Master of Science in Data Analytics


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In support of the university’s mission, the Master of Science in Data Analytics (MSDA) is designed to appeal to a broad range of individuals. The program balances theory with practice, offers an extensive set of traditional and state-of-the-art courses, and provides the necessary flexibility to accommodate students with various backgrounds, including computer professionals who want to expand their understanding of Data Analytics, as well as individuals whose undergraduate degrees are not in Computer Science but wish to broaden their knowledge in Data Analytics.


  • Design software applying modeling and data analysis techniques to solve real-world problems using cutting edge techniques, communicate findings, and effectively present results using data visualization techniques.
  • Demonstrate knowledge of statistical algorithms in data analysis for improved design decision making.
  • Apply social, ethical, and legal principles of technologies and their applications in the data analytics field.
  • Communicate effectively individually or in cross-functional teams.


  • Big-data architect
  • Principal Data Scientist
  • Data Warehouse Engineer
  • Management analyst
  • Data scientist
  • Data engineer
  • Research analyst – data science division
  • Instructor at a college or university teaching Data Analytics in addition to Computer Science courses.


  • Data Analyst (DA)
  • Principal Data Scientist (PDS)
  • Big Data Architect (BDA)
  • Big Data Analyst (BDA)
  • Data Warehouse Engineer (DWE)
  • Business Analysis Engineer (BAE)


Master’s in Data Analytics degree requires completion of 36 credits. Students will take 12 credits of core courses which is common with all the programs, 6 credits of career applications, and 18 credits in Data Analytics content area.

Area Number of Courses Credits
Core Courses 4 12
Career Application Courses 2 6
Specialization Courses 6 18
Total 12 36
Program Prerequisites

All new Data Analytics program students need certain basic skills to prepare them for success in the Data Analytics Program. Data Analytics degree provides a broad understanding of computer science theory and technology. Students who do not have the required background need to take some or all of the prerequisites before taking the core Courses. Thus, to be successful, students must have a background in the following courses.

Code Course Title Prerequisite Microcredentials Credits
COMP 109 Computer Algorithm and Programming Logic Using Python None 3
COMP 260 Introduction to Operating Systems COMP 109 3
COMP 270 Essentials of Networking COMP 109 3
COMP 329 Data Structures and Algorithm Analysis None 3
COMP 350 Database Concepts None 3
Core Courses (4 core course- 12 Credits)

These courses provide a breadth of foundational knowledge to implement computer interfaces, software design, communication between systems, and how to manage IT systems. These are all crucial elements for IT professionals to apply these building blocks to any given system or project.

Code Course Title Prerequisite Microcredentials Credits
COMP 501 Advanced Operating Systems COMP 260 3
COMP 502 Design and Analysis of Algorithms COMP 329 3
COMP 503 Networking and Telecommunications COMP 270 3
COMP 504 Database Management Systems COMP 350 3
Application Courses (2 Courses – 6 Credits)

These courses offer an opportunity for students to apply what they have learned throughout the program to a practical project or to a master’s thesis. While the practical project provides for application of knowledge acquired throughout the program and would be represent work that could demonstrate career-readiness to potential employers, the thesis would generally serve to demonstrate a student’s research potential and could be used to demonstrate readiness for doctoral work. Regardless of the option, students will demonstrate basic research knowledge and abilities, which would be used toward completion of either the project or thesis.

Code Course Title Prerequisite Microcredentials Credits
COMP 505 Research Methods None 3
Choose One of the Following
COMP 682 Data Analytics Capstone Project COMP 505 3
COMP 698 Master Thesis COMP 505 3
Specialization Courses (Any 6 Courses – 18 Credits)

These advanced courses cover the depth of topics related to Data Analytics and allow students to develop their knowledge based upon their intended professional trajectories.

Code Course Title Prerequisite Microcredentials Credits
COMP 523 Big Data Principles COMP 504 DA/BDA 3
COMP 524 Metadata Applications in Complex Big Data Problems COMP 504 PDS/BDA/BAE 3
COMP 525 Role of Analytics in Decision-making None DA/PDS/BAE 3
COMP 528 Data Analytics Foundation None DA/BDA 3
COMP 529 Information Fusion None BDA 3
COMP 531 Algorithms for Data Analytics COMP 329 BDA 3
COMP 542 Numerical Analysis   3
COMP 543 Data-Intensive Distributed Computing   3
COMP 544 Special Topics in Data Science None 3
COMP 596 Internship I in Data Analysis Completion of core courses and 50% of the program courses 3
COMP 626 Web Analytics None DWE 3
COMP 627 Descriptive and Predictive Analytical Tools COMP 528 DWE 3
COMP 628 Special Topics in Data Analytics None 3
COMP 629 Privacy and Security in Big Data None BDA 3
COMP 630 Text Analytics COMP 504 PDS 3
COMP 631 Cloudera Certified Associate (CCA) Data Analyst None Cloudera Certification/BAE 3
COMP 632 Microsoft Certified Azure Data Scientist Associate None Microsoft Azure Certification/PDS 3
COMP 696 Internship II in Data Analysis COMP 596 3

NOTE: Students who wish to take a course that is offered by in another program may petition to do so to their advisor by providing justification for the relevance of the addition as part of their professional trajectory, their intended consulting project, and/or personal interest. A maximum of 2 courses from can be applied from another program.


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