datos no SQL</a>, como <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, no impone esquemas a los datos y permite almacenar datos más complejos, por ejemplo, documentos JSON jerárquicos o gráficos. Sin embargo, las bases de datos NoSQL no tienen las ricas capacidades de consulta de SQL, y no pueden asegurar la integridad referencial, i.e. reglas sobre cómo se estructuran los datos en las tablas y que rigen las relaciones entre ellas.</li>
+<li><a href="https://en.wikipedia.org/wiki/NoSQL">una base de datos no SQL</a>, como <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, no impone esquemas a los datos y permite almacenar datos más complejos, por ejemplo, documentos JSON jerárquicos o gráficos. Sin embargo, las bases de datos NoSQL no tienen las ricas capacidades de consulta de SQL, y no pueden asegurar la integridad referencial, i.e. reglas sobre cómo se estructuran los datos en las tablas y que rigen las relaciones entre ellas.</li>
 <li><a href="https://en.wikipedia.org/wiki/Data_lake">Los lagos de datos</a> se utilizan para grandes colecciones de datos en bruto y sin estructurar. Los lagos de datos se utilizan a menudo con big data, donde los datos no caben en una sola máquina, y tienen que ser almacenados y procesados por un clúster de servidores. <a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> es el formato de datos que se suele utilizar junto con big data.</li> 
 </ul>
 </dd>
diff --git a/1-Introduction/01-defining-data-science/translations/README.hi.md b/1-Introduction/01-defining-data-science/translations/README.hi.md
index 050e6c86..8d5c6848 100644
--- a/1-Introduction/01-defining-data-science/translations/README.hi.md
+++ b/1-Introduction/01-defining-data-science/translations/README.hi.md
@@ -106,7 +106,7 @@
 डेटा स्टोर करना चुनौतीपूर्ण हो सकता है, खासकर अगर हम बड़े डेटा के बारे में बात कर रहे हैं। डेटा को स्टोर करने का तरीका तय करते समय, भविष्य में डेटा को क्वेरी करने के तरीके का अनुमान लगाना समझ में आता है। डेटा को स्टोर करने के कई तरीके हैं:
 <ul>
 <li>एक रिलेशनल डेटाबेस तालिकाओं के संग्रह को संग्रहीत करता है, और उन्हें क्वेरी करने के लिए SQL नामक एक विशेष भाषा का उपयोग करता है। आमतौर पर, तालिकाओं को विभिन्न समूहों में व्यवस्थित किया जाता है जिन्हें स्कीमा कहा जाता है। कई मामलों में हमें स्कीमा को फिट करने के लिए डेटा को मूल रूप से परिवर्तित करने की आवश्यकता होती है।</li>
-<li><a href="https://en.wikipedia.org/wiki/NoSQL">एक NoSQL</a> डेटाबेस, जैसे कि <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, करता है डेटा पर स्कीमा लागू नहीं करता है, और अधिक जटिल डेटा संग्रहीत करने की अनुमति देता है, उदाहरण के लिए, पदानुक्रमित JSON दस्तावेज़ या ग्राफ़। हालाँकि, NoSQL डेटाबेस में SQL की समृद्ध क्वेरी क्षमता नहीं होती है, और यह संदर्भात्मक अखंडता को लागू नहीं कर सकता है, अर्थात डेटा को तालिकाओं में कैसे संरचित किया जाता है और तालिकाओं के बीच संबंधों को नियंत्रित करने के नियम।</li>
+<li><a href="https://en.wikipedia.org/wiki/NoSQL">एक NoSQL</a> डेटाबेस, जैसे कि <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, करता है डेटा पर स्कीमा लागू नहीं करता है, और अधिक जटिल डेटा संग्रहीत करने की अनुमति देता है, उदाहरण के लिए, पदानुक्रमित JSON दस्तावेज़ या ग्राफ़। हालाँकि, NoSQL डेटाबेस में SQL की समृद्ध क्वेरी क्षमता नहीं होती है, और यह संदर्भात्मक अखंडता को लागू नहीं कर सकता है, अर्थात डेटा को तालिकाओं में कैसे संरचित किया जाता है और तालिकाओं के बीच संबंधों को नियंत्रित करने के नियम।</li>
 <li><a href="https://en.wikipedia.org/wiki/Data_lake">डेटा लेक</a> संग्रहण का उपयोग कच्चे, असंरचित रूप में डेटा के बड़े संग्रह के लिए किया जाता है। डेटा झीलों का उपयोग अक्सर बड़े डेटा के साथ किया जाता है, जहां सभी डेटा एक मशीन पर फिट नहीं हो सकते हैं, और सर्वरों के एक समूह द्वारा संग्रहीत और संसाधित किया जाना है। <a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> डेटा प्रारूप है जिसे अक्सर बड़े डेटा के संयोजन में उपयोग किया जाता है।</li>
 </ul>
 </dd>
diff --git a/1-Introduction/01-defining-data-science/translations/README.ko.md b/1-Introduction/01-defining-data-science/translations/README.ko.md
index 0de83124..19b5ecd4 100644
--- a/1-Introduction/01-defining-data-science/translations/README.ko.md
+++ b/1-Introduction/01-defining-data-science/translations/README.ko.md
@@ -107,7 +107,7 @@
