Data Science

Degrees Offered

  • Master of Science in Data Science (Non-Thesis)

Program Description

This non-thesis Masters program is designed to give candidates a foundation in statistics and computer science and also provide knowledge in a particular application domain of science or engineering. The balance between these three elements is a strength of the program and can prepare candidates for Data Science careers in industry, government, or for further study at the Ph.D. level. Throughout is an emphasis on working in teams, creative problem solving, and professional development.

Professors

Douglas Nychka, Applied Mathematics & Statistics, Professor

Paul Sava, Geophysics, Professor

Associate professors

Dorit Hammerling, Applied Mathematics & Statistics, Associate Professor

Michael Wakin, Electrical Engineering, Associate Professor

Hua Wang, Computer Science, Associate Professor

Teaching Associate Professor

Wendy Fisher, Computer Science, Teaching Associate Professor

Program Requirements

The field of Data Science draws on elements of computer science, statistics and interdisciplinary applications to address the unique needs of gaining knowledge and insight through data analysis. This non-thesis masters program is designed to give candidates a foundation in statistics and computer science and also provide knowledge in a particular application domain of science or engineering. The balance between these three elements is a strength of the program and can prepare candidates for Data Science careers in industry, government, or for further study at the PhD level. Moreover, the coursework will be flexible and tailored to each candidate. For example, the program will allow a candidate to increase his/her skills in data analytics while developing a focused area of application or alternatively allow a candidate with depth in an area of application to gain skills in statistics and computer science.

This program will follow a 3 X 3 + 1 design: three modules and a mini-module.

Modules (Each consisting of three 3 credit courses)

  • Data Modeling and Statistical Learning
    • MATH530: Statistical Methods
    • MATH560: Statistical Learning I
    • MATH561: Statistical Learning II
  • Machine Learning, Data Processing and Algorithms, and Parallel Computation
    • CSCI303: Introduction to Data Science
    • CSCI470: Introduction to Machine Learning
    • CSCI575: Machine Learning  OR  CSCI563: Parallel Computing for Scientists and Engineers
  • Individualized and Domain Specific Coursework
    • Example Modules:

      Electrical Engineering: EENG411 (Digital Signal Processing); EENG509 (Sparse Signal Processing); EENG511 (Convex Optimization and its Engineering Applications); EENG515 (Mathematical Methods for Signals and Systems); and/or EEGN519 (Estimation Theory and Kalman Filtering)

      Geophysics: GPGN533 (Geophysical Data Integration & Geostatistics); GPGN570 (Applications of Satellite Remote Sensing); and/or GPGN605 (Inversion Theory)

Mini-module (Comprising three 1 credit courses)

  • Professional Development
    • SYGN502:   Introduction to Research Ethics
    • SYGN5XX:  Leadership and Teamwork
    • LICM501:    Professional Oral Communication
First Year
Fallleclabsem.hrs
CSCI303INTRODUCTION TO DATA SCIENCE  3.0
CSCI470INTRODUCTION TO MACHINE LEARNING   3.0
MATH530STATISTICAL METHODS I  3.0
ELECT Elective Approved by Program  3.0
12.0
Springleclabsem.hrs
CSCI575MACHINE LEARNING  3.0
MATH560INTRODUCTION TO KEY STATISTICAL LEARNING METHODS I  3.0
ELECT Elective Approved by Program  3.0
LICM501PROFESSIONAL ORAL COMMUNICATION  1.0
10.0
Second Year
Fallleclabsem.hrs
MATH561INTRODUCTION TO KEY STATISTICAL LEARNING METHODS II  3.0
ELECT Elective Approved by Program  3.0
SYGN502INTRODUCTION TO RESEARCH ETHICS  1.0
SYGN5XX LEADERSHIP AND TEAMWORK  1.0
8.0
Total Semester Hrs: 30.0

**Electives for the third module can be designed by the student but the plan needs to be approved by the program curriculum committee.  Although this individualized module can draw on graduate courses from across the university, two specific examples from engineering and geophysics are given below:

Electrical Engineering Module: EENG411 (Digital Signal Processing); EENG509 (Sparse Signal Processing); EENG511 (Convex Optimization and its Engineering Applications); EENG515 (Mathematical Methods for Signals and Systems); and/or EEGN519 (Estimation Theory and Kalman Filtering)

Geophysics Module: GPGN533 (Geophysical Data Integration & Geostatistics); GPGN570 (Applications of Satellite Remote Sensing); and/or GPGN605 (Inversion Theory)