M.S. Degree in Data Science

The Master's of Science degree program in Data Science is an interdisciplinary program hosted by the Department of Computer Science and the Department of Mathematical Sciences. The program is a DHS STEM Designated Degree Program [30.7001].  It situates computing-specific competencies in computer science and statistical-related fields including database, data mining, machine learning, and big data within the broader interdisciplinary space. It uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structured and unstructured data. One may think of data science as a blending together of methods and ideas from analysis, statistics, databases, big data, artificial intelligence, numerical analysis, graph theory, and visualization for the purposes of finding information in data and applying that information to solving real-world problems. It incorporates empirical, theoretical, and computational techniques to solidify data-driven decisions. The program offers a thesis track as well as a non-thesis track. The program also offers opportunities to the students to extend the interdisciplinary space to other disciplines beyond Math and CS.

The key competencies for the Master's degree in Data Science include:

  • Computational and statistical thinking, and applied mathematical foundations
  • Algorithms and software foundations
  • Computing fundamentals, including programming, data structures, algorithms, and software engineering
  • Data acquirement and governance
  • Data management, storage, and retrieval
  • Data mining and machine learning
  • Big data, including complexity, distributed systems, parallel computing, and high-performance computing
  • Analysis and presentation, including human-computer interaction and visualization
  • Model building and assessment

Admission Requirements

The formal program requirements are listed in the University Catalog. Students entering the Master's program in Data Science are expected to have a Bachelor's Degree from an accredited college or university with a grade point average of 3.0 out of 4.0 and with the following mathematics and computer science courses completed before beginning the program. If all the courses have not been completed by the time of application, then acceptance into the program will be conditional upon the remaining courses being completed before beginning the program.

In addition to standard university requirements, acceptance into the program requires a cumulative GPA of 3.0 for the prerequisite courses and for students whose native language is not English, proof of English proficiency. Proof of English proficiency may be a transcript showing full-time attendance for at least one academic year at a university where the instruction is in English, or TOEFL scores of 71 on the IBT or 525 on the PBT. IELTS scores may be submitted in place of the TOEFL. The minimum IELTS requirement is a score of 6.0.

How to Apply

Application Deadline

We accept applications year-round and review them as they come in. It is important to complete and submit your application as early as possible. It can sometimes take several weeks to route and process your application.

Program Requirements?

The formal program requirements are listed in the University Catalog, but in summary, students must complete 30 credit hours of which 18 credit hours are required courses, 6 credit hours of electives, and 6 credit hours of culminating experience.

Required Courses (18 Credit Hours)
Elective Courses (6 Credit Hours)
Culminating Experience (6 Credit Hours)

For more information about graduate admissions, please visit the Graduate Studies website. For more information on international admission, visit the Office of Global Education website.

Plan of Work

Semester Course Work Hours Offering
1st Math 50015: Applied Statistics 3 F
  Math 50024: Computational Statistics 3 F
  CS 63005: Advanced Database Systems Design 3 F
2nd Math 50028: Statistical Learning 3 S
  CS 63016: Big Data Analytics 3 S
  Elective 3  
3rd CS 63015: Data Mining Techniques 3 F
  Elective 3  
  Capstone or  Internship or Thesis I 3 F, S
4th Capstone or Internship or Thesis I 3 F, S
  Total: 30  


Graduation Requirements:

  • Successful completion of 30 credit hours.
  • Successful completion of the Capstone Project for the non-thesis option.
  • Successful thesis defense for the thesis option.
  • Six of the elective credit hours must be taken at the 60000 levels.
  • Students may complete a capstone-related elective course in place of an internship with approval from the graduate coordinator.
  • GPA must be kept greater than or equal to 3.0 all the time.

Financial Aid: Scholarships, Graduate Assistantships,  and Teaching Assistantships