Business professionals with a proficiency in business analytics are in short supply. According to a widely cited report by McKinsey and Co., by 2018 the United States could “face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” The Center for Applied Business Analytics (CABA) was established in June of 2015 to build capacity in the field of business analytics across the Moore School and the University of South Carolina.
The center regularly hosts events related to analytics in business, including the Applied Analytics Forum.
Mission and Key Goals
The mission of the CABA is to enhance business analytics teaching and research. A key goal is to provide our students with opportunities to better understand how to transform data into meaningful decisions through the use of analytics. The CABA plays a major supportive role in the Moore School’s business analytics curriculum, providing expertise in analytically rigorous methods. CABA also works with local and state business partners to bring in real-world data sets that can be used in teaching and research. By training our students how to make data-driven decisions using real company data, we better prepare our students to serve the needs of employers (state, regional and national).
Analytics Curriculum
The Moore School offers a Graduate Certificate in Business Analytics, an undergraduate concentration in business analytics and a Master of Science in Business Analytics. Students taking these program tracks are able to combine solid business skills with an understanding of analytics—a skill set that many employers are looking for.
Analytics Faculty
| Name | Analytics Focus |
|---|---|
| Rafael Becerril |
Descriptive, predictive and prescriptive analytics; analytics best practices; marketing analytics; research design; econometrics; data mining; Bayesian statistics; high performance computing |
| McKinley L. Blackburn |
Econometrics and regression modeling |
| Mark Cecchini |
Descriptive, predictive and prescriptive analytics; machine learning; sentiment analysis; analytics in accounting; Perl |
| Mark Ferguson |
Descriptive, predictive and prescriptive analytics; pricing analytics; supply chain analytics |
| Kirk Fiedler |
Database management; data architecture and knowledge management; privacy and ethics; innovation |
| Clark Hampton |
Audit data analytics; robotic process automation; data use privacy and ethics |
| Ozgur Ince |
Financial data analysis; forecast risk analysis, Monte Carlo simulation, risk-reward optimization; causal inference |
| Hugh Kim |
Sentiment analysis in finance; investor attention to disclosure information; big data analysis of financial institutions |
| Stanislav Markus |
Descriptive analytics; philosophy of science; survey design and analysis; case studies; elite interviewing |
| Greg Niehaus |
Descriptive, predictive and prescriptive analytics related to decision making uncertainty |
| Cem Ozturk |
Descriptive, diagnostic, predictive, and prescriptive analytics; empirical industrial organization; causal inference; channels and competition; marketing and public policy; sustainability; digital marketing |
| Sunny Park |
Predictive and prescriptive analytics, big data analytics; machine learning |
| Olga Perdikaki |
Descriptive, predictive and prescriptive analytics; retail supply chain analytics; empirical retail operations management |
| Maureen Petkewich |
Descriptive analytics and data visualization |
| Necati Tereyagoglu |
Descriptive, predictive and prescriptive analytics; structural estimation and causal analysis; pricing analytics; empirical operations management; people-centric operations |
| Marc van Essen |
Evidence-based management; meta-analysis; econometrics; data visualization |
| Sriram Venkataraman |
Descriptive, predictive, and prescriptive analytics; empirical operations management; structural estimation; healthcare analytics |
| Joel Wooten |
Descriptive analytics; regression modeling, VBA, sports analytics |
| Wenxin Xu |
Descriptive and predictive analytics; data visualization |
Upcoming Events
Master of Science in Business Analytics Virtual Info Session
Monday, Sep. 23, 2024
Location: See Description for Event Location.
Cost: FREE
Master of Science in Business Analytics Virtual Info Session
Monday, Oct. 21, 2024
Location: See Description for Event Location.
Cost: FREE
Master of Science in Business Analytics Virtual Info Session
Thursday, Nov. 21, 2024
Location: See Description for Event Location.
Cost: FREE