Business Analytics Track

Customer Analytics is a research-oriented class that examines how firms can use analytics to gain actionable insights about customers both internal to the firm (employees) and external to the firm (consumers) to create, manage, and grow their business and brands. The class gives students the analytical tools to develop and operationally execute strategies that enhance customer engagement and loyalty.

Advanced Quantitative Analytics provides students with multivariate statistics training to analyze “big data” sets. The course covers discrete choice modeling (logistic and probit models), classification techniques (discriminant and cluster analyses), data reduction techniques (factor analysis), and advanced predictive techniques (regression models with interactions and curvilinear effects, structural equation modeling, and factorial ANOVA). The course also trains students on three widely-used statistical packages, IBM-SPSS, SAS, and R.

This course provides an overview of the concepts, technologies, and tools necessary to support and improve electronic commerce, with emphasis on tools and methodologies for measuring and enhancing digital presence. The two major areas covered are web analytics and search analytics. Through a semesterlong group project, the course focuses on how these concepts can be used to measure, analyze, and improve user experience, web traffic, and conversion rates.

 

Business analytics leverages the vast data resources available today to identify trends and patterns that are critical to enhancing business performance. This course introduces students to contemporary business analytics methods, including predictive and descriptive analytics techniques, and demonstrates how to practically apply analytics to real-world business decisions.

This course aims to provide students with a practical understanding of AI technology. It covers key factors for the successful development, deployment, and management of AI; machine learning; and other algorithmic approaches to automated decision making. Students will better understand the societal impacts of AI, ethical considerations in the use of AI, the limitations of AI, and approaches to balance AI risks and benefits.

This course is focused on harnessing the power of unstructured data to perform advanced analytical techniques. Students will be exposed to big data technologies (NoSQL, Hadoop, etc.) to understand how to manage and interact with large, complex data sets. We will also cover various analytical and machine learning techniques that can apply to these data, with particular attention to text data from reports, articles, and social media.

The course provides an overview of the fundamentals necessary to conduct data analytics with Python, including understanding Python objects, data types, structures, packages, and data flow statements and, reading, writing, manipulating, and plotting data. Students will perform predictive analytics via machine learning using industry-standard packages.

This course provides students with an introduction to how to effectively fill the role of project manager. It covers a blend of conceptual knowledge and practical skills necessary for the effective management of complex projects.

This course is designed to provide a broad overview of management consulting and other related advisory services professions while also helping students develop skills that are broadly applicable in these professions as well as in other fields (business, politics, not-for-profit, etc.). Working both individually and in teams, students will gain an appreciation of what makes consulting and advisory services unique from other areas of business.