Master of Applied Business Analytics Courses

Concepts/principles of business statistics, data analysis, and presentation of results. Topics include exploratory data analysis, basic inferential procedures, statistical process control, time series/regression analysis, and analysis of variance. These methods are selected for their relevance to managerial decision making and problem-solving.

Introduction to Python with a focus on steps of using data for decision making; topics include: data acquisition, parsing, handling missing data, summarization, augmenting, transformation, subsetting, sampling, aggregation, and merging. Prereq: Programming experience

The use of visualization for exploring (and communicating with) data: discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. The students will have practical, hands-on experience with interactive data visualization using modern, state-of-the-art software on real-world datasets.

Case-, technical-, and discussion-based introduction to strategic use of artificial intelligence for firm strategy. Topics include: business value, impact, benefits, and limitations. Course is equally divided by cases, discussion, lecture, and technical demonstration.

Fundamentals of data exploration; detecting relationships and patterns in data; cluster analysis, hierarchical and partition-based clustering techniques; rule induction from data.

Fundamentals of predictive modeling and data mining; assessing performance of predictive models; machine learning and statistical classification and prediction; logistic regression; decision trees, random forests; k- nearest neighbor techniques, naïve Bayesian classifiers, neural networks.

Controlled experiments in business settings, experiment design, A/B testing; specialized statistical methodologies; fundamentals of econometrics, instrumental variable regression, propensity score matching.

Fundamentals of database modeling and design; extract, transform, and load; data pre-processing, quality, integration, and stewardship issues; advances in database and storage technologies for unstructured and big data.

In an era where artificial intelligence plays an increasingly pivotal role in business analytics, responsible AI practices are imperative. This advanced course will equip students with the knowledge, skills, and ethical mindset needed to develop AI solutions that are not only technologically proficient but also responsible, fair, and transparent. It covers topics such as ethical AI, algorithm fairness and bias mitigation, and explainable AI.

Hands-on, integrative application of analytics methodologies, techniques, and tools learned throughout the program in the context of a specific analytics problem. Introduction to agile project management. Experience with the entire data analytics cycle, starting from business and data understanding as well as data cleaning and integration and ending with the development and presentation of results, interpretations, insights, and recommendations.

Fundamentals of decision analysis, optimization, linear and integer programming, risk analysis, heuristics, simulation, decision technologies.

Analysis of time series data; understanding its components including trend, seasonality, autocorrelation, and stationarity; model interpretation and forecasting; traditional statistical and modern machine-learning views of temporal dependency; combining time series analysis with other business analytical tools to discover hidden knowledge and gain competitive advantages ahead of time.

Generative AI for Business Applications will give students an understanding of the transformative paradigm of Generative AI - its applications, limitations and specifically on how it can unlock previously inaccessible value. This course is designed to prepare students for the commercial and ethical choices they will face as leaders and managers - as adoption of these technologies becomes ubiquitous.

This course is designed for students who wish to -

Deepen Generative AI Understanding: Acquire an in-depth comprehension Generative AI framework, exploring its principles and advancements.

Excel in Functional and Consulting Roles: Equip yourself with applications of GenAI in various functional roles in diverse business settings, from startups to major corporations.  Also, enabling consulting opportunities by gaining expertise in technological innovation, navigating transformation, and securing competitive advantages in various industries through GenAI application.

Leverage GenAI for Business Innovation: Recognize and utilize the vast potential of Generative AI to enhance business strategies and decision-making across multiple industries.

Customer Analytics addresses how to use data to learn about and market to individual customers. Marketing is evolving from an art to a science. Many firms have extensive data about consumers' choices and how they react to marketing campaigns, but few firms have the expertise to intelligently act on such information. In this course, students will learn the scientific approaches to analyze and act on customer information. While students will employ quantitative methods in the course, the goal is not to produce experts in statistics; rather, students will gain the competency and working experience to interact with and manage a marketing analytics team. The course uses a combination of lectures, cases, and exercises to learn the material. This course takes a hands-on approach with real-world databases and equips students with tools that can be used immediately on the job.

The course will develop the capability of students to analyze and interpret big data – characterized by volume, variety, velocity, and veracity – that is fundamental to managing supply chains. Through a combination of lectures, readings, hands-on exercises with real world problems and datasets, and guest speakers from industry with expertise in big data analytics in managing supply chains, the course will provide opportunities to: (i) Get exposed to the various facets of big data analytics: data access, data aggregation, data analysis, data visualization, and data interpretation; (ii) Evaluate the appropriateness and inappropriateness of big data analytics; (iii) Work on big data analytics exercises relevant to managing supply chains; and (iv) Interpret and communicate the results of big data analysis to senior management.

Grounded in a data-driven approach to solving business challenges tied to people, this course emphasizes how students can apply their HR domain knowledge to inform, shape, and deliver on people analytics projects. The course aims to give you exposure to core concepts in machine learning, including prediction, classification, and segmentation, in order to collaborate with data scientists, generate insights, and inform decisions. Students will learn how to more effectively communicate their data insights through streamlined storytelling to provide compelling recommendations to decision makers. Students will be given the opportunity to use Excel and/or Tableau, and will also be introduced to predictive analytics software.