Certificate in Business and Managerial Analytics
Decision making is increasingly more complex and challenging due to factors such as rapidly changing technology, social media, globalized supply chains, and availability of data on virtually every aspect of a business. Today’s business managers need to understand and rely on current technology to leverage organizational knowledge, and use analytical models and statistical techniques to make data-driven decisions.
Marquette University's Graduate School of Management offers a 15 credit, graduate certificate program in business and managerial analytics. It is a modular certificate that stands alone but also allows for credits to be “stacked” into existing graduate business programs (i.e., MBA or MS programs).
The program is designed for working professionals and managers wanting to understand how to leverage their organization’s data and to utilize it in business decision making and risk reduction. No programming knowledge is required for this certificate.
This certificate helps those with existing business acumen to properly frame business problems in light of the overall management strategy, to identify appropriate data within and outside of the organization, to apply analytical approaches, and use business judgement to understand how results inform the art of decision making.
See the curriculum in a graphical representation in this flowchart.
Core requirements - 9 credits
MBA 6100 - Business Analytics (3 credits) -OR-
Provides a structured and effective way of tackling a wide range of managerial problems using analytics. Introduces students to basic concepts in business analytics and several quantitative techniques that are important for the practical analysis of a broad range of business problems and widely accepted by the practitioners. These techniques provide a framework to support managerial decision making. Lectures cover the topics in sufficient details to make one feel comfortable in their use. Also stresses the importance of critical thinking skills to make sound managerial decisions, to apply ethical reasoning to business situations and to communicate effectively in business settings.
MSCS 6520 - Business Analytics
Foundational topics in the analysis of data from a business perspective. Includes methodology for the development of business analytics systems. Examines technology employed for business analytics in a variety of industry segments and the benefits derived from business analytics. Foundations of text and data mining techniques commonly used for classification, clustering and prediction. Students are presented techniques for developing a business case, evaluating predictive performance and managing data. Includes exercises using analytic technology and a project to apply analytics to a customer application.
MSCS 6510 - Business Intelligence (3 credits)
Foundational topics in business intelligence. Includes properties and benefits for business intelligence and methodology for the development of business intelligence solutions. Examines technology employed for managing data and creating visualizations and dashboards. Topics include developing a business case, evaluating performance and managing data. Presents overview of data architectures commonly used in business intelligence solutions and includes exercises using common techniques for prediction and time series analysis.
MSCS 6931 - Ethical and Social Implications of Big Data (3 credits)
Electives - 6 credits
MARK 6130 - Customer Relationship Management (3 credits)
Analyzes how companies can obtain a sustainable competitive advantage by managing their relationships with their customers more effectively. Teaches the main marketing variables that impact customers' satisfaction judgments. Emphasis on understanding the powerful relationship between customer loyalty and company profits. Discusses and evaluates the most effective methods for responding to dissatisfied customers' complaints.
MARK 6160 - Marketing Research (3 credits)
Addresses how the information used to make managerial decisions is generated by gathering data, analyzing data, interpreting results, and preparing research reports. Appropriate for both users of research results and those who aspire to be marketing researchers. The format consists primarily of lectures, some video presentations and a research project. SPSS, and to some extent, SAS are used for performing data analysis.
MARK 6165 - Marketing Analytics (3 credits)
Analytics adds an all-important quantitative edge to marketing, helping companies transform data, information and insights into more effective decisions and higher profits. For students and business professionals preparing to advance in marketing, analytics is one of the top must-have skills that hiring companies are seeking. Differs from traditional marketing research courses by focusing on the marketing strategies underlying quantitative analysis and how that analysis leads to greater profitability. Gives students a toolbox of techniques to explore familiar marketing challenges. Uses a combination of hands-on practice, case studies, guest speakers and lecture to give students the analytical tools and the mindset to migrate from a qualitative to a more quantitative brand of marketing and improve job potential
ECON 6560 - Applied Econometrics (3 credits)
Specification, estimation, and statistical verification of multiple linear regression models, and hypothesis testing. Causes, consequences, detection of such problems as heteroscedasticity, autocorrelation, specification and measurement errors. Other topics include estimation of models with panel data and limited dependent variables.
ECON 6561 - Applied Time-Series Econometrics and Forecasting (3 credits)
Continuation of ECON 6560 focusing on more advanced econometric and forecasting techniques using primarily time-series models such as ARIMA and transfer functions, VAR, VEC, and GARCH, as well as the method of combining forecasts. Emphasis on the practical knowledge of above techniques, and on reporting and presenting econometric results.
Gainful Employment Disclosure
Gainful Employment Disclosure - student acknowledgement form