Analytics

Specialize in Analytics.

This Advanced Certificate of Achievement demonstrates a competence in analytics.

To earn it, an MSAIB student must complete 7.5 credits. Students are required to take the required courses (each worth 2.5 credits):

 

CIS 432: Machine Learning For Business Analytics

This course aims to train practitioners of analytics methods to construct, evaluate, and apply machine learning (ML) models in a variety of business applications using modern tools. The course covers programming tools for handling data, computational frameworks, cloud platforms, and an array of advanced ML algorithms. Hands-on work is emphasized through class exercises, homework assignments, and projects. The course is self-contained, but basic programming skills are required.

GBA 468P: Prescriptive Analytics with Python

GBA468P expands and develops the students’ analytical toolkit to include prescriptive analytics methodologies for managerial decision-making. The coursework follows the FACt approach to business problem solving and will cover diverse applications in operational management, supply chain analytics, marketing, and finance.

Modeling techniques covered include: Decision tree models Constrained optimization models Monte Carlo simulation The course will be taught primarily using Python.

GBA 436R: Predictive and Causal Analytics

Businesses now gather data on their customers, competitors, and marketplace on an unprecedented scale. Simultaneously, the adoption of machine learning has made the cost of estimating sophisticated predictive models cheaper than ever. As the cost of making predictions has decreased, the need for decision-makers who can critically examine these analyses has increased. This is because implementations of sophisticated machine learning methods can lead to dramatically incorrect decisions and inferences if decision-makers do not correctly link their statistical models to the business context and the problem at hand.

This course is fundamentally about how to learn from data. We will demonstrate the stark differences in the goals and implementations of descriptive, predictive, and causal analysis. Students will expand their analytic toolkit in each of these areas and learn how to select the right tool for the job. Particular attention will be paid to using these analyses to drive business decisions and how to respond to data analytics questions in job interviews.

This course emphasizes a hands-on approach. We will apply data analysis in six unique cases and four assignments that span a wide variety of topics and feature real-world data. The course uses the R programming language and RStudio.

The ultimate objective of this class is to prepare you to be a producer and consumer of data analytics and to enhance your ability to correctly utilize data when making business decisions. Whether working with data directly or communicating with people who do, these skills are essential for the more sophisticated and successful business decision-maker.