Graduate: MA Program

30 Credit Hours Required

• Core Course Requirement (12 credits)
  • MAT 653 Mathematical Data Science
  • MAT 695 Mathematical Analysis
  • MAT 727 Linear Algebra
  • STA 622 Complex Data Analysis
• Electives (12 – 15 credits)
  • Choose electives tailored to your field.
  • Many exciting Mathematical Data Science electives and interdisciplinary data science electives offered.
• Capstone Experience (6 or 3 credits)
  • Thesis Option: MAT 699 Thesis (6 credits)
  • Project Option: MAT 687 Project in Mathematics (3 credits)

Year 1 – Breadth and Foundational Courses

Fall 1:

• MAT 727 Linear Algebra
• MAT 695 Mathematical Analysis
• One Elective

Spring 1:

• MAT 653 Foundations of Mathematical Data Science
• Two Electives

Year 2 – Specialization and Project

Fall 2:

• STA 622 Complex Data Analysis
• Elective

Spring 2:

• Elective
• MAT 699 Thesis ( 6 hrs )
  • or MAT 687 Project in Mathematics ( 3 hrs )

Year 1 – Breadth and Foundational Courses

Fall 1:

• MAT 727 Linear Algebra
• MAT 965 Mathematical Analysis
• One Elective

Spring 1:

• MAT 653 Foundations of Mathematical Data Science
• Two Electives

Summer 1: Elective or project

• MAT 699 Thesis ( 6 hrs )
  • or MAT 687 Project in Mathematics ( 3 hrs )

Year 2 – Specialize and Project

Fall 2:

• STA 622 Complex Data Analysis
• Two Electives

Mathematical Data Science Electives

• MAT 628 Linear Programming and Optimization
• MAT 632 Introduction to Graph Theory
• MAT 751 Topological Data Analysis
• MAT 749 The Mathematics of Machine Learning
• STA 642/IAA 621 Statistical Computing
• STA 670/ IAA 623 Categorical Data Analysis
• STA 703 Topics in High Dimensional Data Analysis (prereqs)
• Upcoming Bayesian statistics course
• CSC 605 Data Science
• CSC 610/IAC 622 Big Data and Machine Learning
• CSC 611 Advanced Data Science
• CSC 622 Advanced Digital Image Processing
• CSC 625 Bioinformatics
• CSC 654/IAC 620 Algorithm Analysis and Design

Applied Data Science Electives

• ECO  – Economics
  • ECO 625 Data Methods in Economics
  • ECO 663 Predictive Data Mining
  • ECO 664 Time Series and Forecasting
• ERM – Educational Research Methodology
  • ERM 675 Data Visualization and Presentation
• IAA – Advanced Data Analytics
  • IAA 621 Statistical Computing
  • IAA 622 Complex Data Analysis
  • IAA 623 Categorical Data Analysis
• IAB – Bioinformatics
  • IAB 620 Introduction to Bioinformatics
  • IAB 621 Bioinformatics
  • IAB 622 Advanced Bioinformatics
• IAC – Computational Analytics
  • IAC 620 Algorithm Analysis and Design
  • IAC 621 Data Science
  • IAC 622 Big Data and Machine Learning
• IAF – Information and Analytics
  • IAF 601 Introduction to Data Analytics-Methods and Approaches
  • IAF 602 Statistical Methods for Data Analytics
  • IAF 603 Preparing Data for Analytics
  • IAF 604 Machine Learning and Predictive Analytics
  • IAF 605 Data Visualization
  • IAF 606 Solving Problems with Data Analytics
• IAG – Geospatial Analytics
  • IAG 620 Understanding Geographic Information Systems
  • IAG 624 Advanced Remote Sensing-Imaging
  • IAG 625 Spatial Analysis
• IAL – Cultural Analytics
  • IAL 620 Text Mining and Natural Language Processing
  • IAL 621 Content Analysis for Social Network Data
• ISM – Information Systems and Operations Management
  • ISM 645 Principles of Predictive Analytics
  • ISM 646 Visualizing Data to Design Strategy
  • ISM 647 Cognitive Computing and Artificial Intelligence Applications for Business
  • ISM 671 Organizing Data for Analytics