Academic Courses

This course is a refresher of critical math skills you will use regularly in your MSBA Classes. Topics covered include: Calculus Review, Basic Probability, Random Variables and Probability Distributions, Sampling and Sampling Distributions, Bayesian Approach, Matrices, and Logarithms. Bootcamp does not teach these subjects but rather refreshes the student’s prior knowledge of the aforementioned topics.

This course refreshes the students’ knowledge of programming for data manipulation and analysis. built upon a prior knowledge of programming, the course will conduct exercises in R, Python, SQL and other languages. students will also be using a variety of unix tools, file-transfer methods, etc. Course may also employ cloud-based services such as AWS and/or Azure.

This course introduces students to economics of business and provides an overview of a variety of business functions including marketing, strategy, operations, finance, etc. The course familiarizes students with commonly used frameworks in business.

This course runs throughout the length of the program. Students are provided the basis to abstract a business problem into a data-based problem and provided a methodology commonly adopted by consulting firms. The course then combines the consulting method with the CRISP-DM framework commonly used in data science projects. This course provides the foundation for managing projects in the capstone course.

This course examines how statistical methods lead to understanding and describing data. We use R/RStudio to review basic data exploration and statistical methods. Topics covered include simple descriptive statistics through hypothesis testing and modeling.

In this course, students gain competence in practical database, data warehousing, and data management skills with emphasis on query, data modeling, ETL, and data management. They will also become familiar with major elements of the big data ecosystem.

We will study the fundamental principles and techniques of data mining in order to extract useful information and knowledge from data. We will improve our ability to approach problems "data-analytically,” examine real-world examples that place data mining in context, and apply data-mining techniques while working hands-on with data mining projects. The course will provide an understanding of the general framework for building and evaluating predictive models, both for classification and numeric prediction data mining tasks. The course will cover supervised predictive modeling techniques as well as unsupervised predictive modeling techniques.

This is an introduction to basic concepts in machine learning, both supervised and unsupervised. The basic ideas of neural networks will be presented. Machine learning from data streams (online learning) using stochastic gradient descent is also covered.

Where the art of graphic design meets with the science of data analytics. Learn how to perform exploratory analysis through visualization, how to create professional-looking visualizations for use in business reports and presentations, and how to design interactive visualizations and dashboards.

Delve into a number of selected current and emerging data analytics areas that are increasingly important for organizations. Areas include advanced elements of the predictive modeling process, ensemble methods, cost-aware data analytics, mining text and data, recommender systems, and other advanced topics.

This course introduces students to optimization and simulation, powerful techniques for better decision-making (in particular, linear, integer and non-linear programming), and Monte Carlo and discrete event simulation, and discuss their application to problems in business and data analytics.

The MSBA Analytics Practicum leverages skills and techniques learned throughout the course of the program and applies them to real-world business situations. Students formally define problems, clean data, aggregate with other data sources, and identify and use appropriate analytical techniques to address questions.

This course will introduce students to state-of-the-art quant models. Students will individually develop quant models and back-test them using archival data. Students will learn to address the following issues:

• What are the stock characteristics that predict future returns?
• How do you build a quantitative model that effectively combines multiple characteristics?
• How to back test to assess the performance of quantitative models?

This course introduces the theories and practices for modern project management: methodologies for organizing effort, quantitative tools for planning and evaluating project risk and progress, and inter-personal skills for leading and communicating with various project stakeholders. Topics include the role of project and portfolio management, organizational structures, and their influence on project management, managing scope and specifications, planning and network scheduling, monitoring, and reporting, costing, risk management. We will use network analysis, simulation, decision trees, and linear optimization methods relevant to project management.

Hands-on, gentle introduction to the tools that data scientists use to forecast the future, including bleeding-edge methods developed by the instructor. You will learn how to apply these tools to applications ranging from demand forecasting to algorithmic trading to real time warning systems for adverse medical events. We will cover time series forecasting from both the classical as well as the machine learning perspectives in the R environment, one of the industry standards in data science.

This course will begin discussion process analytics including process metrics, types of variability, process control (statistical process control), and process capability analysis. It will then move on to discuss issues relating to causal analysis. This will lead to a discussion of experimentation in organizations, one-factor experiments (A/B testing), one-way Analysis of Variance (ANOVA), two-way ANOVA, factorial designs, and fractional factorial designs.

Deep learning models aim to recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions, and reinforcement learning methods focus on learning how to map situations to actions, so as to maximize a reward. ISOM 678 introduces some of the key ideas and techniques developed from the machine learning communities on deep learning and reinforcement learning, with an emphasize on the practical understanding and implementations of these techniques in real world business problems. In this course, algorithms and concepts are presented to build intuition for how different methods work, without advanced mathematics. Topics include single- and multi- layer networks, convolutional and recurrent neural networks, multi-armed bandits, finite Markov decision processes and on-policy and off-policy data methods. Programming is central to the course and is based on the R and Python programming languages.

The course introduces modern data-driven supply chain management techniques. Specific focus will be on supply chain resiliency and efficiency. The topics include deep-tier supply network visibility, supply risk identification, demand and lost sales estimation, data-driven sales and operations planning, and inventory management. We will use graph-theoretic, simulation, econometric, and operations research methods relevant to supply chain management.

The MSBA Analytics Practicum leverages skills and techniques learned throughout the course of the program and applies them to real-world business situations. Students formally define problems, clean data, aggregate with other data sources, and identify and use appropriate analytical techniques to address questions.

Using real-world applications from various industries, the goal of the course is to familiarize students with tools and methods used for measuring and managing the value of current and future customers effectively. The tools we introduce are commonly employed by managers to assess marketing decisions, such as a new customer acquisition campaign. These tools are also used by executives and investors to assess the health of customer-based businesses.

AI in Marketing examines the burgeoning role of AI in marketing decisions and actions. The course will adopt the customer equity framework, which links the value of the customer to the organization to the following components: customer acquisition, customer retention, and relationship development. The course will be built around these components, examining the application of marketing technology and AI to support growth through each component. We will use the customer journey to tie these components together.

Pricing is a critical business activity and one that is often viewed as a significant challenge. In this course we will take a hands-on approach to developing pricing tactics and strategies. Using data whenever possible we will explore techniques for setting prices in a variety of contexts.

This course will focus on the use of analytic methods to solve marketing problems. Problems will be drawn from a variety of contexts including pricing, customer relationship management and sales forecasting. The course will emphasize the use of marketing analytics as a means of supporting decision making. Toward this end, we will emphasize the development of tools that can be implemented in practice. We will rely on both frequentist and Bayesian methodologies.

Over the past decade, a professional sports team's decision processes have been transformed from being based mainly on intuition and experience, to being based on copious amounts of data and sophisticated statistical models; this trend has even been highlighted in popular culture through the bestselling book and blockbuster movie, "Moneyball". In this course, we will focus on the use of analytics and data for improving human capital related decisions in the context of both sports and non-sports. The core of the class will focus on developing data management and statistical skills.

This course introduces students to statistical and computational methods used in employee recruitment, selection, promotion and turnover. A significant portion of the course is devoted to experimental methods designed to test the efficacy of HR policies before an organization implements them.