This course introduces tools for decision-making under uncertainty, ranging from the fundamentals of probability theory, decision theory, and statistical models to basic software for data analysis. Topics include statistical independence, conditional probability, Bayes theorem, discrete and continuous distributions, expectation and variance, decision trees, sampling and sampling distributions, interval estimation, correlation, and simple regression.

This course provides an introduction to programming in Python and R, two of the most popular languages for data analytics. The course will cover basic concepts such as conditionals and loops, but also functions and program modularity, as well as algorithms and data structures.

This course provides a basic introduction on general business management. Topics include organizational structure, the role of different business domains in an organization (accounting, finance, operations, and marketing), and how they relate.

The objective of this course is to help you learn to analyze data and use methods of statistical inference in making business decisions. This course focuses on the application of fundamental concepts from Probability and Statistics to drawing inferences from data. Topics will include Bayesian modeling, multivariate analysis, causal inference, A/B testing, and experimental design, with special emphasis on diagnostics and model-building techniques appropriate to the study of real-world data. Assignments with applications to real-world data are an integral part of the course.

The focus of this course is on managing and retrieving data of all types (structured, semi structured, or unstructured), from both technical and business perspectives. The course topics include relational data management systems, theory of databases and models (CAP, ACID, Distributed Computing and Storage), Document (MongoDB), and other models for Big Data. The course also provides a basic conceptual introduction to Hadoop, Map-reduce, Hive, Apache Spark (in general, the big data architecture).

This course provides an introduction to the principles and techniques for data visualization. Students will learn visual representation methods and techniques that increase the understanding of complex data and models. Principles will be drawn from statistics, graphic design, cognitive psychology, information design, communications, and data mining. Specific topics that will be covered include design principles for charts and graphs, common visualization tools (Tableau, Excel, and R), effective presentations, dashboard design, and web-based visualizations. 

In this course, we explore common machine learning techniques and think about the application of these techniques to both structured and unstructured datasets found in business. Specific topics include Linear Rregression (logistics regression, k-nearest neighbors, and SVMs) and Unsupervised Learning (Principal Components and Clustering Methods: Hierarchical, partitioning, and probabilistic).

This course continues the introduction of machine learning techniques with a particular emphasis on business applications. Specific topics include Model and Variable Selection Model and Variable Selection (Overfitting and Overconfidence, Bias-Variance Tradeoffs; Information Criterions and Cross-Validation; Model Averaging and Ensemble Learning; Feature Selection; Regularization, Shrinkage and LASSO Estimators), Nonlinear Prediction Methods (Tree-Based Methods: Decision Trees and Random Forests; Regression Splines; Kernel Methods and Gaussian Processes), Modeling with Latent Variables (Hidden Markov Models; Graphical Models).

Mathematical optimization technology is key to turning data into better decision-making. The application of large-scale optimization models can bring a critical competitive advantage to many firms. This course focuses on developing such optimization models for operational and strategic decision making, with applications that include vehicle routing, employee scheduling, network design, and capacity planning. Methodologies include Linear programming, integer programming, nonlinear programming, constraint programming, heuristics, and column generation.

This is an integrative course that charts the path to value from analytical modeling for business problems. Building on a set of multidisciplinary cases cross-cutting across functional areas of business, this course integrates the three increasing levels of analytics involved in reaping business value from data in a given problem: the descriptive phase of inferring key features and relationships in the problem, the predictive phase of forecasting outcomes of short-term tactical actions, and the prescriptive phase of long-term planning based on analytics.

This course introduces students to both the micro and macro perspectives of organizational behavior and theory. At the micro-level, it covers factors for working in and managing an effective work team, including building teams, team contracting, team coordination, and team creativity. Macro topics include team networks, informal and formal organizational networks, communication networks, and innovation culture.

The course will address several areas of finance that rely heavily on data analytics, including 1) High Frequency Trading and Market Micro-structure, 2) Quantitative portfolios and Asset Management, 3) “Smart” Beta and Performance Analysis, and 4) Credit Analysis. The class uses tools from statistics, data mining (machine learning), and NLP/text-mining.

Operations and supply chain analytics is concerned with the development and application to data of business analytics tools to support high-impact strategic, tactical, and operational decisions within both manufacturing and service firms. Topics include supply chain design, demand forecasting, inventory planning, sales and operational planning, revenue management, staffing in service organizations, and healthcare management. The underlying feature in these applications is managing the risk that arises from supply and demand mismatches with the goal of maximizing enterprise value. The course emphasizes how sophisticated and holistic implementation of the operations and supply chain analytics toolbox, integrating descriptive, predictive, and prescriptive analytics techniques, can be an essential lever to increase or sustain a firm’s competitive advantage.

Marketing has become much more quantitative and data-intensive in recent years. Strategies like interactive marketing, customer relationship management, and database marketing push companies to utilize the information they collect about their customers to make better marketing decisions. Marketing transaction data — which is a common type of Big Data — often forms the core set of information used for making marketing decisions. This course focuses on how analytical techniques from data mining, machine learning, and statistical modeling can be applied to solve marketing problems. This course focuses on a series of data-intensive case studies to solve marketing problems. Specifically, the case studies considered include pricing decision support systems using retail transaction data, understanding customer churn in the cell-phone market, upgrading freemium customers to paying customers, and lifetime cycles in direct marketing. 

This course will be aimed at helping the MSBA students know how to target their future business audiences. Students will learn delivery skills AND will simultaneously learn how to construct arguments and problem-solve for decision-makers and how to understand what these audiences need from them (including superior, peer, and subordinate audience groups). Students will be evaluated on presentation assignments that are relevant to business analytics.

This course will explore the ethical challenges that businesses face when making use of AI, map out policies that have been proposed as solutions to these challenges, and analyze the normative arguments behind these policies. The goal of the course is to acquire the skills necessary to understand the ethical challenges which emerge from AI, and develop responsible corporate practices around these technologies. The course is organized around six core principles for the responsible use of AI: Autonomy, Explainability, Discrimination, Fairness, Benefit, and Responsibility & Control.

These courses are mini-semester-length projects designed to provide “real-life” business analytics context, for example, analytical marketing, operations, finance, or HR analytics. Students will be asked to manage a large data set, develop appropriate quantitative models and analytical insights, interact with the company, and deliver midterm and final presentations to company executives and faculty.