MS&E 125: Introduction to Applied Statistics


Quick Links:

  1. Course Description
  2. Course Staff
  3. Class Schedule
  4. Grading Components
    1. Homework
    2. Quizzes
    3. Final Project
    4. Attendance
  5. Lecture Laptops Policy
  6. Course Communication
  7. Study Groups
  8. Policy on Large Language Models (LLMs)
  9. Access and Accommodations
  10. Academic Coaching
  11. Diversity statement
  12. Acknowledgements

Course Description

Effectively analyzing data plays a crucial role across various disciplines, enabling data-driven decision-making, knowledge discovery, and predictive modeling. This course provides an introduction to many foundational tools in applied data science, including data manipulation, hypothesis testing, and linear regression. The curriculum combines mathematical concepts with practical examples, placing a strong emphasis on developing programming skills in Python.

Prerequisites: Math 51 or CME 100 or equivalent.

Recommended Prerequisites: MS&E 120 or equivalent, CS 106A or equivalent.

Course Staff

Mike Van Ness (Instructor) (mvanness at stanford dot edu)

  • You can often find me at my desk on the second floor of Huang.

Alex Patel (CA) (patel24 at stanford dot edu)

Camila Nicollier Sanchez (CA) (camilans at stanford dot edu)

Elizabeth Griffin (CA) (elizg at stanford dot edu)

Louisa Edwards (CA) (edwardsl at stanford dot edu)

Class Schedule

Lecture: Tuesdays & Thursdays @ 1:30pm - 2:45pm PT at Hewlett Teaching Center 201

In-person lecture attendance is required.

Office Hours:

  • Cami: Mon 3:30-4:30pm Huang B007
  • Elizabeth: Tue 4:30-5:30pm Huang B019
  • Alex: Wed 10-11am Huang B019
  • Louisa: Wed 5:30-6:30pm Huang B019

  • Mike: Thu 10-11am Huang B020

  • 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.

Extra Probability Lecture:

We will be holding an optional lecture on probability on Monday April 7th at 8pm on Zoom. This lecture is intended for students without previous exposure to probability, but is worth attending for any students looking to brush up on probability fundamentals. The lecture will be recorded and posted to Canvas.

Grading Components

The final grade breakdown is as follows:

  • Homework (40%)
  • Quizzes (30%)
  • Final project (20%)
  • Attendance (10%)

Homework

All homework assignments are to be completely individually unless otherwise stated. You may discuss homework assignments at a high level with other students, but all final work must be your own.

There will be 6 homework assignments throughout the quarter. Homeworks assignments will generally be due Thursdays at 11:59pm.

Each student is alloted 5 slip days for homework assignments.

  • Each slip day adds 24 hours to the original deadline.
  • You may only use a maximum of 2 slip days per assignment. This ensures that homework solutions can be released in a timely manner.
  • Additional extension will only be granted with an OAE accommodation letter, or extraordinary circumstances.

Extra coding HW0: An optional HW0 is available now for those looking for an introduction to the Python programming language.

Quizzes

There will be 2 in-lecture quizzes. These quizzes are currently scheduled for May 1st and June 3rd. The second quiz will be cumulative and will act as a final exam. There will be a redemption policy for Quiz 2, meaning that if you score higher on Quiz 2 than on Quiz 1, your Quiz 2 score will replace your Quiz 1 score. There will be no additional exam during the final exam period.

Final Project

The course final project will give you hands-on experience doing data analysis on a dataset of your choosing. The project will completed in teams of 3, and will contain a written report and a presentation. The due date will be sometime during the final exam period. For more details, see link to project coming soon.

Attendance

In-person attendance is required for all lectures. If you attend all lectures, you will receive a 100% participation grade. To track lecture attendance, there will be an ungraded concept check to complete at the beginning of every class.

If you cannot attend a lecture because of an excused absence, please fill out this Google Form before the lecture. Excused asbences include:

  • Illness.
  • Personal emergencies.
  • Important life or professional events.
  • Pre-planned athletic events or travel.

Lecture recordings will be available for a limited time for those who miss lecture with an excused absence.

Note: lecture attendance will not be recorded for week 1 due to fluctuation in enrollment.

Lecture Laptops Policy

Laptops and tablets with attached keyboard are not allowed during lectures. This is in response to recent research that suggests that laptop use during lectures, especially for work unrelated to the course, reducing learning outcomes for students (see e.g. this paper). You’re welcome to use tablets during lecture that lie flat for note taking, reviewing course content, etc. If you feel you would benefit from being able to use a tablet but don’t own a tablet, consider renting one from Lanthrop for the quarter. If you need to use a laptop during lecture for OAE-related reasons, place notify the course staff. Repeated use of laptops during lecture without excuse will result in a reduced attendance grade.

Course Communication

We use the Ed platform to manage course questions and discussion. We encourage students to make public Ed posts whenever possible so that all students can learn from the resulting discussion. Please make private Ed posts when needed instead of emailing course staff, unless you have a question or concern you’d only like to share with an individual member of the course staff.

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 Monday, April 8 at 5pm PT. Our goal is to form the study groups the following day, so students can begin discussing the first homework assignment.

Policy on Large Language Models (LLMs)

LLMs like ChatGPT are becoming increasingly essential in the workplace. To that end, the use of LLMs is not only permitted in this course, but encouraged. Use this course as an opportunity to learn where LLMs are most useful, and where they fall short.

Many coding problems from past iterations of the course can now be fully solved by freely-available LLMs. With this advantange in mind, the difficulty and extent of coding required for this course may be increased compared to previous years. Other aspects of the course including in-person quizzes and project presentations will not be affected by LLMs.

Lastly, while we encourage the use of LLMs, we strongly discourage copying the output of an LLM directly to answer a homework question. An increasingly important skill is evaluating the output of an LLM and using only the parts that are correct. To that end, we may deduct points from homework assignments if multiple answers appear to be taken directly from the output of an LLM. This is especially true for non-coding questions, where writing responses in your own words is critical.

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.

Academic Coaching

Want help navigating the ups and downs of academic life at Stanford? Feeling overwhelmed by your coursework, anxious about exams, or simply curious if the way you’re studying is actually working for you? Sign up for a free 1:1 academic coaching session. Academic Coaches can help you come up with a personalized action plan to address things like procrastination, motivation, focus, time management, exam preparation and anxiety, reading & note-taking, learning as a neurodivergent student, and much more. Sign up at academicskills.stanford.edu, and check out other free Academic Skills resources at studentlearning.stanford.edu.

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 Josh Grossman, Madeleine Udell, and Sharad Goel.