Analytics, M.S.

Program Description

The Master’s of Science in Analytics (Master’s of Science in Analytics and Applied Artifical Intelligence, effective AY 24-25) program at DSU will prepare graduates with the skill set necessary to transform them into analytics and data science professionals. These professionals are needed to solve crucial data-driven business problems and assist with analytics-driven decision making that is needed in the work force as the progression of big data jobs continues to grow.

Use of information technology (IT) and statistical analysis continues to grow in business and industry and, along with that growth, comes a need for individuals with advanced training. Graduates of these programs may find careers such as analytics professionals, data scientists, data architects, data governance specialists, predictive modelers, business intelligence/analytics developers, data visualization specialists, business analysts, market analysts, financial analysts, supply chain analysts, data analysts, computational scientists, or machine learning software engineers in technical, industrial, business, health care and financial companies of all sizes, as well as in the public sector.

Program Delivery

Courses in the MSA program may be offered using a variety of instructional delivery methods:

1. Face-to-face on site in Madison, SD in a traditional classroom setting;
2. At a distance via Internet, using a combination of both live and/or encoded streaming videos of classes, interactive course web boards, course web sites, and e-mail. All courses are web-enhanced.

Program Goals

Upon graduation, graduates of the program will:

• be able to prepare and transfer big data sets into actionable information in an easy-to-understand format to support analytics through the use of advanced data processing tools.
• be able to select the appropriate analytics techniques and apply advanced analytical tools to solve data analytics problems.
• be able to demonstrate a good understanding of using information technology and computing languages to implement analytics solutions.
• be able to assess alternative approaches and infrastructures for implementing big data analytics.
• be able to manage data analytics projects to ensure delivery of a successful data analytics initiative throughout its life cycle.
• be able to interpret the results of the analysis.

Program Completion

The program can be completed on a full or part-time basis, with classes offered in three academic terms, fall, spring, and summer. Time to complete really depends upon the number of credit hours taken per semester and the number of knowledge requirements needed. Full-time students (6 credit hours per semester) can complete the program in four semesters (assuming two knowledge support courses are required). The program must be completed within 5 years of the date the program is started (first course taken).

Admission Requirements Specific to the MSA

Entering students will be required to have a baccalaureate degree from institutions with full regional accreditation for that degree. International students must have an undergraduate (bachelor’s) degree that is the equivalent to a four-year undergraduate degree in the U.S.

• Minimum undergraduate grade point average of 2.70 on a 4.0 scale (or equivalent on an alternative grading system)
• Transcripts should show completion of courses in key areas equivalent to:
  • Database design/programming including familiarity with SQL (INFS 760 or STAT 410/510)
  • Understanding of the principles of programming (INFS 605 or equivalent)
  • Understanding of statistical principles (INFS 608 or (STAT 281 and STAT 441/541))

Students who have not had appropriate coursework or acceptable experience to meet the knowledge requirements will be admitted to the program if they meet the other minimum requirements.   However, these students will be required to meet the knowledge requirements by satisfactory completing the specified knowledge support courses as part of their program Plan of Study (POS).

Program Faculty

Jun Liu, Program Coordinator

Dorine Bennett, Omar El-Gayar, Chris Olson, Ronghua Shan, Daniel Talley