M.S. Concentration in Data Science

Program Overview

The Master's in Computer Science with a focus on Data Science at this university requires completion of 30 credits, including core courses in data science theory, systems, and data analysis. Elective courses cover various subfields within computer science, biostatistics, and other related disciplines. To complete the degree, students must also fulfill a statistics requirement. The program emphasizes hands-on learning and prepares students for careers in data science or related fields.

Program Outline

Outline:

• Program Structure:
• Students must complete 30 course credits.
• 18 credits can be from 500-level courses.
• 12 credits must be from 600-900 level courses (excluding independent studies).
• Maximum of 12 credits from independent studies.
• Maximum of 9 credits from courses outside the Computer Science Department.
• Maximum of 6 credits can be taken pass/fail.
• Courses with a grade below a C cannot be counted towards the MS degree.
• Limited transfer credits allowed.
• Overall GPA for the 30 credits must be 3.0 or higher.
• Course Requirements:
• Data Science Core Requirements:
• Four core requirements (one from each of three areas, plus one additional from any area).
• Courses must be completed with a grade of B or better.
• Theory for DS:
• COMPSCI 514 - Algorithms for Data Science
• COMSPCI 611 - Advanced Algorithms
• COMPSCI 651 - Optimization for Computer Science
• Systems for DS:
• COMPSCI 532 - Systems for Data Science
• COMSPCI 645 - Database Design and Implementation
• COMPSCI 677 - Distributed and Operating Systems
• Data Analysis:
• COMPSCI 585 - Natural Language Processing
• COMPSCI 589 - Machine Learning
• COMPSCI 590V - Data Visualization and Exploration
• COMPSCI 682 - Neural Networks: A Modern Introduction
• COMPSCI 683 - Artificial Intelligence
• COMPSCI 687 - Reinforcement Learning
• COMPSCI 689 - Machine learning: pattern classification
• COMPSCI 685 or 690N - Advanced Natural Language Processing
• COMPSCI 690V - Visual Analytics/Computer Vision
• Data Science Elective Requirements:
• Two elective requirements.
• Courses must be completed with a grade of B or better.
• Courses cross-listed as core and elective can only satisfy one area requirement.
• Pre-approved outside courses can count towards 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
• 515 - Algorithmic Fairness & Strategic Behavior
• 532 - Systems for DataSci
• 546 - Applied Infomation Retrieval
• 574/674 - Intelligent Visual Computing
• 585/685 - (Advanced) Natural Language Processing
• 589/689 - Machine Learning
• 590OP - Applied Numerical Optimization
• 571 - Data Visualization
• 611 - Algorithms
• 645 - Database Design and Implementation
• 650 - Applied Information Theory
• 670 - Computer Vision
• 677 - Distributed & Operating Systems
• 682 - Neural Networks: A Modern Intro.
• 683 - Artificial Intelligence
• 687 - Reinforcement Learning
• 690F - Responsible AI
• 690D - Deep Learning for NLP
• 651 - Optimization
• 690R - Computing: Human Movement Analysis
• 614 - Randomized Algorithms and Probabilistic Data Analysis
• 690S - Human-Centric Machine Learning
• 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:
• One statistics requirement.
• Courses must be completed with a grade of B or better.
• Pre-approved outside courses can count towards the CompSci MS core/course requirements.
• COMPSCI:
• 550 - Introduction to Simulation
• 688 - Graphical Models
• DACSS:
• 603 - Introduction to Quantitative Analysis
• 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
• 750 - Applied Statistical Learning
• ECE:
• 603 - Probability and Random Processes
• SCH-MGMT:
• 650 - Statistics for Business