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Darla Moore School of Business

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Center for Applied Business Analytics

The Moore School recognizes the need to increase the number of graduates with experience in business analytics, aiding our business community in addressing the ongoing shortage of professionals with analytics expertise.

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.

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 and an undergraduate concentration 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

Nichelle Carpenter 

Meta-analysis; descriptive statistics; data visualization; regression analysis

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

Ramkumar Janakiraman 

Marketing analytics; social media analytics; customer and retail analytics; social network analysis; causal models; applied econometrics

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 

Stacey Mumbower

Data visualization; predictive analytics; data mining; database management; econometrics; choice modeling

Greg Niehaus

Descriptive, predictive and prescriptive analytics related to decision making uncertainty

Sunny Park

Predictive and prescriptive analytics, big data analytics; machine learning 

Pelin Pekgun

Descriptive, predictive and prescriptive analytics; pricing analytics; supply chain analytics

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 VanEssen

Evidence-based management; meta-analysis; econometrics; data visualization

Joel Wooten

Descriptive analytics; regression modeling, VBA, sports analytics

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