Graduate Certificate in Business Analytics
The Business Analytics Graduate Certificate is designed, based on extensive benchmarking across academia and industry, to train the data-savvy managers that today’s marketplace demands.
Despite staggering increases in the ability to capture and store data, the majority of business decisions continue to be made in the same ad hoc fashion as they were before the information age. One of the reasons we do not see more data-driven decision making is that companies have an abundance of data but lack the ability to turn the data into useful knowledge. To accomplish this, firms need managers and leaders who combine solid business and communication skills with an analytics skillset. A recent study by the McKinsey Global Institute found that U.S. businesses looking to capitalize on the massive volumes of data available today face a shortage of 1.5 million of these types of managers who are well-trained in data analytics.
Candidates pursuing this certificate are required to complete four courses (12 credit hours) beyond the MBA core coursework; three of them required and one that may be chosen from a list of provided electives.
| Graduate Certificate in Business Analytics |
MGSC 777 - Advanced Quantitative Methods
MKTG 708 - CRM/Data Mining
MGSC 891 - Data Resource Management
Additional Courses: (one required for Graduate Certificate)
MGSC 778 - Revenue Management
MKTG 717 - Marketing Spreadsheet Modeling
FINA 772 - Portfolio Management
CSCE 590 - Big Data Analytics
(*Indicates required course for Business Analytics functional specialization)
*MGSC 777 - Advanced Quantitative Methods in Business:
Students will gain experience using cutting-edge analytical tools to support business decision making, including advanced topics in data visualization, geographic information systems, and Excel development with VBA. In addition, this course has a focus on both written and verbal communication of analytical results.
*MKTG 708 - CRM/Data Mining:
Firms have invested considerable resources in setting up customer relationship management (CRM) programs, while improvements in technology and software have provided the means to analyze key outcomes of CRM programs (as measured by satisfaction, loyalty, and profitability). Implementing a CRM marketing program entails extracting meaningful information from large databases using analytical techniques (commonly referred to as data mining), developing insights and strategies, and then implementing them. The topics that will be covered in this course include basics of customer relationship management; customer lifetime valuation analysis using transactional data; and data mining using Excel Miner to perform hands on analytics. Student will develop skills related to multiple linear regression, classification and regression trees, logistic regression, neural networks, discriminant analysis, market basket analysis, and cluster analysis. Companies use these techniques to evaluate customer profitability, target profitable customers, and implement data driven marketing decisions.
*MGSC 891 - Data Resource Management:
Overview of data resource management, including database technology and design, information architecture planning, and database administration. A design project is required.
MGSC 778 - Revenue Management:
This course covers the concepts of forecasting demand, segmenting customers and allocating capacity or customizing price offers to each distinct customer segment such that the firm's profits are maximized.
MKTG 717 - Marketing Spreadsheet Modeling:
This course focuses on the conceptual foundations and application of basic econometric and statistical models used in marketing analytics contexts. The understanding of such models should enable students to properly use them in real business settings using commonly available software.
FINA 772 - Portfolio Management:
Utilizes the techniques learned in FINA 762 to analyze and recommend investment opportunities for a portion of the Moore School endowment. The course culminates with a sequence of presentations and recommendations to the Moore School’s Business Partnership Executive Board.
CSCE 587 - Big Data Analytics:
This course covers foundational techniques and tools required for data science and big data analytics. The course focuses on concepts, principles, and techniques applicable to any technology environment and industry. Tools such as R and MapReduce/Hadoop are covered (Note that this is a full-semester course in the Computer Science department.)