This focused M.S. track is developed within the structure of the current M.S. in Statistics and the M.S. program in ICME. Students in the program will develop strong mathematical, statistical, computational and programming skills through the M.S. requirements, and they will gain a fundamental data science education by focusing 18 units of elective courses in the area of data science and related courses. Upon the successful completion of the Data Science M.S. degree students will be prepared to continue on to their Ph.D. in Statistics, ICME, MS&E, or Computer Science or as a data science professional in industry. Completing the M.S. degree gives no guarantee or preference for admission to the Ph.D. program.
The track will attract both engineering or science students interested in better understanding the mathematical and statistical underpinnings of data science, as well as mathematically oriented students who are looking to gain expertise in data science and applications.
The M.S. in Data Science track is overseen by a steering committee comprised of ICME and Statistics faculty members.
Application and Selection Process
For those who want to pursue the Data Science subplan, you must apply to the Statistics Master's degree program and then declare your preference by selecting the subplan in the "Department Specialization" option.
Applicants who select Data Science will only be considered for the Data Science program, not in addition to the Statistics M.S. degree program.
Admission to the M.S. program is made by the Statistics admissions committee, which will have representation from the Data Science track steering committee.
It is the applicant's responsibility to ensure he or she meets all eligibility requirements before applying.
Students should have successfully taken an equivalent of Linear Algebra and one other statistics courses to be eligible.
The coursework follows the requirements of the traditional ICME M.S. degree with additional restrictions placed on the general and focused electives. As defined in the general Graduate Student Requirements, students have to maintain a grade point average (GPA) of 3.0 or better and classes must be taken at the 200 level or higher. Students satisfying the course requirement of the Data Science track do not have to satisfy the other course requirements for the M.S. in Statistics
The total number of units in the degree is 45, 36 of which must be taken for a letter grade.
Requirement 1 : Foundational (12 units)
Students must demonstrate foundational knowledge in the field by completing the following core courses. Courses in this area must be taken for letter grades.
- CME 302 - Numerical Linear Algebra - 3
- CME 305 - Discrete Mathematics and Algorithms - 3
- CME 307 - Optimization - 3
- CME 308 - Stochastic Methods in Engineering - 3
- CME 309 - Randomized Algorithms and Probabilistic Analysis
Requirement 2 : Data Science Electives (12 units)
Data Science electives should demonstrate breadth of knowledge in the technical area. The elective course list is defined. Courses outside this list are subject to approval. Courses in this area must be taken for letter grades.
- STATS 200 - Introduction to Statistical Inference - 3
- STATS 203 - Introduction to Regression Models and Analysis of Variance - 3
- or STATS 305 - A Introduction to Statistical Modeling
- STATS 315A - Modern Applied Statistics: Learning - 2-3
- STATS 315B - Modern Applied Statistics: Data Mining - 2-3
- or equivalent courses as approved by the adviser.
Requirement 3 : Specialized Electives (9 units)
Choose three courses in specialized areas from the following list. Courses outside this list are subject to approval.
- BIOE 214 - Representations and Algorithms for Computational Molecular Biology - 3-4
- BIOMEDIN 215 - Data Driven Medicine - 3
- BIOS 221/STATS 366 - Modern Statistics for Modern Biology - 3
- CS 224W - Social and Information Network Analysis - 3-4
- CS 229 - Machine Learning - 3-4
- CS 246 - Mining Massive Data Sets - 3-4
- CS 347 - Parallel and Distributed Data Management - 3
- CS 448 - Topics in Computer Graphics - 3-4
- ENERGY 240 - Geostatistics - 2-3
- OIT 367 - Business Intelligence from Big Data - 3
- PSYCH 204 - A Human Neuroimaging Methods - 3
- STATS 290 - Paradigms for Computing with Data - 3
Requirement 4 : Advanced Scientific Programming and High Performance Computing Core (6 units)
To ensure that students have a strong foundation in programming, 3 units of advanced scientific programming for letter grade at the level of CME212 and three units of parallel computing for letter grades are required.
Note: Programming proficiency at the level of CME211 is a hard prerequisite for CME212 (students may ONLY place out of 211 with prior written approval). CME211 can be applied towards elective requirement.
