Program Information

Overview of Program Information

The Master of Science with Major in Data Science and Analytics (MSDSA) is a multi-college interdisciplinary program jointly administered by the Department of Mathematical Sciences in the Charles E. Schmidt College of Science, the Department of Computer & Electrical Engineering and Computer Science in the College of Engineering and Computer Science, the Department of Information Technology and Operations Management in the College of Business and the Department of Political Science in the Dorothy F. Schmidt College of Arts and Letters. The program aims to prepare students with essential skill sets needed to analyze small, fast, big, massive and complex data. To allow for maximum flexibility in career aspirations, students may select from four concentrations:

Data Science via Scientific Inquiry Concentration, Department of Mathematical Sciences.
Data Science and Engineering Concentration, Department of Computer & Electrical Engineering and Computer Science. (This concentration is also available fully online.)
Data Science in Business Concentration, Department of Information Technology and Operations Management.
Data Science in Society Concentration, Department of Political Science.

College of Business Program Sheet

Additional details about the version of the program offered by the College of Business can be found in the document below:
- Program Sheet (pdf)

Comparing Business Analytics to Data Science

Business Analytics	Data Science
Is the statistical study of business data to gain specific business insights	Is the study of data in general, using statistics, algorithms, and technology
It is mandatory for businesses to use business analytics to gain a competitive advantage	Is an intellectual, abstract, and technical field - advancing the technology and science of data analytics
Is strategic - answers the question as to what a specific analysis means for the business	Is technical - answers the question as to what mathematical and computer science concepts to use to make the data analysis efficient and accurate
Is domain specific; business domain expertise is required	Is a generalist field interested in data as numbers; no domain expertise is required
Is used by business decision makers. Findings are used to inform the organizations' decision-making.	Is not used by business decision makers. Findings cannot inform organizations' decision-making.
Is a cognitive and business-oriented analysis, seeking direct implications for the business ("so what?" story)	Is algorithm-based and machine learning and AI constructed
Is only used when a well-defined business question is asked with the given data set	Is used without clear questions that need to be answered with the given data set
Studies trends and patterns specific to a business	Studies almost every trend and pattern
Does not involve much coding. It is more statistics oriented.	Coding (programming) is widely used. Employs traditional analytics and good computer science knowledge.
Bases the analysis on statistical concepts	Uses statistics at the end of analysis following coding
Heavy use of tools: focused on a basket of built tools, techniques, with each tool used for specific business problems. Tools used extensively in business analytics are Excel, Tableau, PowerBI, SQL, Python, SPSS, SAS, Google analytics, and many more. The most commonly used techniques are - Statistical Methods, Forecasting, Predictive Modeling and storytelling.	No use of tools; focused on mathematical concepts, algorithms and coding.
Stakeholders' privacy is an important factor in the approaches to analysis and data handling	Privacy issues are not considered
Top industries where business analytics is used: finance, healthcare, marketing, retail, supply chain, economics, telecommunications.	Top industries/applications where data science is used: physical sciences, machine learning, AI.