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.

QTM 7515: Reinforcement Learning and Sequential Decision Making in Practice

3 credits

This course is a practical hands-on introduction to analytical models for sequential decision­ making, which involves making a series of decisions over time with the goal of improving metrics of interest. Reinforcement Learning (RL) - a field of Artificial Intelligence (Al) that brought us Google DeepMind's AlphaGo and self-driving cars - is a focal area of the course. You will learn about classical RL concepts and techniques, such as bandit problems, Markov Decision Processes, dynamic programming, Monte Carlo methods, and temporal difference learning. However, the main overall objective of the course is to illustrate the interaction and use of numerous analytical techniques necessary to solve actual business problems from diverse areas like marketing, inventory management, supply chain management, healthcare operations, manufacturing, financial services, and humanitarian logistics. The course is set up as a guided journey through the lifecycle of several projects that the instructor has completed with actual organizations, increasing in complexity as the semester progresses. The open-source programming language Python and possibly other software tools will be utilized as needed during the course.

 Note: This course is self­ contained. However, you will get more out of the course if you take it after taking at least one other modeling course at Babson (e.g., QTM7800, QTM7571/QTM6300, QTM9510, ECN7520/ECN6300/ECN63100). You do not need to have programming experience before taking the course.

(QTM 7800 for MBA students) or (QTM 6300 for MSBA students) Or (MSF Students)

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

QTM7575  Financial Modeling using Simulation and Optimization 

The focus of this course is on developing spreadsheet models for a wide variety of financial concepts including, but not limited to portfolio optimization, option pricing, asset allocation, value at risk, asset prices, etc. Students will gain familiarity with the financial instruments through the construction of the models, and will gain greater insights by analyzing and solving the models. Simulation and optimization are used extensively to analyze the models. Particular attention is paid to modeling uncertainty via random variables and the mathematics of stochastic variables.  

Prerequisite: QTM7200

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.

QTM9501 Business Forecasting

If you have taken ECN7520, Economic & Financial Forecasting, you cannot take this course.  

This course will introduce elementary time series models and discuss advanced forecasting methods in the context of real business data and decision-making situations._ The objectives of the course are to provide experience in using time series data (e.g., sales, profits, stock prices, economic indicators, industry sector indicators) to explain the impact of various internal and external factors and predict future trends; to provide a framework for comparing alternative forecasting models for validity, accuracy, and feasibility; to enhance an appreciation for the limitations of forecasting models; to provide exposure and experience in using statistical software to develop forecasting models; and to develop skills at communicating statistical concepts, methods, results, and inferences effectively in a managerial context._ Teamwork and professional presentation of analysis and recommendations will be required during this course.  

Prerequisite: QTM8400 or (QTM7010 and QTM7020 or QTM7010 and QTM8200) or completion of the One Year, Two Year or Fast Track modules

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

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

SCN 7500: Using the Science of System Thinking to Identify Market Driven Sustainability Solutions

3 Credits

The sustainability problems facing our society are extreme and wide-reaching - however, the pervasive nature of these problems result in amble business opportunities for the open-minded, knowledgeable, and creative leader. Sustainability-related issues are no longer an altruistic "tack­ on" to business strategy and development - but rather an integral component of current economic and business success.

This course will tackle the biggest sustainability challenges facing our global society through use of system thinking framework. Systems thinking provides a holistic way of examining the interconnected aspects of an issue, allowing the skills learned in this course to be highly transferable to a host of other challenges beyond sustainability related issues.

The course cover fundamentals of system thinking and then apply those tools to various sustainability problems. Much of the content will apply to climate change because this crisis is leading directly to new business opportunities, such as alternative energy markets, mineral supply shortages and the development of a battery recycling industry, altered global shipping routes due to opening of Arctic sea ice, burgeoning carbon credit markets to offset emissions, shifting consumer preferences, etc. Also, the far-reaching nature of climate change allows us to also touch on other related sustainability topics as they relate to business and the economy, such as water resource conservation and the circular economy, regenerative agriculture, global inequality, and the role of global geopolitics in solving sustainability problems.

Our focus will be solutions oriented. To do this we will learn to use a to draw causal loop diagrams, identify leverage points, and visualize the role of feedback loops and various policy initiatives in solving sustainability problems. Guest speakers from the private sector will provide additional context for application of these tools in the workplace. Supplemental readings will highlight how to translate knowledge into effective leadership in the workplace. We will practice making fact-based arguments through in-class debates about controversial topics related to sustainability solutions (e.g., carbon tax, plastic bans, which countries are responsible for paying for climate damages). Team learning is an essential component of the course; multiple group projects will enable students to work together to apply the tools taught in this course to identify climate solutions.

Prerequisites: None