Concentration in Mathematical Foundations of Data Science

The MA program is being discontinued pending SACSCOC approval and is not accepting applications for admission. The University is still authorized to offer the program and issue the associated credential for students who are currently enrolled in the program. The department still offers an MS in Applied Statistics.

Highlights

• Get an edge in your career.
• Learn the math behind cutting-edge data science methods.
• Develop skills for:
  • Technology and Business Intelligence
  • Banking and Finance
  • Health Care and Medical Industry
  • Government Agencies
• Choose electives for your career from:
  • Mathematics
  • Statistics
  • Computer Science
  • Economics
  • Informatics and Analytics
  • Cultural Analytics
  • Geographic Information Systems
  • Bioinformatics
  • Information Systems and Supply Chain Management
• 1.5 year and 2 year programs offered

Students sought

• Those with a Bachelor’s degree in mathematics, statistics, computer science, or other quantitative field.
• Students interested in pursuing a career in data science.

Click here to apply now!

Deadlines
Spring: Nov. 15th
Fall: Jul. 1st

[accordion title=’Curriculum’]

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)

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[accordion title=’Sample Schedule #1 (Standard MA student schedule)’]

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 )

[/accordion]

[accordion title=’Sample Schedule #2 (1.5 year)’]

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

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[accordion title=’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

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Contact: Ratnasingham Shivaji, Graduate Program Director
r_shivaj@uncg.edu