Master of Science in Applied Data Science

The School of Engineering offers a master's degree in applied data science (MSADS). Advances in technology have contributed to a deluge of data in virtually any domain. Computational techniques are being developed to store, process, and interpret data. New insights into data contribute to increased productivity, correlations among previously distinctive domains, and improved decision-making.

Data science is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. It employs techniques and theories drawn from many fields within the broad areas of statistics and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, uncertainty quantification, computational science, data mining, databases, and visualization. At the same time, it requires domain-specific knowledge to apply the techniques and theories effectively. Bob Hayes, Chief Research Officer at AnalyticsWeek thinks of 'data science' as "a flag that was planted at the intersection of several different disciplines that have not always existed in the same place." Current Data Science programs are either too technical or too specialized.

As an interdisciplinary program, the Master of Science in Applied Data Science (MSADS) decouples the computational techniques from the domains of interest, thus allowing all students to learn data science techniques, while concentrating on learning about data in one or more domains of interest.

Learning Goals

The MSADS provides outcomes for students from any domain, who are seeking a graduate program focused on obtaining non-trivial insight from Big Data. These outcomes cover not only the development of critical academic and professional skills, but also opportunities for employment in highly visible and needed sectors of the marketplace.

Students in the MSADS program will gain:

  • Advancement of data science and analytics through teaching and research in an environment that is conducive to achieving educational excellence.
  • Exposure to data science and analytics techniques, tools, and methodologies.
  • Exposure to domain-related issues related to data in any domain of interest.
  • Fundamental discovery in data science and analytics.
  • The ability to attain the highest standards in professional and ethical practice.

In sum, students will acquire the skills and real-world knowledge to succeed in applied data sciences through an in-depth exposure to the methodologies and tools of data science. A sequence of required courses and elective courses, and the final team-driven capstone project provide depth and breadth to the students' learning experiences.

In addition to required courses, those in specialization areas build in-depth knowledge and skills in the area of student's interest. Courses in other engineering and management fields are available as electives.


Students who wish to pursue the MSADS come from many different backgrounds. Some come from engineering and computing. Others come from specific domain backgrounds, like biology, healthcare, behavioral sciences, or business. All have a desire to use data to make deeper connections within their field and drive decision making.

Companies across industries and governments reap the benefit of using skills from data science to tackle complex Big Data challenges. Career opportunities can be found in commerce, government, for-profit and not-for-profit organizations, and the services and manufacturing sectors. Examples of employment opportunities for MSADS graduates include:

  • ​Applied Data Science Enterprise professional
  • Big Data consultant
  • Business intelligence reporting professional
  • Data Analyst
  • Data Controller
  • Data mining or Big Data Engineer
  • Health Data Analyst
  • Statistician
  • Research Data Scientist

Students may enter the MSADS program from any background, but may expect to take up to six credits of bridge course work to prepare for the program, depending on their background. For example, students with no prior programming experience would be required to take CS 0101 Introduction to Computing (Python programming). Students seeking to pursue a particular concentration may need to take a course in that field. These additional prerequisites will be determined on an individual basis at the time an offer of admission is made.

Data is ubiquitous in the modern world, and data scientists with skills and knowledge to analyze that data are a valuable, sought-after resource.

Prerequisites and Foundation Competencies

The MSADS degree requires students to have competencies that will allow them to pursue graduate coursework. Knowledge and/or experience in data science, programming, and specific domains is necessary. Gaps in knowledge and experience in these areas can be remedied by bridge courses offered in the MSADS program:

CS 0101Introduction to Computing3
Domain-Specific Bridge Course (Individualized)3

Students who are accepted into the program with certain bridge courses should complete the bridge requirement in the first semester with a grade of B or higher to satisfy the bridge requirement.  Students may take graduate level courses and bridge courses at the same time.  Bridge courses do not count for credit towards the degree.

Program Requirements

MSADS students will complete four required courses, as described below. In addition, students should select a concentration from one or more specialization areas in which they have an interest with their advisor's guidance. Concentrations currently include computational analytics or health analytics.  Additional individual areas of interest may be discussed with the advisor in areas such as bioinformatics, business analytics or social analytics. Students may also take two elective courses from the list below.  

The program requires two capstone courses and four required core courses listed below. Completion of a minimum of eight three-credit courses, plus the two-semester capstone sequence, for a total of 30 credits, comprise the graduation requirements for the MSADS program.

To earn the Master of Science in Applied Data Science, students complete the following:

MA 0417Applied Statistics I3
SW 0422Visual Analytics3
SW 0508Data Warehouse Systems3
SW 0518Data Mining and Business Intelligence3
Concentration Courses
Select two courses in one of the following concentration areas:6
Computational Analytics
Database Management Systems
Pattern Recognition
Health Analytics
Healthcare Economics and Marketing
Finance and Quality Management in Healthcare Organizations
Elective Courses
Select two elective courses from the following: 16
Computing Technical Electives
Computational Biology
Computational Statistics for Biomedical Sciences
Artificial Intelligence
Advanced Database Concepts
Applications and Data Security
Mathematics Electives
Applied Statistics II
Probability Theory
Statistics Theory
Capstone Sequence
SW 0550
SW 0551
Capstone Professional Project I
and Capstone Professional Project II
Total Credits30