About

Table of contents

1. Course description and prerequisites
2. Resources
3. Study groups
4. Computing environment
5. Access and accommodations
6. Diversity statement

Course description and prerequisites

An increasing amount of data is now generated in a variety of disciplines, ranging from finance and economics, to the natural and social sciences. Making use of this information requires both statistical tools and an understanding of how the substantive scientific questions should drive the analysis. In this hands-on course, we learn to explore and analyze real-world datasets. We cover techniques for summarizing and describing data, methods for statistical inference, and principles for effectively communicating results.

Prerequisites:

• MS&E 120 or equivalent
• CS 106A or equivalent

Resources

The following textbooks may be useful, and are available free of charge. The treatment in the course draws mostly from All of Statistics and Computational and Inferential Thinking.

All of Statistics, by Larry Wasserman

Computational and Inferential Thinking: The Foundations of Data Science, by Ani Adhikari, John DeNero, and David Wagner.

R for Data Science, by Garrett Grolemund and Hadley Wickham

Statistics, by David Freedman, Robert Pisani, and Roger Purves

Natural Experiments in the Social Sciences, by Thad Dunning

While the books above are free, note that the MS&E department has an Opportunity Fund through which students may request financial assistance to purchase any necessary course materials.

Study groups

We encourage you to work together in groups to solidify your understanding of the course material. If you would like assistance forming a study group, please complete this form by Thursday, April 6 at 9pm PT. Our goal is to form the study groups the following day, so students can begin discussing the first homework assignment.

Computing environment

Most course assignments will be completed in Jupyter notebooks in the Python programming language. We will demonstrate how to set up Google Colab as a computing environment for the class.

Access and accommodations

Stanford is committed to providing equal educational opportunities for students with disabilities.

If you experience disability, please register with the Office of Accessible Education (OAE). Professional staff will evaluate your needs, support appropriate and reasonable accommodations, and prepare an Academic Accommodation Letter for faculty. To get started, or to re-initiate services, please visit oae.stanford.edu.

If you already have an Academic Accommodation Letter, we invite you to share your letter with us. Academic Accommodation Letters should be shared at the earliest possible opportunity so we may partner with you and OAE to identify any barriers to access and inclusion that might be encountered in your experience of this course.

Diversity statement

It is our intent that students from all backgrounds and perspectives be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength, and benefit. We aim to present materials and conduct activities in ways that are respectful of this diversity. Your suggestions are encouraged and appreciated. Please let us know if you have ideas to improve the effectiveness of the course for you personally or for other students or student groups.