Download Program Proposal for a Master`s Degree

Program Proposal for a Master’s Degree | Statistics - Data Science | 2015-2016 | Stanford University
Name:
12/15
Student ID Number:
Email:
Phone:
Proposed Degree Conferral (Qtr/Yr):
For complete description of program requirements visit: http://exploredegrees.stanford.edu/archive/201516/schoolofhumanitiesandsciences/statistics/#masterstext
Courses must be taken for letter grade, if offered.
Program Proposal:
Quarter
& Year
Requirement 1:
Dept & Course
Number
Course Title
Grade
1.
2.
3.
4.
Requirement 2:
1.
2.
3.
4.
Requirement 3:
1.
2.
3.
4.
Requirement 4:
1.
2.
3.
4.
Requirement 5:
Total Units:
Signatures:
Student:
Date:
Faculty Adviser (required):
Date:
Student Services Verification/PS Entry:
Date:
Units
Bring the approved Program Proposal form to the Student Services Officer (Sequoia Hall, Room 127)
Curriculum Requirements
The total number of units in the degree is 45 (36 of which must be taken for a letter grade).
Requirement 1: Mathematical Core
12 units
Requirement 2: Advanced Scientific
Programming and High Performance
Req. 2 continued
Computing Core
Parallel Programming
3 units
3 units
CME 302 - Numerical Linear Analysis (3)
CME 212 – Advanced Programming for Scientists
and Engineers (3)
CME 304 - Numerical Optimization (3)
CME 214 - Software Design In Modern Fortran
for Scientists and Engineers (3)
or
CME 364A Convex Optimization I (3)
CS 107 - Computer Organization and Systems
(3-5)
CS 149 - Parallel Computing (3-4)
CME 305 - Discrete Math and Algorithms (3)
CS 249B - Large-scale Software Development (3)
CS 315A - Parallel Computer Architecture
and Programming (3)
CME 308 - Stochastic Methods in Engineering
(3)
Requirement 3: Statistical Core
CME 213 – Introduction to parallel
computing using MPI, openMP and CUDA
(3)
CME 342 – Parallel Methods in Numerical
Analysis (3)
CS 316 – Advanced Multi-Core Systems (3)
Requirement 4: Domain Specialization or preparatory courses
12 units
9 units
STATS 200 - Intro to Stat Inference (3)
BIOE 214 - Rep. and Algorithms for
Computational Molecular Biology (3-4)
CS 347 - Parallel and Distr. Data Mining (3)
STATS 203 - Intro to Regression Models &
Analysis of Variance (3)
BIOMEDIN 215 - Data Driven Medicine (3)
CS 448 - Topics in Comp. Graphics (3-4)
or
STATS 305 - Intro to Statistical Modeling (3)
BIOS 221/STATS 366 - Modern Stats for Modern
Biology (3) (summer quarter)
ENERGY 240 - Geostatistics (2-3)
STATS 315A - Modern Applied Statistics:
Learning (2-3)
CS 224W - Social and Information Network
Analysis (3-4)
OIT 367 – Business Intelligence for Big Data
(4)
STATS 315B – Modern Applied Statistics: Data
Mining (2–3)
CS 229 – Machine Learning (3-4)
PSYCH 204A - Human Neuroimaging Meth.
(3)
CS 246 – Mining Massive Data Sets (3-4)
PSYCH 303 - Human and Machine Hearing
(3)
STATS 290 - Paradigms for Computing with
Data (3)
Requirement 5: Practical Component



6 units
Capstone project supervised by a faculty member and approved by the steering committee. The capstone project should be
computational in nature. Students should submit a one‐page proposal, supported by the faculty member, to the steering
committee ([email protected]) for approval at least one quarter before.
Clinics, such as the Stanford Data Challenge Lab (ENGR 250); Data Impact Lab (ENGR 350).
Workshop Course: Other courses that have a strong hands-on and practical component, such as STATS 390 Consulting
Workshop (1-3 units).
https://statistics.stanford.edu/academics/ms-statistics-data-science