斯坦福大学 Data Science-新东方前途出国

M.S. in Statistics: Data Science

The increasing importance of big data in engineering and the applied sciences motivates the Department of Statistics and ICME (Institute for Computational and Mathematical Engineering) to collaboratively offer a M.S. track that trains students in data science with a computational focus.

This focused M.S. track is developed within the structure of the current M.S. in Statistics and the M.S. program in ICME.

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.

Coursework

The Data Science track develops strong mathematical, statistical, and computational and programming skills through the general master's core and programming requirements, in addition to providing fundamental data science education through general and focused electives requirement from courses in data sciences and related areas.

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.

Submission of approved Master's Program Proposal, signed by the master's adviser, to the student services specialist by the end of the first quarter of the master's degree program. A revised program proposal is required to be filed whenever there are changes to a student's previously approved program proposal.

Students must demonstrate breadth of knowledge in the field by completing five core areas.

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.

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.

Requirement 3 : Specialized Electives (9 units)

Choose three courses in specialized areas from the following list. Courses outside this list are subject to approval.

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.

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.

 Data Science Sample Schedules

The Data Science track schedule typically spans 5 quarters.

5 quarter schedule for most students:
Year 1:
Aut: CME 200, CME211, STATS200
Wtr: CME212, CME364A, STATS200 or 203
Spr: STATS315B, CME308, elective
Year 2:
Aut: CME302, STATS305A, HPC course (or take CME213 in spring), practical
Wtr: CME305, STATS315A, practical, elective

5 quarter schedule for students who are well prepared:
Must have taken the equivalent of CME200 and STATS200 prior to starting the program.
Year 1:
Aut:  CME211, STATS305A, elective
Wtr: CME212, CME364A, STATS203
Spr: CME213, STATS315B, CME308
Year 2:
Aut: CME302, practical, elective
Wtr: CME305, STATS263, STATS315A, elective

4 quarter schedule:
This schedule is very demanding and students typically prefer the experience gained with a 5 quarter schedule.

Student must have taken the equivalent of CME200 and STATS200 before starting the program.

Year 1:
Aut: CME211, STATS305A, elective
Wtr: CME212, CME305, CME364A, STATS315A
Spr: CME213, STATS315B, CME308, practical
Year2:
Aut: CME302, elective (2), practical
 

Notes:

1.    Because CME211 is the pre-requisite to CME212, those who take CME211 will can count it as an elective.

2.    CME302 requires the equivalent of CME200 as prerequisite.

3.    STATS305A requires the equivalent of STATS200 as prerequisite.

4.    STATS315A requires the equivalent of STATS200 and (STATS203 or 305A) as prerequisite.