QTM

Credits 4

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

Credits 4

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

Credits 4 Babson College Undergraduate Catalog 2026-2027

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

4 Intermediate Liberal Arts Credits This course builds on the modeling skills acquired in the QTM core with special emphasis on case studies in Business Analytics – the science of iterative exploration of data that can be used to gain insights and optimize business processes. Data visualization and predictive analytics techniques are used to investigate the relationships between items of interest to improve the understanding of complex managerial models with sometimes large data sets to aid decision-making. These techniques and methods are introduced with widely used commercial statistical packages for data mining and predictive analytics, in the context of real-world applications from diverse business areas such as marketing, finance, and operations. Students will gain exposure to a variety of software packages, including R, s the most popular open-source package used by analytics practitioners around the world. Topics covered include advanced methods for data visualization, logistic regression, decision tree learning methods, clustering, and association rules. Case studies draw on examples ranging from database marketing to financial forecasting. This course satisfies one of the core requirements towards the new Business Analytics concentration. It may also be used as an advanced liberal arts elective or an elective in the Quantitative Methods or Statistical Modeling concentrations. Credits 4

Subject

QTM

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Prerequisite

QTM1010 (or QTM2420)

4 Advanced Liberal Arts Credits Linear Algebra provides the mathematical background for modern applications in statistics and data science. In this course we study linear algebra beginning with the classic but still essential application of solving systems of linear equations. We use this as an entry to think of the properties of high dimensional spaces, and the relationships between those spaces. Students will learn how to compute with matrices and see their application to diverse areas such as cryptography, image recognition, page rank in computer searches and establishing fair ranking and voting systems. Credits 4 293

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Prerequisite

AQM2000

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

4 Advanced Liberal Arts Credits The course will provide students with the opportunity to reinforce their skills in data science, including but not limited to data acquisition, data preparation/wrangling, exploratory data analysis/visualization, model building, testing, presentation, and deployment by coaching them through a real-world, data-intensive project. The course will consist of two main components: • A structured curriculum designed to enhance students’ data science and analytics skills for implementing analytical projects and effectively communicating their results to management and other stakeholders; • A consulting project in which student teams will be tasked with solving a real-world problem presented by an external organization or partner, utilizing techniques and methodologies from data science and analytics. Under the supervision of the instructor, student teams will work on the assigned problem throughout the semester, communicating with the external organization/partner during the project and presenting their results periodically throughout the project. Despite being a group project, the workload will demand each student to dedicate significantly more time to the course compared to AQM courses that do not involve work with an external organization/partner. This course satisfies the Advanced Experiential learning requirement through an extensive project involving an external organization/partner. curriculum) Credits 4 294

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Prerequisite

AQM2000 (QTM 2000 in the older

Babson College Undergraduate Catalog 2026-2027

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

(Formerly QTM2610) 4 Advanced Liberal Arts Credits This course extends the modeling tools presented in prior statistics courses and focuses on the application and validation of models developed using real data in the context of finance, economics, and marketing research. Examples of applications include modeling the impact of advertising on sales, admission yields for business schools, patterns of voting behavior and a variety of survey data. This course focuses on implementing data analysis techniques using a statistical software package and interpreting the results in a decision-making environment. Emphasis is placed on understanding the limitations of modeling approaches, as well as the diversity of potential applications in business. Credits 4 295

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Prerequisite

AQM2000

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

132

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

4 Advanced Liberal Arts Credits Cryptology includes the study of both cryptography, the science of developing _secret codes_ or ciphers for secure and confidential communication, and cryptanalysis, the breaking of ciphers. Coding theory consists of mathematical techniques for detecting and correcting errors that occur during data transmission. These topics are critical to secure and reliable information exchange, with applications ranging from e- commerce to the transmission of photographs from deep space to military operations. Through this exploration into the technical, social, and historical aspects of cryptology and coding theory, students will learn and extensively use basic concepts from number theory, finite field and ring theory, matrix algebra, and the software package GAP. Highlighted topics include the RSA cryptosystem, digital signatures, DES, linear and cyclic codes, and the coding theory based McEliece cryptosystem. This course is suitable for students with one year of university-level mathematics, or the equivalent; it should also be interesting for upperclassman from a variety of majors. Credits 4

Subject

QTM

Credits (min)

4

Credits (max)

4

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Prerequisite

AQM1000

The fundamental objective of this course is to prepare students for the successful completion of the first level probability examination (Exam P) of the Society of Actuaries. While the necessary theory is addressed, this course focuses on problem solving, so it is well suited fo any student with an interest in applied probability concepts and how they are related to a wide variety of situations within and beyond actuarial science, finance, and economics. Topics include general probability and univariate and multivariate probability distributions. Credits 4

Subject

QTM

Credits (min)

4

Credits (max)

4

Credit unit

Credits

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Prerequisite

AQM2000

Credits 0 Babson College Undergraduate Catalog 2026-2027

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

Credits 0

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

Credits 0

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

Credits 0

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

Credits 0

Subject

QTM

Type

course

Edition

2026-2027-undergraduate

Source

catalogs.babson.edu

(Quantitative Methods)

Subject

QTM

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

(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? Credits 3 199

Subject

QTM

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Data exploration and data-driven decision making are integral to identifying and validating business opportunities. Depending on the nature of the problem and the entrepreneurial 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, students encounter a selection of these techniques and develop their ability to formulate analytics problems in ambiguous contexts, quantify the performance of various solutions, and articulate the key results of their analysis to a non-technical audience, including through visualization methods. Credits 1.5

Subject

QTM

Credits (min)

1.5

Credits (max)

1.5

Credit unit

credits

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

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. Credits 2 Babson College Graduate Catalog 2026-2027

Subject

QTM

Credits (min)

2

Credits (max)

2

Credit unit

Credits

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Decision Making in Practice 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/

Subject

QTM

Credits (min)

3

Credits (max)

3

Credit unit

credits

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Subject

QTM

Credits (min)

3

Credits (max)

3

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

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 Credits 3

Subject

QTM

Credits (min)

1.5

Credits (max)

3

Credit unit

Credits

Type

course

Edition

2026-2027-graduate

Source

catalogs.babson.edu

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. Credits 2 201

Subject

QTM

Credits (min)

2

Credits (max)

2

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

Recommended Courses for Students Interested in Real Estate Please note that taking these courses is not sufficient to satisfy the finance concentration. The following courses are for students interested in a career in real estate. For these students, we suggest taking the following courses: Course Code Title Credits

Subject

QTM

Credits (min)

1.5

Credits (max)

1.5

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

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. Credits 3

Subject

QTM

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

2026-2027

Source

catalogs.babson.edu

(Formerly Introduction to Data Science and Business

Subject

QTM

Credits (min)

1.5

Credits (max)

1.5

Type

course

Edition

2026-2027

Source

catalogs.babson.edu