Math and Science | Babson College

The Course Catalog includes course descriptions of all courses offered by F. W. Olin Graduate School of Business. For descriptions of the courses offered in the current or upcoming semesters, please see our Course Listing

Math and Science


QTM6110 Data Exploration (Quantitative Methods)

1.5 CreditsData is valuable when it is used to make good decisions and avoid bad ones. We consider the value of data as a resource by studying how the variety of information available can be displayed, interpreted and communicated. Students will see the different approaches suggested by both traditional statistical methods and the recent advances in big data analytics. The course will emphasize the ways in which managers and entrepreneurs are both producers and consumers of data.

1.5 credits


QTM6300 Machine Learning for Business
(Formerly Data Exploration and Analytics)
3 Blended Credits
This course will examine the methods and challenges faced in turning data into insightful analytics in business. With data sizes significantly increasing in the last decade, extracting meaningful information to compete successfully is essential. You will accomplish this by learning techniques for data gathering, data analysis, and visualization as well as in discussion on companies currently trying to turn the information they gather into business opportunities. We will learn a variety of methods and software for finding patterns(such as regression, neural networks, association rules, CART, forecasting etc.), building models, and ultimately making decisions using large data sets. Guest speakers who are executives and consultants in the field of analytics and visualization will discuss how they address these challenges in their companies. This is a hands-on course with in-class exercises and group projects to help students learn and apply data analysis techniques preparing them for the practical challenges analysts face in the real world. We will address questions such as:

- How does Amazon recommend products based on your past purchases?
- How to forecast energy consumption based on historical weather and consumption data?
- How do credit-card companies detect fraud?
- What challenges does Big Data pose to companies and how to handle these challenges?

3 credits


QTM6600 Analytics for Decision-Makers
1.5 Credits (MSAEL core)
Data exploration and data-driven decision making are integral in identifying and validating business opportunities. Depending on the nature of the problem and the institutional context, techniques ranging from classical statistical methods (descriptive and inferential statistics) to more recent advances in big data and tools (Excel, R, Tableau) might provide the greatest utility and deepest insights. In this course, we encounter selection of these techniques and develop our ability to formulate analytics problems in ambiguous contexts, quantify performance of various solutions, and articulate the key results of our analysis to a non-technical audience, including using visualization methods.

Prerequisites: MOB6600 and EPS6600

1.5 credits


QTM7200 Data, Models and Decisions

2 CreditsData, Models and Decisions (DMD) - This course is concerned with identifying variation, measuring it, and managing it to make informed decisions. Topics include: numerical and graphical description of data, confidence intervals, hypothesis testing, regression, decision analysis, and simulation. Applications to Economics, Finance, Marketing, and Operations illustrate the use of these quantitative tools in applied contexts. The course utilizes spreadsheet, statistical, and simulation software.

2 credits


QTM7571 Introduction to Machine Learning Methods for Business
(Formerly Business Intelligence, Analytics & Visualization)
3 Credits
This course introduces statistical and machine learning methods for business intelligence. Given the ease of data collection and storage, extracting meaningful information from data has become an essential trait for competitiveness, for companies large and small. In this course, you will learn a variety of statistical and machine learning methods that companies use to turn data into insights. You will get hands-on experience with the implementation of statistical and machine learning methods, from data pre-processing to generating predictions, and evaluating model accuracy. Your learnings will be in practical contexts, such as:

- Predicting prices of homes as provided by the leading real estate database company
- Predicting the statuses of the loans for the peer-to-peer loan lending company LendingClub
- Building a recommendation engine for the music website

The various methods covered in this course will be implemented using the RStudio coding language. No prior knowledge of RStudio is required.

Prerequisites: QTM 7200 OR QTM7800

3 credits


QTM7580 Independent Research

1.5-3 CreditsIndependent research is available for all academic divisions. Registration is manual for students through Graduate Programs and Office of Graduate Academic Services.

Independent Research provides an opportunity to conduct in-depth research in areas of a student's own specific interest. Students may undertake Independent Research for academic credit with the approval of a student-selected faculty advisor, the appropriate division chair, and Graduate Academic Services. Please note that a student is responsible for recruiting a faculty advisor through the student's own initiative and obtain the advisor's prior consent/commitment before applying for an independent research project. The research project normally carries 1.5 or 3 credits.

For more information and a proposal outline please visit:

3 credits


QTM7800 Business Analytics

2 Credits (Core MBA)If you have taken and passed QTM7200, you cannot register for QTM7800, as these two courses are equivalent

In the BA stream of the course, regression models are used to understand dependence relations and thereby improve the accuracy of predictive modeling. Sensitivity analyses are used to determine which factors drive our decisions, and, thus, determine which factors need to be carefully managed. In the OIM stream of the paired course, strategic tradeoffs are discussed to understand the operations and information models for a variety of settings (e.g., startups, nascent or established organizations) and thereby improve any model by utilizing resources (e.g., physical assets, people, data, digital technologies, markets) and processes for the flow of goods, people and information.

2 credits


QTM9505 Financial Simulation
1.5 Intensive Elective Credits
This course focuses on a quantitative technique, simulation, that enables finance professionals to make informed decisions under uncertainty. After taking this course, students will:
(a) have a basic understanding of the theoretical background for this technique; (b) have experienced implementing simulation models with Excel, @RISK, and VBA; (c) have used simulation in important financial applications such as new product development, capital budgeting under uncertainty, asset allocation under different definitions of risk, modeling asset price dynamics, derivative pricing, and hedging.

Prerequisites: QTM7800

3 credits


QTM9510 Optimization Methods and Applications

1.5 Credits

This course provides an introduction to optimization models and their applications to a variety of business decision problems. Linear, integer, and network models will be discussed. Emphasis will be placed on understanding the relevant applications for various models, the strengths and limitations associated with the models, using software tools to solve optimization problems, and interpreting and analyzing solution results. In-class analysis of problems and/or cases will be utilized to reinforce material covered in lecture. Teamwork outside the class will be required in order to complete assignments. Evaluation will be based on both individual and team performance.

3 credits


QTM9515 Introduction to Data Science
(Formerly Introduction to Data Science and Business Analytics)
1.5 Intensive Elective Credits
This course is an introduction to data science - the science of iterative exploration of data that can be used to gain insights and optimize business processes. The course is set up as a journey through the data analytics lifecycle of a project based on an actual company and introduces predictive analytics techniques in the context of real-world applications from diverse business areas. A map of applications and an overview is provided for advanced methods for data visualization, logistic regression, decision tree learning methods, clustering, and association rules. The course utilizes the advanced visualization software Tableau, the free open-source statistical modeling language R, and various other tools like cloud computing to gain insights from data. The case studies include data sets from a variety of industries and companies, including financial planning startups, online retailers, telecommunications companies, and healthcare organizations.

Prerequisites: QTM7200 or QTM7800

1.5 credits