MS students wishing to add the Data Science Concentration to their MS degree are asked to submit the pre-application and are required to:
Credits. You must take a total of 30 credits with the following restrictions:
All DataSci core courses can be used toward the CompSci MS core requirements.
DATA SCIENCE THEORY CORESThe following course can be used to complete the Theory for DS core requirement:
The following course can be used to complete the Systems for DS core requirement:
The following courses can be used to complete the Data Analysis core requirement:
Students must complete two of the following courses with a grade of B or better. Courses that are crossed-listed as core and elective may only satisfy one area requirement. Outside courses on this list are preapproved and can count toward the CompSci MS core/course requirements
COMPSCI |
501- Formal Language Theory; 520/620- Advanced Software Engineering: synthesis and development; 521/621- Advanced Software Engineering: analysis and evaluation; 514- Algorithms for DataSci; 532- Systems for DataSci; 546 - Applied Infomation Retrieval; 574/674/590IV/690IV - Intelligent Visual Computing; 585/685/690N- (Advanced) Natural Language Processing; 589/689- Machine Learning; 590OP- Applied Numerical Optimization; 590T- Algorithmic Fairness & Strategic Behavior; 590V/690V/670- (Advanced)Visual Analytics/Computer Vision; 611- Algorithms; 645- Database Design and Implementation; 646- Information Retrieval; 650- Applied Information Theory; 677- Distributed & Operating Systems; 682- Neural Networks: A Modern Intro.; 683- Artificial Intelligence; 687 - Reinforcement Learning; 690F- Responsible AI; 690D- Deep Learning for NLP; 690OP/651 - Optimization; 690RA - Randomized Algorithms and Probabilistic Data Analysis; 691DD- Research Methods in Empirical Computer Science; 691O - Tools for Explanatory & Tutoring Systems; 692R - Machine Learning in the Real World; 745- Advanced Systems for Big Data Analytics |
BIOSTATS |
690JQ Modern Applied Statistics Methods; 650 Applied Regression Modeling; 683 - Introduction to Causal Inference; 690B Introduction to Causal Inference in a Big Data World; 690T Statistical Genetics; 730 Applied Bayesian Statistical Modeling; 740 Analysis of Longitudinal Data; 743 Analysis of Categorical Data in Public Health; 748 Applied Survival Analysis; 749 Statistical Methods in Clinical trials |
ECE |
565-Digital Signal Processing; 597MS-Math Tools for Data Science; 608- Signal Theory; 697CS- Intro to Compressive Sensing; 746- Statistical Signal Processing |
MIE |
620- Linear Programming; 684- Stochastic Processes in Industrial Engineering I; 724- Non-Linear and Dynamic Programming |
SCH-MGMT | 602-Business Intelligence and Analytics |
STAT | 697BD- Biomed And Health Data Analysis |
Students must complete one of the following courses with a grade of B or better. Outside courses on this list are preapproved and can count toward the CompSci MS core/course requirements
COMPSCI |
550 - Introduction to Simulation; 688- Graphical Models |
STAT | 501- Methods of Applied Statistics; 525- Regression Anaylsis; 526- Design of Experiments; 535 -Statistical Computing; 597S- Intro to Probability and Math Statistics; 605- Probability Theory; 607-Mathematical Statistics I; 608- Mathematical Statistics II; 625- Regression Modeling |
MATH | 605 - Probability Theory |
BIOSTATS |
650 - Applied Regression Modeling; 690B Introduction to Causal Inference in a Big Data World; 730 - Applied Bayesian Statistical Modelling |
ECE | 603 - Probability and Random Processes |
SCH-MGMT | 650 - Statistics for Business |