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M.S. Concentration in Data Science

MS students wishing to add the Data Science Concentration to their MS degree are asked to submit the pre-application and are required to:

DATA SCIENCE COURSE REQUIREMENTS

  1. Core requirements. You must have satisfied four Data Science core requirements (one from each of three areas, plus one additional requirement from any of the three areas). This requirement is usually satisfied by taking courses and getting a B or better in them.
  2. Elective Requirements. You must have satisfied two Data Science elective requirements 
  3. Statistics Requirement. You must have satisfied one Data Science statistic requirement

  4. Credits. You must take a total of 30 credits with the following restrictions:

    • No more than 18 of the course credits may come from courses at the 500 level. 500-level classes taken to satisfy core requirements fall into this group.
    • At least 12 of those credits must come from courses at the 600-900 level that are not independent studies. 600-level classes taken to satisfy core requirements fall into this group.
    • No more than 12 credits may come from independent studies 
    • No more than 9 credits may come from courses outside of the Computer Science Department. (Credit for graduate courses from other departments must be approved by the GPD.)
    • No more than 6 credits may be taken pass/fail
    • Classes with a grade below a C may not be counted toward the MS degree.
    • Only a limited number of credits may be transferred from other programs or institutions.
  5. GPA. Your overall grade point average for those 30 credits must be 3.0 or higher.

DATA SCIENCE CORE REQUIREMENTS

All DataSci core courses can be used toward the CompSci MS core requirements. 

DATA SCIENCE THEORY CORES

The following course can be used to complete the Theory for DS core requirement:

  • Algorithms for Data Science (COMPSCI 514)
  • Advanced Algorithms (COMSPCI 611)
DATA SYSTEMS CORES

The following course can be used to complete the Systems for DS core requirement:

  • Systems for Data Science (COMPSCI 532)
  • Database Design and Implementation (COMSPCI 645)
  • Distributed and Operating Systems (COMPSCI 677)
DATA SCIENCE AI CORES

The following courses can be used to complete the Data Analysis core requirement:

  • Natural Language Processing (COMPSCI 585)
  • Machine Learning (COMPSCI 589)
  • Data Visualization and Exploration (COMPSCI 590V)
  • Neural Networks: A Modern Introduction (COMPSCI 682)
  • Artificial Intelligence (COMPSCI 683)
  • Reinforcement Learning (COMPSCI 687)
  • Machine learning: pattern classification (COMPSCI 689)
  • Advanced Natural Language Processing (COMPSCI 685 or 690N)
  • Visual Analytics/Computer Vison (COMPSCI 690V)

DATA SCIENCE ELECTIVE REQUIREMENTS

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

DATA SCIENCE STATISTICS REQUIREMENTS

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

 

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