Degree Requirements

Students in the Master of Science in Data Science (MSDS) program must successfully complete 30 credits based on any of the following options:

  • Courses (30 credits)
  • Courses (27 credits) + MS Project (3 credits)
  • Courses (24 credits) + MS Thesis (6 credits)

Independent of the chosen option, all core courses in the respective tracks are required.

At most two courses can be chosen from outside the respective track with approval of the respective Program Co-Directors. Computational track students are allowed at most three electives that are non-Computer Science courses. Statistics track students are allowed at most three electives that are non-Math courses. 


If a student chooses the MS project or MS thesis option, the project or thesis must be related to data science and requires approval from one of the Program Co-Directors.

The MSDS program has computational and statistics tracks that students must choose from at admission time. These tracks have different core courses but share the same admission requirements and electives.

Students may choose an elective outside the list after approval of their respective advisor.

M.S. in Data Science

Core Course Requirements for Computational Track
CS 675Machine Learning3
CS 644Introduction to Big Data3
CS 636Data Analytics with R Program3
CS 677Deep Learning (Deep Learning)3
MATH 661Applied Statistics3
Core Course Requirements for Statistics Track
MATH 660Introduction to statistical Computing with SAS and R3
MATH 661Applied Statistics3
MATH 678Stat Methods in Data Science3
CS 644Introduction to Big Data3
CS 675Machine Learning3
or MATH 680 Advanced Statistical Learning
Electives and Foundation Courses15
Computer Science Electives
CS 610Data Structures and Algorithms3
CS 631Data Management System Design3
CS 632Advanced Database System Design3
CS 634Data Mining3
CS 636Data Analytics with R Program (only available to students in the Math core)3
CS 639Elec. Medical Records: Med Terminologies and Comp. Imp.3
CS 643Cloud Computing3
CS 645Security and Privacy in Computer Systems3
CS 656Internet and Higher-Layer Protocols3
CS 659Image Processing and Analysis3
CS 661Systems Simulation3
CS 670Artificial Intelligence3
CS 676Cognitive Computing3
CS 677Deep Learning (Deep Learning(available only to students in statistics track))3
CS 683Software Project Management3
CS 684Software Testing and Quality Assurance3
CS 681Computer Vision3
CS 708Advanced Data Security and Privacy3
CS 731Applications of Database Systems3
CS 732Advanced Machine Learning3
CS 735High Performance Analytics Dat3
CS 744Data Mining and Management in Bioinformatics3
CS 782Pattern Recognition and Applications3
Math Electives
MATH 630Linear Algebra and Applications3
MATH 631Linear Algebra3
MATH 644Regression Analysis Methods3
MATH 660Introduction to statistical Computing with SAS and R (only available to students in computational track)3
MATH 662Probability Distributions (only available to students in computational track)3
MATH 664Methods for Statistical Consulting3
MATH 665Statistical Inference (only available to students in computational track)3
MATH 678Stat Methods in Data Science3
CS 680Linux Kernel Programming3
CS 683Software Project Management3
MATH 699Design and Analysis of Experiments3
MATH 717Inverse Problems and Global Optimization3
MATH 786Large Sample Theory and Inference3
MATH 787Non-Parametric Statistics3
Other Electives
BIOL 638Computational Ecology3
BME 698Selected Topics3
MGMT 635Data Mining and Analysis3
MGMT 630Decision Analysis3
FIN 600Corporate Finance I3
FIN 641Derivatives Markets3
FIN 642Derivatives and Structured Finance3
MRKT 613 (Market Planning and Analysis)
MRKT 630Models of Consumer Behavior3
IS 631Enterprise Database Management3
IS 665Data Analytics for Info System3
IS 687Transaction Mining and Fraud Detection3
IS 688Web Mining3
BNFO 601Foundations of Bioinformatics I3
BNFO 602Foundations of Bioinformatics II3
BNFO 615Data Analysis in Bioinformatics3
BNFO 620Genomic Data Analysis3
Total Credits30

Recommended course sequence M.S. in Data Science for Computational Track

  Fall Spring
Year 1 CS 675 Machine Learning CS 631 Data Management and System Design
MATH 661 Applied Statistics CS 644 Big Data
CS 636 R for Data Science CS 677 Deep Learning
Year 2 Free elective or Master thesis course Free elective or Masters thesis course
Free elective or Master project course
Free elective

Recommended course sequence for M.S. in Data Science for Statistics Track

  Fall Spring
Year 1 MATH 660 Intro to Statistical Computing with R and SAS MATH 678 Statistical Methods in Data Science
MATH 661 Applied Statistics CS 644 Big Data
Free Elective MATH 630 Linear Algebra and Applications
Year 2 CS 675 Machine Learning or MATH 680 Advanced Statistical Learning Free elective or Masters thesis course
Free elective or Master thesis for thesis
Free elective or Master project course