Textbook and Other Resources

Our primary textbook is

• Mining of Massive Datasets by Anand Rajaraman, Jure Leskovec and Jeffrey D. Ullman, 2nd edition. The website for the book provides links for buying a hardcopy of the book. You can also download the book’s contents free of charge. In the description below, we will refer to this book as MMDS. Please note that through this site, you also have access to all the videos of lectures from the Stanford MOOC course based on this book. I may assign some of these videos for required viewing so that we may concentrate at times on solving specific problems or understanding analyses related to the material in the book.

In addition to this book, we will also be using other web resources, notably the following:

1. Some videos of lectures on Machine Learning based on the book, Learning From Data, by Yaser S. Abu-Mostafa,Malik Magdon-Ismail and Hsuan-Tien Lin (2012)

2. Videos and tutorials on specific tools in the Python scientific computing/data analysis ecosystem:

   • Jupyter notebooks and Jupyter Lab
   • scikit-learn
   • pandas
   • Matplotlib

   and other selected packages. These can be installed altogether by using a distribution like Anaconda - please go ahead and install the Pythoin 3.7 version on your laptops.

3. This semester I may also use the Google Colab site from time to time.

4. We will most likely use the Google Cloud Platform for class project work. I will provide links to appropriate videos and tutorials in a few weeks once the GCP course credits have been set up.