Statistics & Data Science MS Overview

Program Overview

The M.S. in Statistics and Data Science are terminal degree programs that are designed to prepare individuals for career placement following degree completion. The M.S. does not directly lead to admission to the Statistics Ph.D. program however, those with a strong academic record in statistics and probability theory, and demonstrate promising ability to conduct in-depth research should consider applying to the doctoral program in Statistics.

Students are expected to live within commuting distance of Stanford campus to ensure significant engagement with the department and faculty. Students are not required to live on-campus (graduate housing), but many find it more conducive due to competitive rental market in neighboring cities and transportation logistics.

Department orientation for new Stats and DS students

Our mandatory New Student Orientation typically takes place on the Thursday before Autumn Quarter classes begin. I will offer an online meeting in August to explain enrollment and best practices.

University orientation events will be announced in September. These are hosted by the Graduate Life Office (GLO) and known by their acronym, NGSO. Students should plan to arrive on campus one to two weeks before the start of classes for the quarter.

Familiarize yourself with the Academic Calendar to anticipate pending deadlines throughout your time in the program.

2024-25 First Days of Classes and End of Terms

(These dates are subject to change at the discretion of the University.)

  • Autumn 2024-25: September 26 (Monday) and December 1
    • Winter break: December 16 – January 3
  • Winter 2024-25: January 6 and March 21
    • Spring break: March 24 – March 28
  • Spring 2024-25: March 31 and June 11
  • Summer 2024-25: June 23 and August 16

 

(updated Feb. 2024)

Length of the program

Students typically finish the degree program in 5 or 6 quarters (excluding summer). With a vast schedule of awesome courses offered during the year, the idea of staying longer is quite appealing to many, but one must weigh the cost of tuition and living expenses of enrolling beyond the degree's required 45 units. 

For those who can manage more than three courses each quarter, enrolling in 11+ units of required courses would allow a student to complete the degree in a shorter period of time (less cost of living/housing expenses).

We advise students to take 1-2 required courses each quarter and an elective course of interest in order to make satisfactory degree progress.

Suggested first quarter enrollment

First quarter enrollment example for the Statistics MS:

TBA

Theory of Probability I (STATS 118)

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem.

Prerequisites: Integral Calculus of Several Variables (Math 52) and familiarity with infinite series, or equivalent (4 units)

After taking Stats 118, the students should be able to:

  • Understand the principles of probability in discrete and continuous cases without measure theoretic detail.116Apply counting techniques to solve probability problems in spaces with regularity or symmetry.
  • Recognize important distributions in the exponential families and their connections.
  • Apply probability models to real-world situations, and recognize famous problems in disguise, like the Birthday problem, the Ballot problem, and the Matching problem.
  • Derive expectations and variances of random variables in structured probability spaces.
  • Exploit probabilistic symmetries to solve simple problems.
  • Understand results such as the Central Limit Theorem and Poisson approximation, and recognize their importance in statistical applications.
  • Gain familiarity with more advance topics in probability.

February 2024

Data Mining and Analysis (STATS 202)

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case-based methods, and data visualization.

Prerequisites: Introductory courses in statistics or probability (e.g., STATS 60), linear algebra (e.g., MATH 51), and computer programming (e.g., CS 105) (3 units)

After taking STATS 202 the students should be able to:

  • Understand the distinction between supervised and unsupervised learning and be able to identify appropriate tools to answer different research questions.
  • Become familiar with basic unsupervised procedures including clustering and principal components analysis.
  • Become familiar with the following regression and classification algorithms: linear regression, ridge regression, the lasso, logistic regression, linear discriminant analysis, K-nearest neighbors, splines, generalized additive models, tree-based methods, and support vector machines.
  • Gain a practical appreciation of the bias-variance tradeoff and apply model selection methods based on cross-validation and bootstrapping to a prediction challenge.
  • Analyse a real dataset of moderate size using either R or Python.
  • Develop the computational skills for data wrangling, collaboration, and reproducible research.
  • Be exposed to other topics in machine learning, such as missing data, prediction using time series and relational data, non-linear dimensionality reduction techniques, web-based data visualizations, anomaly detection, and representation learning.
Applied Matrix Theory (MATH 104)

Linear algebra for applications in science and engineering: orthogonality, projections, spectral theory for symmetric matrices, the singular value decomposition, the QR decomposition, least-squares, the condition number of a matrix, algorithms for solving linear systems. MATH 113 offers a more theoretical treatment of linear algebra. MATH 104 and ENGR 108 cover complementary topics in applied linear algebra. The focus of MATH 104 is on algorithms and concepts; the focus of ENGR 108 is on a few linear algebra concepts, and many applications.