 특히 빅 데이터의 경우에, 데이터를 저장하는 것은 어려울 수 있습니다. 데이터를 저장하는 방법을 결정할 때는 나중에 데이터를 쿼리할 방법을 예상하는 것이 좋습니다. 데이터를 저장할 수 있는 방법에는 여러 가지가 있습니다.
 <ul>
 <li>관계형 데이터베이스는 테이블 모음을 저장하고 SQL이라는 특수 언어를 사용하여 쿼리합니다. 일반적으로 테이블은 어떤 스키마를 사용하여 서로 연결됩니다. 많은 경우 스키마에 맞게 원래 형식의 데이터를 변환해야 합니다.</li>
-<li><a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=acad-31812-dmitryso">CosmosDB</a>와 같은 <a href="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a> 데이터베이스는 데이터에 스키마를 적용하지 않으며, 계층적 JSON 문서 또는 그래프와 같은 더 복잡한 데이터를 저장할 수 있습니다. 그러나 NoSQL 데이터베이스는 SQL의 풍부한 쿼리 기능이 없으며 데이터 간의 참조 무결성을 강제할 수 없습니다.</li>
+<li><a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>와 같은 <a href="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a> 데이터베이스는 데이터에 스키마를 적용하지 않으며, 계층적 JSON 문서 또는 그래프와 같은 더 복잡한 데이터를 저장할 수 있습니다. 그러나 NoSQL 데이터베이스는 SQL의 풍부한 쿼리 기능이 없으며 데이터 간의 참조 무결성을 강제할 수 없습니다.</li>
 <li><a href="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> 저장소는 원시 형식(raw form)의 대규모 데이터 저장소로 사용됩니다. 데이터 레이크는 모든 데이터가 하나의 시스템에 들어갈 수 없고 클러스터에서 저장 및 처리를 해야하는 빅 데이터와 함께 사용하는 경우가 많습니다. <a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a>은 빅 데이터와 함께 자주 사용되는 데이터 형식입니다.</li> 
 </ul>
 </dd>
diff --git a/1-Introduction/01-defining-data-science/translations/README.nl.md b/1-Introduction/01-defining-data-science/translations/README.nl.md
index 20cde9b1..9cff07e8 100644
--- a/1-Introduction/01-defining-data-science/translations/README.nl.md
+++ b/1-Introduction/01-defining-data-science/translations/README.nl.md
@@ -108,7 +108,7 @@ De eerste stap is het verzamelen van de gegevens.  Hoewel het in veel gevallen e
 Het opslaan van gegevens kan een uitdaging zijn, vooral als we het hebben over big data.  Wanneer u beslist hoe u gegevens wilt opslaan, is het logisch om te anticiperen op de manier waarop u de gegevens in de toekomst zou opvragen.  Er zijn verschillende manieren waarop gegevens kunnen worden opgeslagen:
 <ul>
 <li>Een relationele database slaat een verzameling tabellen op en gebruikt een speciale taal genaamd SQL om deze op te vragen.  Tabellen zijn meestal georganiseerd in verschillene groepen die schema's worden genoemd.  In veel gevallen moeten we de gegevens van de oorspronkelijke vorm converteren naar het schema.</li>
-<li><a href="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, zoals <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-31812-dmitryso">CosmosDB</a>, dwingt geen schema's af op gegevens en maakt het opslaan van complexere gegevens mogelijk, bijvoorbeeld hiërarchische JSON-documenten of grafieken. NoSQL-databases hebben echter niet de uitgebreide querymogelijkheden van SQL en kunnen geen referentiële integriteit afdwingen, d.w.z. regels over hoe de gegevens in tabellen zijn gestructureerd en de relaties tussen tabellen regelen.</li>
+<li><a href="https://en.wikipedia.org/wiki/NoSQL">A NoSQL</a> database, zoals <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, dwingt geen schema's af op gegevens en maakt het opslaan van complexere gegevens mogelijk, bijvoorbeeld hiërarchische JSON-documenten of grafieken. NoSQL-databases hebben echter niet de uitgebreide querymogelijkheden van SQL en kunnen geen referentiële integriteit afdwingen, d.w.z. regels over hoe de gegevens in tabellen zijn gestructureerd en de relaties tussen tabellen regelen.</li>
 <li><a href="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> opslag wordt gebruikt voor grote verzamelingen gegevens in ruwe, ongestructureerde vorm. Data lakes worden vaak gebruikt met big data, waarbij alle data niet op één machine past en moet worden opgeslagen en verwerkt door een cluster van servers. <a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> is het gegevensformaat dat vaak wordt gebruikt in combinatie met big data.</li> 
 </ul>
 </dd>
diff --git a/1-Introduction/01-defining-data-science/translations/README.pt-br.md b/1-Introduction/01-defining-data-science/translations/README.pt-br.md
index 9f9a3fc2..6f5ade60 100644
--- a/1-Introduction/01-defining-data-science/translations/README.pt-br.md
+++ b/1-Introduction/01-defining-data-science/translations/README.pt-br.md
@@ -107,7 +107,7 @@ Primeiro passo é coletar os dados. Enquanto em muitos casos isso pode ser um pr
