Data Science
Program Overview

Program Overview

The MS in Data Science program consists of 30 graduate-level semester credit hours, of which 15 are foundation, 12 are concentration, and 3 are elective (including the option coursework or project, or internship). Admitted students must declare a concentration.

The program includes 4 concentrations: (1) Data Science, (2) Machine Learning (3) Deep Learning, and (4) Data Mining. Students must choose one of three options: coursework, MS project, or Internship. The program may be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.

Program Objectives

Program Objectives

The Master of Science in Data Science (MSDS) program is designed to prepare students for opportunities and challenges in the field of data science. An MS in Data Science can lead to a variety of career opportunities with high earning potential.

This program offers students the essential knowledge and skills to design, develop, and implement Data Science systems across various high-demand sectors. The curriculum allows students to develop a uniquely broad spectrum of tools and approaches for understanding, analyzing, and modeling data.

Students who complete the MS in Data Science can design, conduct, interpret, and communicate data analysis tasks and studies using methods and tools of statistics, machine learning, computer science, and communications.

Curriculum

Curriculum

The MS in Data Science requires completion of the following 30 credit hours::

Foundation Courses (15 cr)
  • MSDS 510 – Artificial Intelligence
  • MSDS 520 – Database Management
  • MSDS 530 – Quality Assurance
  • MSDS 650 – Data Visualization
  • MSDS 660 – Software Development Life Cycle
Concentration (12 cr)
  • MSDS 610 – Data Science
  • MSDS 620 – Machine Learning
  • MSDS 630 – Deep Learning
  • MSDS 640 – Data Mining
Elective (3 cr)
(Choose one course from the following three courses)
  • MSDS 699 – Capstone Project
  • MSDS 690 – Management of AI Technologies
  • MSDS 695 – Internship
Total Program: 30 Credits
Course Descriptions

Course Descriptions

Explore detailed course descriptions, learning outcomes, and prerequisites for each course in the program.

  • MSDS 510 – Artificial Intelligence: 
  • MSDS 520 – Database Management: 
  • MSDS 530 – Quality Assurance: 
  • MSDS 610 – Data Science: 
  • MSDS 620 – Machine Learning: 
  • MSDS 630 – Deep Learning: 
  • MSDS 640 – Data Mining: 
  • MSDS 650 – Data Visualization: 
  • MSDS 660 – Software Development Life Cycle: 
  • MSDS 690 – Management of AI Technologies: 
Program Admission Requirements

Program Admission Requirements

  • Completed Graduate Admissions Application and Application Fee of $50
  • Bachelor’s degree in a related field — Data Science, Computer Science, Information Technology, Software Engineering, Electronics & Communications, Electrical Engineering, Robotics Engineering, Cyber Security, Artificial Intelligence, Communications Engineering, Computer Applications, Computer Engineering, or work experience in related fields
  • Minimum undergraduate cumulative GPA of 2.0
  • Official transcripts (foreign transcripts require evaluation by a recognized agency — cost is the student’s responsibility)
  • Professional Resume
  • Two letters of recommendation
  • Statement of Purpose
  • GRE (optional)
  • Proof of English Language Proficiency — TOEFL / IELTS / PTE / Duolingo (non-native English speakers)
  • Proof of funds
Degree Outcomes

Degree Outcomes

Successful completion of the MS-DS Program will enable students to:

  • 01Integrate components of data science to produce knowledge-based solutions for real-world challenges using public and private data sources.
  • 02Apply technical knowledge to a variety of real-world problems in diverse substantive areas.
  • 03Prepare for careers as data scientists by solving real-world, data-driven business problems and understand social, ethical, legal, and policy issues.
  • 04Gain expertise in in-demand areas like machine learning, data visualization, data mining, and deep learning.
  • 05Develop team skills to ethically research, develop, and evaluate analytic solutions to improve organizational performance.
Credit for Prior Learning

Credit for Prior Learning

Eligible students may receive credit for prior learning, professional certifications, or relevant work experience.

A maximum of six credits of transfer credit or other approved prior learning credit may be applied toward a master’s degree program. Credit may be granted based on official transcripts, verified work experience, or professional certifications and must be approved by qualified faculty or the Admissions Department. Such credits are recorded as “Credit for Prior Learning,” carry no letter grade, and are excluded from GPA calculations.

Career Options

Career Options

Career options may require additional experience, training, or other factors beyond the successful completion of this program. A Master of Data Science degree can academically prepare you to pursue career options such as:

  • Data Scientist
  • Data Engineer
  • Machine Learning Engineer
  • Quantitative Analyst
  • Business Intelligence Analyst
Work Settings

Work Settings

A Master of Data Science degree can academically prepare you to work in settings such as:

  • Retail and E-commerce
  • Banking and Finance
  • Pharmaceuticals and Research
  • Public Administration
  • Energy and Utilities
Tuition & Fee

Tuition & Fee

Total Credits
30
Credit Hours
Cost per Credit
$500
Per Credit Hour
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