Prerequisites: Intro linear algebra, multivariate calculus (MATH 51) and programming experience on par with CS 106. (3 units)

Learning objectives: Learn concepts and theorems well enough to formulate real world problems in the language of linear algebra and apply linear algebraic techniques to solve the problems.

First-quarter enrollment example for Stats-Data Science:

Introduction to Statistical Inference (STATS 200)

Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: STATS 116. Please note that students must enroll in one section in addition to the main lecture.


Terms: Aut, Win | Units: 4

Introduction to Causal Inference (STATS 209) - when offered

This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate adjustment, AIPW, heterogeneous treatment effects, instrumental variables, regression discontinuity, and synthetic controls. Prerequisites: basic probability and statistics, familiarity with R.


Terms: Aut | Units: 3

Data Mining and Analysis (STATS 202)

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case-based methods, and data visualization.

Prerequisites: Introductory courses in statistics or probability (e.g., STATS 60), linear algebra (e.g., MATH 51), and computer programming (e.g., CS 105) (3 units)

After taking STATS 202 the students should be able to:

  • Understand the distinction between supervised and unsupervised learning and be able to identify appropriate tools to answer different research questions.
  • Become familiar with basic unsupervised procedures including clustering and principal components analysis.
  • Become familiar with the following regression and classification algorithms: linear regression, ridge regression, the lasso, logistic regression, linear discriminant analysis, K-nearest neighbors, splines, generalized additive models, tree-based methods, and support vector machines.
  • Gain a practical appreciation of the bias-variance tradeoff and apply model selection methods based on cross-validation and bootstrapping to a prediction challenge.
  • Analyse a real dataset of moderate size using either R or Python.
  • Develop the computational skills for data wrangling, collaboration, and reproducible research.
  • Be exposed to other topics in machine learning, such as missing data, prediction using time series and relational data, non-linear dimensionality reduction techniques, web-based data visualizations, anomaly detection, and representation learning.

M.S. Program advisor assignments

M.S. program advisors assignments will be announced in September. MS advisor assignments are determined over the summer and will be announced in September. To ensure equity and easy distribution rules, students are assigned by their last name (alpha order).

If needed, you'll be able to discuss with your program advisor at the start of the quarter to help you determine the appropriate enrollment before the final study list deadline. Please see the information concerning course placement in the FAQ section below.

Independent Study (for Elective credit)

While research is not a required component of the degree, the desire to participate in research has been an increasing trend through recent years.

A common request n that has come up in the past few years is regarding the ability to conduct research (for credit), with faculty as independent study/directed reading/independent research.

[More on networking opportunities: Please also browse the information on relevant seminars, student groups and organizations near the bottom of this page.]

While there exists a way to earn credit for independent study/research (STATS299) under the supervision of their program adviser or other Statistics faculty. One must obtain approval from the advisor and provide clearly defined objectives and expected outcome(s) before enrolling in their section.

  1. Develop a goal statement for what the student hopes to accomplish and the purpose of the independent study. (List your goals by explaining what you hope to gain in terms of knowledge, skills, etc.)
  2. Select and/or develop learning objectives related to the goal statement. (Using broad statements, list each objective and/or learning activity in the plan.)
  3. Develop a timetable for implementation of activities and completion of course requirements. (Include what it is that you expect to do and produce and dates for completion and submission. List the types of activities/assignments that the you will be completing by the end of the quarter.)

Other (teaching/research) opportunities

Assistantships

Campus assistantships are not a guarantee and should not be relied upon to fund your tuition.

TA/RA opportunities within the Statistics dept are designated for the doctoral students as it is a predominant training component of their 5-year program. There is very little chance that either of these opportunities would be available to students outside of the Statistics doctoral program. If an opportunity becomes available, it will be announced to the Statistics graduate student population.

Statistics faculty do not manage the hiring of RA/TA, nor do they have funding to support Masters students.

An assistantship may sometimes be obtained from related departments and schools. It is the student's responsibility to find these opportunities and there are no guarantees. Begin an online search for Course Assistant applications at least three months before the start of the next quarter as departments need to start the hiring process well ahead of time.

!Do not commit to a TA/CA position if you do not have sufficient time to devote to the job.

Some departments or schools hire our M.S. students for hourly research assistant positions. This type of work is not to be confused with full or partial tuition allowance (GAP 7.3). Before accepting any work, confirm with the hiring department or school whether it is an hourly position, or if it is a type of tuition allowance.