 Armazenar os dados pode ser desafiador, especialmente se estamos falando de big data. Enquanto decide como armazenar os dados, faz sentido antecipar a forma como você gostaria de consultá-los mais tarde. Existem diversas formas de como os dados podem ser armazenados:
 <ul>
 <li> Bancos de dados relacionais armazenam uma coleção de tabelas, e utilizam uma linguagem especial chamada SQL para consultá-los. Tipicamente, tabelas seriam conectadas umas às outras usando algum schema. Em vários casas nós precisamos converter os dados da forma original para ajustar al schema.</li>
-<li>Bancos de dados <a href="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a>, como <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=acad-31812-dmitryso">CosmosDB</a>, não impõe schema nos dados, e permite o armazenamento de dados mais complexos, como por exemplo, documentos hierárquicos JSON ou grafos. No entanto, bancos de dados NoSQL não possuem a capacidade rica de consulta do SQL, e não podem impor integridade referencial entre os dados.</li>
+<li>Bancos de dados <a href="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a>, como <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, não impõe schema nos dados, e permite o armazenamento de dados mais complexos, como por exemplo, documentos hierárquicos JSON ou grafos. No entanto, bancos de dados NoSQL não possuem a capacidade rica de consulta do SQL, e não podem impor integridade referencial entre os dados.</li>
 <li>Armazenamento em <a href="https://en.wikipedia.org/wiki/Data_lake">Data Lake</a> é usado para grandes coleções de dados na forma bruta. Data lakes são frequentemente usados para big data, onde todos não podem se encaixar em uma máquina, e precisam ser armazenados e processados por um cluster. <a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> é o formato de dado que é frequentemente usado em conjunção com big data.</li> 
 </ul>
 </dd>
diff --git a/1-Introduction/01-defining-data-science/translations/README.ru.md b/1-Introduction/01-defining-data-science/translations/README.ru.md
index 1887369a..b4444390 100644
--- a/1-Introduction/01-defining-data-science/translations/README.ru.md
+++ b/1-Introduction/01-defining-data-science/translations/README.ru.md
@@ -116,7 +116,7 @@
 <ul>
 <li>Реляционные базы данных хранят коллекцию таблиц и используют специальный язык запросов SQL. Обычно, таблицы соединены друг с другом по определённой схеме. Очень часто нам необходимо преобразовать данные, чтобы они подходили под схему.
 </li>
-<li><a href="https://ru.wikipedia.org/wiki/NoSQL">Нереляционные (NoSQL)</a> базы данных, такие как  <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=acad-31812-dmitryso">CosmosDB</a>, не навязывают строгую модель данных и позволяют хранить более сложные данные, например иерархические JSON документы или графы. С другой стороны, нереляционные базы данных не имеют широких возможностей языка SQL и не гарантируют ссылочной целостности данных.
+<li><a href="https://ru.wikipedia.org/wiki/NoSQL">Нереляционные (NoSQL)</a> базы данных, такие как  <a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>, не навязывают строгую модель данных и позволяют хранить более сложные данные, например иерархические JSON документы или графы. С другой стороны, нереляционные базы данных не имеют широких возможностей языка SQL и не гарантируют ссылочной целостности данных.
 </li>
 <li><a href="https://en.wikipedia.org/wiki/Data_lake">Озеро данных</a> - хранилище, используемое для больших коллекций "сырых" данных. Озёра данных часто встречаются в больших данных, когда все данные не помещаются в память одного компьютера и их необходимо хранить и обрабатывать вычислительным кластером. <a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a> - формат данных, часто применяемый в связке с большими данными.
 </li> 
diff --git a/2-Working-With-Data/07-python/README.md b/2-Working-With-Data/07-python/README.md
index 12078005..119b53cb 100644
--- a/2-Working-With-Data/07-python/README.md
+++ b/2-Working-With-Data/07-python/README.md
@@ -16,7 +16,7 @@ Data processing can be programmed in any programming language, but there are cer
 In this lesson, we will focus on using Python for simple data processing. We will assume basic familiarity with the language. If you want a deeper tour of Python, you can refer to one of the following resources:
 
