diff --git a/courseware/intro_data_science.md b/courseware/intro_data_science.md
index bb5af94e3..ed098df44 100644
--- a/courseware/intro_data_science.md
+++ b/courseware/intro_data_science.md
@@ -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.
@@ -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.