Career prospects

At this time, the department does not publish job placement data of its graduates. Instead, we can provide a general trend of job placements in recent years:

Many students find employment in data science, research analytics, software engineering, program management within the technology sector (operations research), or the finance industry (asset management, acquisitions/mergers, business analytics) as well as various governmental services. The majority of our graduates have found employment in the Bay Area and other major cities around the world.

Stanford Career Education hosts career fairs throughout the year, and there is a tremendous benefit to our campus being situated in Silicon Valley. To participate, students upload their resumes in advance via Handshake, indicate which field/industry and companies they are interested in and industry partners reach out to schedule interviews.

Stanford Career Education also explains Where to Find Jobs & Internships !

We don't collect data on salaries. This information can be gathered in an online search of job recruitment and financial education sites.

Advanced Graduate Study

The number of students who pursue graduate programs is steadily increasing.

Statistics MS students that feel strongly about entering a 5-year program of research in statistical theory and applications should meet with their program advisor to discuss which programs and schools are an appropriate place and time to apply. With careful planning, students will be able to build a strong program that will make them highly competitive applicants wherever they apply.

Previous years' graduates had been accepted to doctoral programs in Statistics at Columbia, University of Washington, Wharton School, UC Berkeley and UCLA.

Common questions from incoming Statistics Masters students

Does Stanford require a tuition deposit to secure admission?

Stanford does not require a deposit to confirm your acceptance or initiate matriculation.

The student bill for autumn quarter is due in October.

When can I expect to receive my new I-20 or transfer my current SEVIS record to Stanford?

Student Visa Application in Axess: "Initiate I-20 or DS-2019; Request." You may do so immediately following accepting in Axess. The I-20 process will begin after submission of required documentation. Bechtel International Center will contact you if they require any further information.

How do I know which courses to enroll in when I start in autumn quarter?

Courses that you've taken at your previous institution (or applicable work experience) should be taken into account for the following scenarios:

Statistics students: Autumn Quarter

Probability Theory

  • For those with basic or intro statistics/probability, we recommend starting with STATS116 - Theory of Probability (autumn)
    • Students returning to school may wish to brush-up on their skills in statistics and probability and should also enroll in STATS116; Summary notes courtesy of Professor Dembo.
  • Students with background in Stochastic Processes (Markov chains, Martingale approach, Poisson processes, Gambler's ruin) should consider STATS 217 or STATS 219 winter quarter or probability theory (STATS 310A) autumn quarter for a more advanced theoretical course.
    • Students should be comfortable with probability at the level of STATS116/MATH151 (summary of material) and with real analysis at the level of Math115. Past exposure to stochastic processes is highly recommended.
    • A new course STATS221 focuses on topics in discrete probability that are well beyond undergraduate probability, with particular emphasis on random graphs and networks. While at a level and style similar to STATS217, the material of STATS221 is more modern, and do not overlap any of STATS 217/218/219 (nor with the STATS310 sequence or with MATH236).

Theoretical Statistics

  • Students with prior academic work (or work experience) consisting of advanced statistics and probability may wish to enroll in STATS200 Introduction to Statistical Inference, along with a linear algebra course or other higher-level course in Mathematics.
    • For those familiar with the material in this problem set then STATS200 is recommended (autumn). If the problem set poses a struggle, then we suggest starting with STATS116
    • Using the STATS200 course description to determine if the course content would be redundant material for you, STATS305A (autumn) is recommended instead.

Linear Algebra

  • Same recommendations for the linear algebra requirement:
  • or other higher-level course in Mathematics for those who have prior background in linear algebra:

Programming

  • For those with some programming experience (introduction to programming/intermediate programming), consider one of the following:
    • CS106A Programming Methodologies (A, W, S, Su)
    • CS106B Programming Abstractions (A, W, S, Su)
    • CS106AX Programming Methodologies in JavaScript and Python (Accelerated)
  • or other higher-level course in the same area for those who have programming experience beyond the courses described above.

Data Science students: autumn quarter

Where can I find the course schedule?

ExploreCourses, the university's academic database, can be searched using the program code (e.g., STATS116, CS106, MATH104, etc.) or by subject. Please pay special attention to the quarter(s) that courses will be offered, as not all courses are offered at all times, and some are not offered more than once per year. The course schedule is updated in August each year; ExploreCourses will redirect to the new database when it goes live.

When does autumn quarter registration start?

For Autumn 2024-25, students whose matriculation status is CLEAR will be able to enroll in courses early September(9:00 PM Pacific time).

