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kmoscoe merged 1 commit into from
Feb 6, 2026
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Update links to courseware colabs #672

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14 changes: 7 additions & 7 deletions courseware/intro_data_science.md
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Expand Up @@ -34,7 +34,7 @@ As an example, MIT's large [Introduction to Machine Learning](https://introml.od

We strongly encourage you to change and adapt these notebooks to fit your needs! You can download any notebook in either .ipynb or .py format by clicking on its link and **File > Download**.

Datasets can be changed by editing the list of variables queried (see the [Data Commons for Data Science](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/v2/intro_data_science/Data_Commons_For_Data_Science_Tutorial.ipynb) tutorial for more on this); editing framing and questions is as easy as editing text cells.
Datasets can be changed by editing the list of variables queried (see the [Data Commons for Data Science](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Data_Commons_For_Data_Science_Tutorial.ipynb) tutorial for more on this); editing framing and questions is as easy as editing text cells.

Some ideas:
* Add additional cells for any additional topics you want covered.
Expand All @@ -43,20 +43,20 @@ Some ideas:

## Python notebooks

* [**Data Commons for Data Science Tutorial**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/v2/intro_data_science/Data_Commons_For_Data_Science_Tutorial.ipynb) \\
* [**Data Commons for Data Science Tutorial**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Data_Commons_For_Data_Science_Tutorial.ipynb) \\
A quick tutorial introducing the key concepts of working with the Data Commons Python API. Great for familiarizing yourself with how to adapt datasets to your particular needs.

* [**Feature Engineering**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/v2/intro_data_science/Feature_Engineering.ipynb) \\
* [**Feature Engineering**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Feature_Engineering.ipynb) \\
Explores the first steps of any data science pipeline: feature selection, data visualization, preprocessing and standardization. Pairs well with “Classification and Model Evaluation”.

* [**Classification and Model Evaluation**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/v2/intro_data_science/Classification_and_Model_Evaluation.ipynb) \\
* [**Classification and Model Evaluation**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Classification_and_Model_Evaluation.ipynb) \\
Explores the second half of a data science pipeline: training and test splits, cross validation, metrics for model evaluation. Focus is on classification models. Pairs well with “Feature Engineering”.

* [**Regression: Basics and Prediction**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/v2/intro_data_science/Regression_Basics_and_Prediction.ipynb) \\
* [**Regression: Basics and Prediction**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Regression_Basics_and_Prediction.ipynb) \\
An introduction to linear regression as a tool for prediction, from a modern machine learning perspective.

* [**Regression: Evaluation and Interpretation**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/v2/intro_data_science/Regression_Evaluation_and_Interpretation.ipynb) \\
* [**Regression: Evaluation and Interpretation**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Regression_Evaluation_and_Interpretation.ipynb) \\
A more in-depth look at linear regression, with an emphasis on interpreting model parameters and evaluation metrics beyond simple accuracy. Provides a more statistical perspective.

* [**Clustering**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/v2/intro_data_science/Introduction_to_Clustering.ipynb) \\
* [**Clustering**](https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Introduction_to_Clustering.ipynb) \\
An introduction to clustering analysis for unsupervised learning. Explores the mechanics of K-means clustering and cluster interpretation.
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