Graduate Courses

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: Evening: (QTM7010 and QTM8200) or QTM8400 or QTM7200 Fast Track: MBA7335 or (ECN7201 and MIS7200) One Year: MBA7210 or QTM7200 Two Year: MBA7320 or QTM7301 or QTM7200

Meeting Dates: Friday, November 1st – 6:30 – 9:30 PM Saturday, November 9th – 8:00 AM – 5:00 PM Saturday, November 16th – 8:00 AM – 5:00 PM QTM9515 Introduction to Data Science and Business Analytics 1.5 credit Intensive Elective 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 discusses the data analytics lifecycle, and introduces predictive analytics techniques in the context of real-world applications from diverse business areas such as database marketing, financial forecasting, and operations. The focus is on framing business problems as analytics problems. A brief map of applications and an overview is provided for advanced methods for data visualization, logistic regression, decision tree learning methods, clustering, and association rules. Students will gain exposure to different software packages for data visualization as well as R, the most popular open-source package used by data scientists around the world. Since R is freely available, students will be able to apply the skills acquired in this course regardless of where they work after graduation. Students can pursue these topics in more depth in QTM7571 Business Intelligence, Analytics, and Visualization.

Face-to-Face Dates: August 8-10 QTM7200 | DATA, MODELS AND DECISIONS 2 credits This course prepares students to identify variation, measure and manage data 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.

Face-to-Face Dates: August 15-17 QTM7200 | DATA, MODELS AND DECISIONS 2 credits This course prepares students to identify variation, measure and manage data 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 8200 Applied Decision Models This is a core class for the Evening MBA Program only. Students must complete or have advanced standing credit or pass a waiver exam in QTM7010, Statistics, in order to take this class. The focus of this class is on developing decision support models and using these tools to enhance decision making. It is an application-oriented introduction to the modeling techniques used to structure the way we think about managerial decision situations. Methodologies considered are decision analysis, simulation, optimization, and sensitivity analysis. Spreadsheet models are developed with applications to finance, operations management, logistics, and resource allocation. Some of the classes will be distance-learning classes and some will be held on campus. The asynchronous distance-learning classes provide the flexibility to learn at one's convenience. The on-campus classes will provide instantaneous feedback and allow interaction with fellow students and the professor. Prerequisite: QTM7010

The SLE for Cluster B will be April 20th from 8AM to Noon QTM7200 (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. All of the courses in a cluster must be taken at the same location. QTM7200 will meet for 7 sessions (including final exam), plus a 3 hour online session, and a 2 hour integrated session (April 20th) with ECN7200 and MKT7200. The integrated session is typically scheduled for the final Saturday of the semester - details will be confirmed on the first day of class. QTM7200 is part of Cluster B, but can be taken prior to ECN7200 and MKT7200 as a prerequisite or students can take QTM7200 as a co-requisite while enrolled in ECN7200 and MKT7200. QTM7200 is a prerequisite for Cluster F MOB7202 and MBA7201. QTM7200 is equivalent to QTM8400 Data and Decision Modeling or QTM7010 Statistics and QTM8200 Applied Decision Models.

QTM7571 Business Intelligence, Analytics & Visualization Formerly Business Intelligence & Data Mining 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. We will address questions such as: 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. - 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? Prerequisite: Evening: QTM7010 or QTM8400 or QTM7200 Fast Track: MBA7335 or (ECN7201 and MIS7200) One Year: MBA7210 or QTM7200 Two Year: MBA7320 or QTM7301 or QTM7200

Face-to-Face Dates: March 7-9 QTM7200 | DATA, MODELS AND DECISIONS 2 credits This course prepares students to identify variation, measure and manage data 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.

XXX7580 Independent Research ******Independent research is available for all academic divisions.Registration is manual for students through Graduate Programs and Student Affairs****** 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 Programs and Student Affairs. 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. Authorization for such a project requires submission of a formal proposal written in accordance with standards set forth by the Graduate School. The research project normally carries 1.5 or 3 credits. For more information and a proposal outline please visit: www.babson.edu/grad/gpsa