Master of Science in Data Science

The School of Engineering offers a master's degree in data science (MSDS). 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 Data Science (MSDS) 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 MSDS 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 MSDS 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 MSDS 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 MSDS 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 MSDS 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 CPSC 1101 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.