Content and Professors

This session provides an introduction to the use of Python for abuilding analytical models. The session will focus on how Python code snippets can be edited to instantiate different types of models.

Once we are familiar with the basic concepts of descriptive statistics we can take on more complex tasks. In this session students investigate how to implement these concepts in Excel and how to use statistics to generate conclusions. The first topic covered here is hypothesis tests; the primary decision-making tool in statistics. We begin by applying hypothesis tests to one-variable problems. More realistic models require multiple variables. In working with multiple variables we build regression models. The objective is to develop a model where the dependent variable is explained by one or more independent variables. This allows identification of the factors that determine the outcome of the dependent variable. Students will become familiar with implementing regression in Excel and interpreting the Excel output.

Linear programming (LP) is a tool for the mathematical optimization of complex decision problems. This session begins with a fun, hands-on, interactive problem-solving exercise drawn from production planning. This example is used to introduce the basic concepts behind LP as a mathematical model of the exercise is developed and then solved using the Solver in Microsoft Excel. The session concludes with students building small models in Excel using examples drawn from distribution planning and financial management.

Some real-world problems cannot be modeled using pure optimization techniques, particularly those that involve an element of chance. In these situations, we often resort to simulation, a methodology that combines decisions (e.g., how many service representatives to hire) and uncertainties (e.g., the demand for service). Simulation involves building a computer model that generates probabilistic outcomes based on the decisions we make and the uncertainties we face. One can use simulation models to determine how specific decisions will perform in practice without experiencing their effects firsthand. In this lecture, we will demonstrate how many complicated problems can be analyzed using simulation models built in spreadsheets.

This lecture is designed to introduce you to some of the models and methods used in the emerging field of revenue management (RM). The problem that motivates RM is ancient: how does a company manage its capacity and/or prices to extract the greatest possible revenue from the marketplace? The RM approach is to exploit differences in customer segments and their willingness to pay. For example, a person who books a room in an upscale hotel three days in advance is typically willing to pay much more than someone booking the same room three months in advance. RM focuses on how to manage these different types of customer demands to maximize revenues.

In this class meeting, the focus is on decision-making under uncertainty. For a large class of decision problems, the outcome of a decision is uncertain, but the decision-maker is able to list the possible outcomes of a decision and assign a probability to each of them. Sources of uncertainty include such factors as consumer demand, competitors' behavior, and acts of nature. To address these uncertainties in a decision-making context, decision trees have proved to be a powerful graphical tool. Decision trees allow a decision-maker to consider the possible outcomes of his decisions in a systematic way and to draw correct conclusions with respect to the best course of action. Spreadsheet implementation of decision trees further extend to the decision-maker the ability to perform sensitivity analysis to gain a deeper understanding of how sensitive the optimal solution is to changes or inaccuracies in model assumptions.

Whatever industry you and your team are focused on - be it manufacturing, financial services, life sciences or electronics - projects are key vehicles for innovation, productivity improvement, and growth. The ability to manage the crisp and consistent execution of projects is becoming a crucial capability for the success of most ventures. In fact, as a firms offerings become more digitally driven and/or service-oriented, the manufacturing and assembly component of work declines, and project planning and execution play a vital role. This session discusses the essentials of project management, covering broadly the technical and social aspects of project execution.

Forecasting is an essential step in almost making any business plan. For example, without good sales forecasts, inventory management and capacity planning can go badly wrong. And almost any reliable budget depends on a reliable forecast of the variables influencing cost. As a consequence, analysts have developed a number of different forecasting techniques. This session explores several different methods most commonly used to forecast. A special emphasis is placed on how to integrate forecasting into the business planning process. Exercises will be presented using both excel and specialized forecasting software.

This session will discuss overviews of Database Management Systems, including Relational and On-Line Analytical Processing Systems. A real world example will show managing data for a web site; from online ordering to Pick and Pack. The class will also look at basic Structured Query Language (SQL) statements. The class lab will take a series of flat files and turn them into a database.

Almost every company spends time retrieving data and placing it into spreadsheets.This can be a huge waste of time and often a robotic process.The module will discuss ways to automate this process with Visual Basic for Applications (VBA).A real world corporate example will show how one company retrieves data to help facilitate the management of data from four different reporting methods. The lab will contain a coding exercise to import data in to a spreadsheet.

In this course, students learn to effectively communicate the results of business analytics and business decisions in written and oral presentations, including key questions for the analytic communications: What is happening? Why is it happening? What are the next steps? The course covers data visualization methods, as well as how data visualization software such as Tableau can be used to dramatically improve data analysis  and managerial communications.

This session provides a roadmap of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multi-dimensional data models and OLAP operation; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. We survey the state of the art and mention representative products.

This session covers two types of data mining techniques based on AI and machine Learning, that are useful for predictive modeling. These are CART (Classification and Regression Trees), and Neural Networks. The goal of the session is to not only understand how to build such models, but also gain an appreciation for the particular circumstances in which each modeling technique is appropriate.

In this session, we focus on two additional methods for data mining. First, we examine Clustering, which is a data mining method for discovering distinct segments within a broad data set. We also discuss how clustering can be a useful step in developing multiple target marketing or segmented modeling strategies. The second half of the session examines Market Basket Analysis using Association Rules. This is a useful method for mining large transaction databases to discover affinity patterns. e.g., collections of products that are bought together, or by the same buyers over time.

This session is divided into two sections. To give students an introduction to web analytics, the first half of the class explores how quantitative Internet data to optimize websites and web marketing initiatives is collected, analyzed and reported. These topics are explored through Google Analytics. Particular attention will be paid to the meaning of key metrics included in the Google Analytics suite based on an understanding of how the data from which they were derived. The second half of the class introduces students to the challenges and opportunities of social media. Key difference between web and social media analytics will be drawn.