MS in Data Science and Analytics

Graduates will be able to individually acquire and stage large data sets, design and conduct experiments, and analyze results for complex data analytical problems using their foundational and specialized data science tools and techniques; taking a problem from conceptualization stage through to the production of data-derived business intelligence. 

The special skills the graduating students will acquire or possess include:

  • Real-world experience in applying state-of-the-art data science tools and techniques to solve industry, academic, and/or business data and decision-making challenges.
  • A clear understanding of the ethics and security mechanisms required to safeguard large-scale data collections that contain sensitive and critical information.
  • A comprehensive understanding of modern data analytics, statistical analysis, and visualization tools that facilitate timely, large data analysis.
  • A solid foundational understanding of database systems, database design, and information retrieval; allowing exploitation of a broad spectrum of data repositories and streaming data systems.
  • A demonstrated ability to effectively communicate to a broad audience the relevant information derived from large data collections using a variety of visualization and presentation methods. Students will be able to convey the meanings behind specific data analysis techniques to audiences of various technical knowledge.
  • Training in the latest data analytic methods and tools; including fundamental and advanced statistical and mathematical principles upon which advanced data analysis techniques are built (machine learning, pattern recognition, data mining, etc.).
  • Specialized, advanced training in a chosen emphasis area, such as BioHealth Analytics, High-Performance Computing, Strategic Communications/Data Journalism, Human-Centered Science Design, or Geospatial Analytics.

Degree Requirements

All students will take Core Courses that will provide a foundation of knowledge and an introduction to state-of-the-art technology in Big Data, database design, data ethics, and visualization of high-dimensional and high-volume data.

To understand real-world Big Data issues in context, students will select three courses in an emphasis area. These elective courses will support in-depth analyses and training on data analytic techniques, issues, and problems students will face within a given emphasis area. Students will take a Case Study course to gain hands-on experience with large data sets and use the relevant technology and techniques. A Capstone project will enable students to refine and demonstrate knowledge and skills learned throughout the program. Both courses will provide students with mentoring from faculty, as well as insight from industry partners.

Required Core Courses
DATA_SCI 7010Principles of Data Science and Analytics3
DATA_SCI 7020Statistical and Mathematical Foundations for Data Analytics3
DATA_SCI 7030Applied SQL for Database and Analytics3
DATA_SCI 7040Big Data Visualization3
DATA_SCI 8000Data and Information Ethics1
DATA_SCI 8010Data Analytics with Applied AI and Machine Learning3
Case Study
DATA_SCI 8080Data Science and Analytics Case Study2
Capstone
DATA_SCI 8090Data Science & Analytics Capstone Project3
Electives9
Total Credits30

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Sample Plan of Study MS Online

First Year
FallCreditsSpringCreditsSummerCredits
DATA_SCI 70103DATA_SCI 70203DATA_SCI 80803
DATA_SCI 70303DATA_SCI 80103 
DATA_SCI 70403DATA_SCI 80203 
 9 9 3
Second Year
FallCreditsSpringCredits 
DATA_SCI 80001DATA_SCI 80903 
Emphasis Area Course 13Emphasis Area Course 33 
Emphasis Area Course 23  
 7 6  
Total Credits: 34

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Sample Plan of Study MS On-Campus

First Year
FallCreditsSpringCredits
DATA_SCI 70103DATA_SCI 70203
DATA_SCI 70303DATA_SCI 80103
DATA_SCI 70403DATA_SCI 80203
 9 9
Second Year
FallCreditsSpringCredits
DATA_SCI 80803DATA_SCI 80903
Emphasis Area Course 13DATA_SCI 80001
Emphasis Area Course 23Emphasis Area Course 33
 9 7
Total Credits: 34

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