 * [Learn Python in a Fun Way with Turtle Graphics and Fractals](https://github.com/shwars/pycourse) - GitHub-based quick intro course into Python Programming
-* [Take your First Steps with Python](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)
+* [Take your First Steps with Python](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)
 
 Data can come in many forms. In this lesson, we will consider three forms of data - **tabular data**, **text** and **images**.
 
@@ -230,7 +230,7 @@ While data very often comes in tabular form, in some cases we need to deal with
 
 In this challenge, we will continue with the topic of COVID pandemic, and focus on processing scientific papers on the subject. There is [CORD-19 Dataset](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) with more than 7000 (at the time of writing) papers on COVID, available with metadata and abstracts (and for about half of them there is also full text provided).
 
-A full example of analyzing this dataset using [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-31812-dmitryso) cognitive service is described [in this blog post](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/). We will discuss simplified version of this analysis.
+A full example of analyzing this dataset using [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum) cognitive service is described [in this blog post](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/). We will discuss simplified version of this analysis.
 
 > **NOTE**: We do not provide a copy of the dataset as part of this repository. You may first need to download the [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) file from [this dataset on Kaggle](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge). Registration with Kaggle may be required. You may also download the dataset without registration [from here](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html), but it will include all full texts in addition to metadata file.
 
@@ -242,15 +242,15 @@ Open [`notebook-papers.ipynb`](notebook-papers.ipynb) and read it from top to bo
 
 Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:
 
-* **Image Classification**, which can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso)
-* **Object Detection** to detect different objects in the image. Services such as [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso) can detect a number of common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso) model to detect some specific objects of interest.
-* **Face Detection**, including Age, Gender and Emotion detection. This can be done via [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso).
+* **Image Classification**, which can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum)
+* **Object Detection** to detect different objects in the image. Services such as [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) can detect a number of common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) model to detect some specific objects of interest.
+* **Face Detection**, including Age, Gender and Emotion detection. This can be done via [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum).
 
-All those cloud services can be called using [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-31812-dmitryso), and thus can be easily incorporated into your data exploration workflow. 
+All those cloud services can be called using [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum), and thus can be easily incorporated into your data exploration workflow. 
 