  • International students (with an active SEVIS number) will need to clear the enrollment hold by attending the Maintaining Your Legal Status Sessions (2024 schedule offered once per day from August 29th to September 6th. Sessions are offered twice a day starting September 6th.
    • All F-1 and J-1 students are required to bring their passport, I-20 or DS-2019, and a recent print out/screen shot on digital device of your admissions I-94 electronic record to one of the Maintaining Your Legal Status workshops in order to have your enrollment hold removed. The hold will be removed during the workshop. Prior to attending this workshop, you must update your SEVIS (U.S.) address and U.S. phone number on Axess. 

 

Axess enrollment allows students to plan their quarter starting:

  • August 28 (Mon) Planning opens for undergraduate, graduate, and Graduate School of Business (GSB) students.

 

Stanford's course registration system allows students to enroll in courses with conflicting meeting patterns. While this is allowed at the start of the quarter (first three weeks), it is generally discouraged due to time constraints and expectations; the course should be dropped by the end of Week 3 (Final Study List deadline).

Instructors will not accommodate a student whose classes have conflicting end-quarter exams.

I'm an international student, how do I enroll for Autumn quarter?

Resources from Bechtel International Center

New International Students:

  • Release of Enrollment Holds: All F and J students are required to bring their passport, I-20 or DS-2019, and a recent print out/screen shot on digital device of your admissions I-94 electronic record to one of the Maintaining Your Legal Status workshops in order to have your enrollment hold removed. The hold will be removed within 24 hours.
  • Prior to attending this workshop, you must update your SEVIS (U.S.) address and U.S. phone number on Axess. Instructions on how to update your address can be found on the Bechtel website: How to update your address

F-1 Students Who Attended Other U.S. Schools:

  • All F-1 transfer students must complete the check-in process within 15 days of the program start date. This can only be done after you have updated your SEVIS (U.S.) address field and U.S. phone number in Axess and have attended one of the Maintaining Your Legal Status workshops at Bechtel.
  • After these two requirements have been met you will receive an e-mail instructing you to come to Bechtel to pick up your Transfer Completed I-20.
How easy is it to enroll in required courses each quarter?

Most students report that they were almost always able to enroll in the courses they needed each quarter. It is recommended that students make themselves available at the time that enrollment opens (9 pm Pacific).

If enrollment is closed and the course does not have a waiting list, students should contact the instructor to communicate their desire or need to take the course. Explain that the course is needed for your degree and confirm that you will not be enrolled in a course with a conflicting meeting pattern or final exam. Where possible the instructor will try to accommodate your request.

In some instances, be sure to carefully read the course description for enrollment steps. Some courses require the student to submit an application.

How many units do students typically enroll in each quarter?

Minimum units allowed during the regular academic year each quarter is 8 units which is considered full-time enrollment. Most students enroll in 8 units each quarter and many are able to enroll 10 units.

A few students are able to manage 11-15 units each quarter to finish their degree in less time.

If you need to enroll part-time (minimum 3 units), check your eligibility for Part-Time Enrollment in the Graduate Academic Policy and Procedures guide.

How many quarters should I expect it to take for me to finish my degree?

Most students take 5-6 quarters to finish their degrees, not including summer quarter. Some students can finish it in as few as 4 quarters, many choose to stay for 6 quarters (A,W,S) over two academic years.

Why do some students take longer to finish their degree programs than others?

Some students choose to take fewer required courses each quarter due to a more taxing course-load or due to outside commitments. They may also want to take other courses outside of the degree's requirements.

Can I do a Masters thesis (project)?

A thesis is not required for the Master's degree. Those who are interested in pursuing a thesis project, finding the right faculty is vital to starting any level of research. It takes considerable time and planning before permission is granted. Those who are successful then enroll in the Statistics STATS299 Independent Study course (up to 3 units) under the section number of their M.S. program advisor (or other faculty advisor).

As is stated in the admission offer letter, completing the M.S. degree in Statistics at Stanford is not a bridge to the Statistics Ph.D. at Stanford.

How accessible are faculty and instructors to speak with outside of class?

In addition to their faculty advisor, many students feel comfortable approaching and speaking with faculty and instructors. Bear in mind, Stanford faculty are often committed to various ongoing research projects; it can be difficult to connect or network with Stanford faculty and researchers without learning about what they do. We suggest attending any of the myriad seminars across campus that are of interest to you; which will open up an unparalleled domain of networking possibilities where you can learn about the diverse world of Stanford research.

