Analytics, MS

Program Description

The Master’s of Science in Analytics 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, healthcare and financial companies of all sizes, as well as in the public sector.

Program Goals

Upon graduation, graduates of the program will:

• be able to gather requirements from business or other contexts and goals to clearly articulate a data analytics problem;
• be able to interpret the results of the analysis in such a way as to generate actionable intelligence;
• be able to communicate the results of the analysis to stakeholders in the optimal combination of written, graphical/visual, and verbal means;
• be able to prepare and transform big data sets into actionable information in an easy-to-understand format to support business decision making 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 have 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.

Program Delivery

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

1. Face-to-face on site in Madison, SD in a traditional classroom setting;
2. Using interactive video-conferencing via the Dakota Digital Network offered at multiple sites in South Dakota (sites arranged to meet student need);
3. 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 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 (9 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))

Program Faculty

Dorine Bennett, Omar El-Gayar, Pat Engebretson, William Figg, Tom Halverson, Stephen Krebsbach, Jun Liu, Chris Olson, Josh Pauli, Wayne Pauli, Ashley Podhradsky, Ronghua Shan, Kevin Streff, Daniel Talley, Yong Wang

Program Requirements

The program requires 30 credit hours beyond the baccalaureate. All students must take the following:

Six Core Courses (18 credit hours)

• INFS 762 Data Warehousing and Data Mining 3 credits  
• INFS 772 Programming for Data Analytics 3 credits  
• INFS 774 Big Data Analytics 3 credits  
• STAT 700 Statistical Programming 3 credits  
• STAT 701 Modern Applied Statistics I 3 credits  
• STAT 702 Modern Applied Statistics II 3 credits  

Two required courses (6 credit hours)

• INFS 768 Predictive Analytics for Decision Making 3 credits  
• INFS 770 Advanced Data Mining Applications 3 credits  

Two track/elective courses (6 credit hours)

Electives are divided into three tracks that students may follow or they may elect to take two general elective classes. Tracks that students can follow are Information Systems, Healthcare Analytics, and General.