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MS&E 125

MS&E 125: Applied Statistics [Spring 2022]


  1. Course description
  2. Course staff
  3. Class schedule
  4. Grading policies
  5. Course communication
  6. Computing environment
  7. Attendance
  8. Textbooks (Optional)
  9. Access and accommodations
  10. Diversity statement
  11. Acknowledgements

Course description

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, and CS 106A or equivalent

Course staff

Josh Grossman (Instructor) (jgrossman at stanford dot edu)

Will Cai (CA) (willcai at stanford dot edu)

Alex Chohlas-Wood (CA) (alexcw at stanford dot edu)

Class schedule

Lecture: Tuesdays & Thursdays @ 9:45am - 11:15am PT at 380Y (in the basement of Building 380, the Sloan Math Corner)

  • If you would like to request some music to play at the beginning of lecture, please fill out this form!

Discussion Section: Tuesdays @ 11:30am - 1pm on Zoom

Office Hours:

  • Tuesdays @ 2pm - 4pm PT (Will, on Zoom)

  • Thursdays @ 11:15am - 1:15pm PT (Alex, on Zoom)

  • Thursdays @ 7pm - 9pm PT (Josh, on Zoom)

  • Fridays @ 10am - 12pm PT (Rotating instructors, on Zoom)

  • We may add or reschedule office hours based on demand and student availability. Please let the course staff know if you have conflicts with all of the current times.

For security reasons, the Zoom links for the discussion section and office hours are posted on Canvas.

Office hours are a great opportunity to discuss not only topics directly related to the course, but also anything else that’s on your mind beyond the class, including, for example, questions about career trajectories, and research opportunities in MS&E and beyond.

Please note that there are no regular office hours during the first week of class, but feel free to schedule an appointment if you would like to meet.

Grading policies

  • 7 homework assignments + project proposal (50%)
  • 2 take-home quizzes (25%)
  • Final project (20%)
  • Attendance and participation (5%)

Late homework will not be accepted, but your lowest homework grade will be dropped.

Unless otherwise stated, assignments are to be done individually. You are welcome to work with others to master the principles and approaches used to solve the homework problems, but the work you turn in should be your own. Under no circumstance should you seek out or look at solutions to assignments given in previous years.

Course communication

We use the Ed platform to manage course questions and discussion.

We highly encourage you to use our (anonymous) comment box to let us know which aspects of the class are going well and which could be improved.

Computing environment

We primarily use R, and RStudio is the recommended interface. We extensively use the suite of tidyverse packages.

Attendance

Office hours are not recorded. Lectures and the discussion section will be recorded, though we cannot guarantee audio or video quality.

  • Lecture attendance is mandatory, and lecture attendance is a substantial component of the participation grade. We will periodically conduct attendance checks. If you cannot attend a lecture due to an extenuating circumstance, please email Josh before lecture starts.

  • Laptops and phones are not allowed in lecture, though you are permitted to use a tablet to take handwritten notes. This article explains why we have this policy.

  • We highly encourage you to attend discussion sections, and be sure to bring your laptop. That being said, attendance is not mandatory. The discussion section will consist of one-on-one help with hands-on exercises that prepare you for the homework assignments.

Textbooks (Optional)

All of Statistics, by Larry Wasserman

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

All of the key resources for this class are avilable online, free of charge. However, please note that the MS&E department has an Opportunity Fund through which students may request financial assistance to purchase any necessary course materials.

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.

Acknowledgements

The MS&E 125 materials were adapted from course content developed by Sharad Goel. Thanks Sharad!