Where do students typically live during their first year?

Most first-year students choose to live on campus in graduate housing. However, there are also many students who prefer to live off-campus in the surrounding Bay Area.

Graduate students are guaranteed campus housing their first year.

Graduate Housing Lottery

The Graduate Housing Lottery is the process by which new and continuing graduate students, as well as non-matriculated students such as post-docs, apply for 2022-23 and summer 2022 housing. Students will have the opportunity to rank their desired housing options and form groups. Housing is available for single students, couples, and families.

The campus housing application is available via Axess in April:

  1. Go to the Student drop-down menu and select Housing and Dining
  2. Select Apply for Housing
  3. Follow the instructions to submit your application.

R&DE Student Housing Assignments will be hosting a series of webinars covering the Graduate Housing Lottery:

Key Dates

  • April 5: Application portal opens.
  • May 3: Applications due for summer 2022 and 2022-23
  • May 27: Assignments announced for summer 2022 and 2022-23

 

Must-have items on your packing list

If these items aren't already in your suitcase, be sure to purchase them before the end of autumn quarter!

  • Reusable water bottles (at least 2)
  • Reusable thermos (for Statistics coffee and espresso to-go!)
  • An umbrella (or a big rain poncho to drape over yourself and your backpack)
  • A waterproof jacket
  • Comfortable walking shoes

 

If you plan to bring/purchase a bike (scooter/skateboard)

Bike Information and Resources for New Students

Bike Safety repair stations throughout Stanford's campus

What modes of transportation do students use to get around Stanford and the Bay Area?

As on most college campuses, Stanford students predominantly rely on a bike to get around. For those without access to a car, Caltrain, VTA or SamTrans provide more than adequately fulfill transportation needs up and down the peninsula (including airport shuttles). In addition, Stanford's free Marguerite shuttle service provides access to the campus to/from surrounding cities (Menlo Park, Palo Alto, parts of Redwood City) and to and from the Caltrain stations in Menlo Park and Palo Alto. Bay Area commute-traffic congestion rivals that of other major cities, which means driving on the peninsula to Stanford is impacted during peak hours.

Finding things to do after you relocate to Stanford

How can I get involved in Stanford's student organizations?

Academic Resources and Support

What academic resources does Stanford offer Masters students?

There are many resources available across Stanford. Masters students most often take advantage of the workshops and career fairs sponsored by BEAM and similar events offered by the School of Engineering's Xtend Career Forum for the Data Science program.

What software and computing resources are available to students?

The statistics courses taught by the Department typically require some knowledge of the programming language R. Many courses rely on Python coding.

Recommended resources:

Are there seminars open to students?

Yes: the Statistics Seminar is offered by the department, and the Probability Seminar is offered jointly with Stanford Math. Additionally, many other departments hold seminars that are open to students of all disciplines:

Stanford student groups that may be of interest are:

Does the department offer internship credit?

International students who are employed off-campus are subject to the policies outlined by Bechtel International Center concerning Curricular Practical Training.

In order to be eligible to be hired, international students (F-1) MUST file for CPT via BechtelConnect and enroll in the course STATS298 Industrial Research for Statisticians.

Please follow the Statistics department protocol for CPT before starting the application.

Getting to know Stanford

Notification/Obligation to Read Email

For many University communications, email to a student's Stanford email account is the official form of notification to the student, and emails sent by University officials to such email addresses will be presumed to have been received and read by the student. Emails and forms delivered through a SUNet account by a student to the University may likewise constitute formal communication, with the use of this password-protected account constituting the student's electronic signature. Read the entire policy pertaining to University Communication with Students.

Summer quarter distance-learning enrollment option (NDO student)

Master’s degree students who will matriculate autumn quarter have the option to take statistics courses online via the Stanford Center for Professional Development (SCPD) before arriving on campus. Registration and enrollment is administered through SCPD (NDO student status).

Matriculation will proceed as usual with autumn quarter start.

If you have any questions about course placement for summer quarter, please email Caroline Gates (cgates [at] stanford.edu (cgates[at]stanford[dot]edu)), your Student Services Officer in Statistics.

  • Enrollment in summer courses via SCPD does not require you to be on campus.
    • International student visas will be processed over summer with a start date in September.
  • CS dept policy: Students are obligated to enroll in the maximum unit for the CS course as a NDO student.

Summer tuition: 1/10th the full-time tuition cost + SCPD fees

Prior to Graduate Admissions matriculating your student record for autumn, Statistics and Data Science students may enroll in one or two courses online:

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