Advanced Scientific Programming; take 3 units
- CME 212 - Advanced Software Development for Scientists and Engineers - 3
- Parallel Computing/HCP courses: (3 units)
- CME 213 - Introduction to parallel computing using MPI, openMP, and CUDA - 3
- CME 323 - Distributed Algorithms and Optimization - 3
- CME 342 - Parallel Methods in Numerical Analysis - 3
- CS 149 - Parallel Computing - 3-4
- CS 315A - Parallel Computer Architecture and Programming - 3
- CS 316 - Advanced Multi-Core Systems - 3
- CS 344C, offered in previous years, may also be counted
Students who do not start the program with a strong computational and/or programming background will take an extra 3 units to prepare themselves by, for example, taking CME211 Programming in C/C++ for Scientists and Engineers or an equivalent course, such as CS106A/B/X.
Requirement 5 : Practical Component
Students are required to take 6 units of practical component that may include any combination of:
- A capstone project, supervised by a faculty member and approved by the student's adviser. The capstone project should be computational in nature. Students should submit a one-page proposal, supported by the faculty member and sent to the student's Data Science adviser for approval (at least one quarter prior to start of project).
- Master's Research: STATS 299 Independent Study.
- Project labs offered by Stanford Data Lab: ENGR 250 Data Challenge Lab, and ENGR 350 Data Impact Lab.
- Other courses that have a strong hands-on and practical component, such as STATS 390 Consulting Workshop up to 1unit.
The Management Science and Engineering Department offers a limited number of fellowships restricted to first-year MS&E doctoral students, with priority given to those new to Stanford. These fellowships are not available to students admitted to the MS&E Master of Science program. It is not necessary to apply for these fellowships. All doctoral applicants indicating a desire to be considered for financial aid are considered for the fellowships.
The Vice Provost for Graduate Education (VPGE) administers a limited number of fellowships for graduate students. Please see the VPGE websitefor more information.
Please see GAP 7.2 for Stanford's policy on fellowships and other stipend support.
Please see Tax Information for information on tax treaties and taxation of fellowships.
We encourage students to apply for outside fellowships. Here is a listing of some you might consider.
- Hertz Foundation
- International Institute for Applied Systems Analysis (IIASA)
- Link Foundation Energy Fellowship
- NDSEG Graduate Fellowships
- NPSC Graduate Fellowships
- NSF Graduate Research Fellowship Program
- Soros Fellowship for New Americans
Course and Research Assistantships
The Management Science and Engineering Department offers a limited number of course and research assistantships. Priority for assistantships goes to continuing MS&E doctoral students. New students and students enrolled in other departments are unlikely to receive MS&E assistantships, and should seek other funding opportunities.
Current students may apply for course assistantships online at the Student Forms page.
The Center for Teaching and Learning offers services for instructors and TA/CAs.
International students hired as TA/CAs must be screened by English for Foreign Students (EFS) for readiness to use English in a teaching role. Please see the EFS website for detailed information and requirements.
Current students may contact faculty members directly regarding the possibility of research assistantships.
We seldom hire hourly graders for our courses, preferring to support students on course assistantships whenever possible. If we do hire a grader, it will be after the first week of the quarter, and an email will be sent to students if we need applications.
Students appointed as CAs, RAs or graders must have a Social Security number and completed I-9 paperwork. Please see Payroll for Employeesand Tax Information for additional information.
Please see GAP 7.3 for Stanford policy on assistantships.
Graduate Student Loans
Permanent residents and U.S. citizens may apply for need-based loans. Information on student loans and financial aid requirements and deadlines are available at the Stanford University Financial Aid Office.
Applications for the 2017-18 academic year will open September 30, 2016.
PhD: December 6, 2016 (Tuesday)
MS: January 10, 2017 (Tuesday)
Welcome to Graduate Admissions, Office of the University Registrar, Stanford University. The tabs above contain detailed instructions and information on applying to Stanford's graduate programs; review them carefully.
Decisions regarding admission are made by the admitting departments, however all offers of admission are subject to verification of university admission requirements such as degree conferral, academic transcripts, official test scores and English proficiency, by Graduate Admissions, Office of the University Registrar.
CS 106A (Programming Methodology), CS 106B (Programming Abstractions), CS 106X (Programming Methodology and Abstractions), CS 107 (Computer Organization Systems), CS 140 - 181, or other course with the faculty adviser's approval. Students who have these skills may elect a more advanced CS course. Must be taken for a letter grade.
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