Mission

The mission of the MS in Data Science program is to provide students with the ability to integrate the theory and practice of computing in the representation, processing, and use of information while upholding the tradition of the liberal arts education.

Program Educational Objectives

Graduates of the MS in Data Science program shall:

1. Demonstrate advanced knowledge and skills in data science, including data analysis, machine learning, statistics, and programming, enabling them to effectively tackle complex real-world problems in various domains.
2. Engage in continuous learning and professional development activities to stay abreast of the latest advancements in data science and the computing field.

Student Outcomes

By the time of graduation, students in the MS in Data Science program will have an ability to:

1. Analyze a complex problem and apply principles of computing and other relevant disciplines to elaborate solutions to it;
2. Design, implement, and evaluate a computing-based solution to meet a given set of requirements in the context of the program’s discipline;
3. Communicate effectively in a variety of professional contexts;
4. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles; 
5. Function effectively as a member and leader of a team engaged in activities appropriate to the program’s discipline;
6. Apply theory, techniques, and tools throughout the data science lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.

Admission Requirements

Students applying from the following BS disciplines: Computer Science, Data Science, Mathematics, Bioinformatics, Statistics, Engineering, ITM, and Physics. Students applying from related majors may need remedial courses.

Curriculum

A minimum of 30 credits are required to graduate, distributed as follows:

• Core Courses (18 credits)
• Elective Courses (6 or 9 credits)
• Project or Thesis (3 or 6 credits)

Core Courses (18 credits)

• DSC602     Python for Data Science (3 cr.)
• DSC604     Statistics for Data Science (2 cr.)
• DSC610     Data Science and its Applications (3 cr.)
• DSC611     Applied Machine Learning (3 cr.)
• DSC612    Data Ethics (1 cr.)
• DSC613    Data Engineering (1 cr.)
• DSC614    Data Visualization (2 cr.)
• DSC697    Research Methods in Data Science (3 cr.)

Elective Courses (6 or 9 credits)

Students must choose 6 or 9 credits from the following courses:

• DSC603      R Programming (1 cr.)
• DSC605      Time Series Analysis (1 cr.)
• DSC615      Big Data Analytics (2 cr.)
• DSC622      Deep Learning and its Applications (3 cr.)
• DSC623      Natural Language Processing with Deep Learning (2 cr.)
• DSC624      Reinforcement Learning (2 cr.)
• DSC625      Introduction to Generative AI (2 cr.)
• DSC643      Statistical Methods in Finance (3 cr.)
• DSC652      Artificial Intelligence for Managers (3 cr.)
• DSC651      Design and Analysis of Algorithms (3 cr.)

Project or Thesis (3 or 6 credits)

• DSC698      Capstone Project (3 cr.)
• DSC699      Thesis (6 cr.)

Sample Study Plan

Year One

Fall (8 credits)

• DSC602 Python for Data Science (3 cr.)
• DSC604 Statistics for Data Science (2 cr.)
• DSCXXX Elective (3 cr.)

Spring (9 credits)

• DSC611 Applied Machine Learning (3 cr.)
• DSC612 Data Ethics (1 cr.)
• DSC614 Data Visualization (2 cr.)
• DSCXXX Elective (3 cr.)

Year Two

Fall (7 credits)

• DSC610 Data Science and its Applications (3 cr.)
• DSC613 Data Engineering (1 cr.)
• DSC697 Research Methods in Data Science (3 cr.)

Spring (6 credits)

• DSC699 Thesis (6 cr.) OR
• DSC698 Capstone Project (3 cr.) ₊ DSCXXX Elective (3 cr. Or 2+1 cr.)