Add links to blog post on how to execute notebooks

1-Introduction/01-defining-data-science/README.md

@@ -143,6 +143,11 @@ If we want to get even more complicated, we can plot the time taken for each mod
 In this challenge, we will try to find concepts relevant to the field of Data Science by looking at texts. We will take Wikipedia article on Data Science, download and process the text, and then build a word cloud like this one:
 
 ![Word Cloud for Data Science](images/ds_wordcloud.png)
+
+Visit [`notebook.ipynb`](notebook.ipynb) to read through the code. You can also run the code, and see how it performs all data transformations in real time. 
+
+> If you do not know how to run code in Jupyter Notebook, have a look at [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
+
 ## Post-Lecture Quiz
 
 [Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/1)

1-Introduction/04-stats-and-probability/README.md

@@ -65,7 +65,7 @@ When we analyze data from real life, they often are not random variables as such
 [180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]
 ```
 
-> **Note**: To see the example of working with this dataset, have a look at the [accompanying notebook](notebook.ipynb). There is also a number of challenges throughout this lesson, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time.
+> **Note**: To see the example of working with this dataset, have a look at the [accompanying notebook](notebook.ipynb). There is also a number of challenges throughout this lesson, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time. If you do not know how to run code in Jupyter Notebook, have a look at [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
 
 Here is the box plot showing mean, median and quartiles for our data:
 

2-Working-With-Data/07-python/README.md

@@ -213,6 +213,8 @@ Since we want to demonstrate how to deal with data, we invite you to open [`note
 
 ![COVID Spread](images/covidspread.png)
 
+> If you do not know how to run code in Jupyter Notebook, have a look at [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
+
 ## Working with Unstructured Data
 
 While data very often comes in tabular form, in some cases we need to deal with less structured data, for example, text or images. In this case, to apply data processing techniques we have seen above, we need to somehow **extract** structured data. Here are a few examples: