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 following two courses (each worth 2.5 credits): 

CIS 434: Social Media and Text Analytics

The rise of social media has empowered customers in an unprecedented way. They are well connected with one another through platforms like Facebook and Twitter, and they can easily express and distribute their comments, criticisms, or endorsements publicly to large audiences in real time.

This fundamental media revolution not only forces companies to actively manage their presence and engage with customers on social media platforms, but also offers a golden opportunity to extract intelligence from vast amounts of unstructured data. Technology and strategy are increasingly intertwined in this new frontier of innovation and competition.

This course draws on a unique blend of social media strategies and the rapidly evolving information technologies that support them. We will explore issues related to monitoring and analyzing social media across companies and industries.

Learning objectives include:

1) Gaining a deeper understanding of social media and its implications in the business world

2) Becoming comfortable working with text data

3) Understanding and applying commonly used methods for analyzing text data

GBA 436: Causal and Predictive Analytics

Businesses now gather data on their customers, competitors, and the broader marketplace at an unprecedented scale. At the same time, the adoption of machine learning has dramatically reduced the cost of estimating sophisticated predictive models. As the cost of making predictions has decreased, the need for decision-makers who can critically evaluate these analyses has increased.

Without a clear link between statistical models, business context, and the problem at hand, even sophisticated machine learning methods can lead to dramatically incorrect decisions and inferences.

This course is fundamentally about learning from data. We will demonstrate the key differences in the goals and implementation of descriptive, predictive, and causal analysis. Students will expand their analytic toolkit across each of these areas and learn how to select the right tool for the job.

Particular attention is paid to using analytics to drive business decisions and to effectively responding to data analytics questions in job interviews.

This course emphasizes a hands-on approach. Students will apply data analysis across six unique cases and four assignments spanning a wide variety of topics, all featuring real-world data. The course uses the R programming language and RStudio. While some prior experience with R is helpful, materials are provided for those who are new to the language or need a refresher.

The ultimate objective of this course is to prepare students to be both producers and consumers of data analytics, enhancing their ability to correctly use data when making business decisions. Whether working directly with data or communicating with those who do, these skills are essential for today’s more sophisticated and successful business decision-makers.

Students also choose an additional course from the two courses below: (2.5 credits each): 

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. The course emphasizes hands-on work through class exercises, homework assignments, and projects. The course is self-contained, but basic programming skills are required.

GBA 468P: Prescriptive Analytics with Python

GBA468 expands and develops the students’ analytical tool kit 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 and Monte Carlo simulation. The course will be taught primarily using Python.