 Here are some examples of exploring data from Image data sources:
-* In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso), and then use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-31812-dmitryso) to build interpretable model.
-* In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso) to extract emotions on people on photographs from events, in order to try to understand what makes people happy. 
+* In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum), and then use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) to build interpretable model.
+* In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) to extract emotions on people on photographs from events, in order to try to understand what makes people happy. 
 
 ## Conclusion
 
@@ -271,7 +271,7 @@ Whether you already have structured or unstructured data, using Python you can p
 
 **Learning Python**
 * [Learn Python in a Fun Way with Turtle Graphics and Fractals](https://github.com/shwars/pycourse)
-* [Take your First Steps with Python](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)
+* [Take your First Steps with Python](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) Learning Path on [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)
 
 ## Assignment
 
diff --git a/2-Working-With-Data/07-python/translations/README.ko.md b/2-Working-With-Data/07-python/translations/README.ko.md
index 0adddb6b..fda2b7ef 100644
--- a/2-Working-With-Data/07-python/translations/README.ko.md
+++ b/2-Working-With-Data/07-python/translations/README.ko.md
@@ -16,7 +16,7 @@
 이 과정에서는 간단한 데이터 처리를 위해 파이썬을 사용하는 것에 초점을 맞출 것입니다. 사전에 파이썬에 익숙해질 필요가 있습니다. 파이썬에 대해 더 자세히 살펴보고 싶다면 다음 리소스 중 하나를 참조할 수 있습니다:
 
 * [Turtle Graphics와 Fractal로 Python을 재미있게 배우기](https://github.com/shwars/pycourse) - GitHub 기반 Python 프로그래밍에 대한 빠른 소개 과정
-* [Python으로 첫 걸음 내딛기](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso) - [Microsoft 학습](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)으로 이동하기
+* [Python으로 첫 걸음 내딛기](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) - [Microsoft 학습](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)으로 이동하기
 
 데이터는 다양한 형태로 나타날 수 있습니다. 이 과정에서 우리는 세 가지 형태의 데이터를 고려할 것입니다. - **표로 나타낸 데이터(tabular data)**, **텍스트(text)** and **이미지(images)**.
 
@@ -230,7 +230,7 @@ df = pd.read_csv('file.csv')
 
 이 도전과제에서 우리는 COVID 팬데믹이라는 주제를 계속해서 다룰 것이며 해당 주제에 대한 과학 논문을 처리하는 데 집중할 것입니다. 메타데이터 및 초록과 함께 사용할 수 있는 COVID에 대한 7000개 이상의(작성 당시) 논문이 포함된 [CORD-19 데이터 세트](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)가 있습니다(이 중 약 절반에 대해 전체 텍스트도 제공됨). 
 
-[건강 인지 서비스를 위한 텍스트 분석](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-31812-dmitryso)를 사용하여 이 데이터 세트를 분석하는 전체 예는 이 블로그 게시물에 설명되어 있습니다. 우리는 이 분석의 단순화된 버전에 대해 논의할 것입니다.
+[건강 인지 서비스를 위한 텍스트 분석](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum)를 사용하여 이 데이터 세트를 분석하는 전체 예는 이 블로그 게시물에 설명되어 있습니다. 우리는 이 분석의 단순화된 버전에 대해 논의할 것입니다.
 
 > **주의**: 우리는 더이상 데이터 세트의 복사본을 이 리포지토리의 일부로 제공하지 않습니다. 먼저 [Kaggle의 데이터세트](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)에서 [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 파일을 다운로드해야 할 수도 있습니다. Kaggle에 가입해야 할 수 있습니다. [여기](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html)에서 등록 없이 데이터 세트를 다운로드할 수도 있지만 여기에는 메타데이터 파일 외에 모든 전체 텍스트가 포함됩니다.
 
