MAST - Mathematics, Analytics, Science and Technology

QTM6110 Data Exploration (Quantitative Methods)

MSEL Course

1.5 Credits

Data 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.

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?

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

QTM7200 Data, Models and Decisions

2 Credits

Data, 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.

QTM7571 Introduction to Machine Learning Methods for Business
(Formerly Business Intelligence, Analytics & Visualization)
3 Credits

This course introduces 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 supervised and unsupervised machine learning methods that companies use to turn data into insights, such as linear regression, k-nearest neighbors, logistic regression, classification and regression trees, etc. You will get hands-on experience in data pre-processing, generating business predictions, and model performance evaluation. Your learnings will be in practical contexts with in-class exercises and projects.

The various methods covered in this course will be implemented using a programming language. No prior knowledge in programming is required.

Prerequisites: QTM 7200 OR QTM7800

QTM7580 Independent Research

1.5-3 Credits

Independent 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: http://www.babson.edu/Academics/graduate/mba/Pages/independent-research.aspx

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.

QTM9510 Optimization Methods and Applications

3 Credits 

This is a hands-on course in quantitative business modeling designed to give you a practical approach to the main techniques necessary to make better business decisions and provide business insights. Models discussed span different business disciplines including finance, operations, transportation and supply chain, marketing, and human resources. Throughout the course, our focus is going to be on the mathematical and spreadsheet modeling in optimization, and on best practices for developing and solving optimization models. Students will work on an entrepreneurial, experiential case study at the end of the course.

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