@@ -242,15 +242,15 @@ df = pd.read_csv('file.csv')
 
 최근에는 이미지를 이해할 수 있는 매우 강력한 AI 모델이 개발되었습니다. 사전에 훈련된 신경망이나 클라우드 서비스를 사용하여 해결할 수 있는 작업이 많이 있습니다. 몇 가지 예는 다음과 같습니다:
 
-* **이미지 분류(Image Classification)** 는 이미지를 미리 정의된 클래스 중 하나로 분류하는 데 도움이 됩니다. [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso)과 같은 서비스를 사용하여 자신의 이미지 분류기를 쉽게 훈련할 수 있습니다.
-* **물체 검출** 은 이미지에서 다른 물체를 감지합니다. [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso)과 같은 서비스는 여러 일반 개체를 감지할 수 있으며 [커스텀 비전(Custom Vision)](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-31812-dmitryso) 모델을 훈련하여 관심 있는 특정 개체를 감지할 수 있습니다.
-* **얼굴 인식** 은 연령, 성별 및 감정 감지를 포함합니다. 이것은 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso)를 통해 수행할 수 있습니다.
+* **이미지 분류(Image Classification)** 는 이미지를 미리 정의된 클래스 중 하나로 분류하는 데 도움이 됩니다. [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum)과 같은 서비스를 사용하여 자신의 이미지 분류기를 쉽게 훈련할 수 있습니다.
+* **물체 검출** 은 이미지에서 다른 물체를 감지합니다. [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum)과 같은 서비스는 여러 일반 개체를 감지할 수 있으며 [커스텀 비전(Custom Vision)](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) 모델을 훈련하여 관심 있는 특정 개체를 감지할 수 있습니다.
+* **얼굴 인식** 은 연령, 성별 및 감정 감지를 포함합니다. 이것은 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum)를 통해 수행할 수 있습니다.
 
-이러한 모든 클라우드 서비스는 [Python SDK](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-31812-dmitryso)를 사용하여 호출할 수 있으므로, 데이터 탐색 워크플로에 쉽게 통합할 수 있습니다.
+이러한 모든 클라우드 서비스는 [Python SDK](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum)를 사용하여 호출할 수 있으므로, 데이터 탐색 워크플로에 쉽게 통합할 수 있습니다.
  
 다음은 이미지 데이터 소스에서 데이터를 탐색하는 몇 가지 예입니다:
-* 블로그 게시물 중 [코딩 없이 데이터 과학을 배우는 방법](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/)에서 우리는 인스타그램 사진을 살펴보고 사람들이 사진에 더 많은 좋아요를 주는 이유를 이해하려고 합니다. 먼저 [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-31812-dmitryso)을 사용하여 사진에서 최대한 많은 정보를 추출한 다음 [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-31812-dmitryso)을 사용하여 해석 가능한 모델을 빌드합니다.
-* [얼굴 연구 워크숍(Facial Studies Workshop)](https://github.com/CloudAdvocacy/FaceStudies)에서는 사람들을 행복하게 만드는 요소를 이해하고자, 이벤트에서 사진에 있는 사람들의 감정을 추출하기 위해 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-31812-dmitryso)를 사용합니다.
+* 블로그 게시물 중 [코딩 없이 데이터 과학을 배우는 방법](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/)에서 우리는 인스타그램 사진을 살펴보고 사람들이 사진에 더 많은 좋아요를 주는 이유를 이해하려고 합니다. 먼저 [컴퓨터 비전(Computer vision)](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum)을 사용하여 사진에서 최대한 많은 정보를 추출한 다음 [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum)을 사용하여 해석 가능한 모델을 빌드합니다.
+* [얼굴 연구 워크숍(Facial Studies Workshop)](https://github.com/CloudAdvocacy/FaceStudies)에서는 사람들을 행복하게 만드는 요소를 이해하고자, 이벤트에서 사진에 있는 사람들의 감정을 추출하기 위해 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum)를 사용합니다.
 
 ## 결론
 
@@ -273,7 +273,7 @@ df = pd.read_csv('file.csv')
 **Python 학습**
 
 * [거북이 그래픽과 도형으로 재미있는 방식으로 파이썬 배우기(Learn Python in a Fun Way with Turtle Graphics and Fractals)](https://github.com/shwars/pycourse)
-* [파이썬으로 첫걸음(Take your First Steps with Python)](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-31812-dmitryso): 관련 강의 [Microsoft 강의](http://learn.microsoft.com/?WT.mc_id=academic-31812-dmitryso)
+* [파이썬으로 첫걸음(Take your First Steps with Python)](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum): 관련 강의 [Microsoft 강의](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)
 
 ## 과제