QTM1000 Quantitative Methods for Business Analytics I
4 CreditsThe course introduces the necessary core quantitative methods that are prerequisites to follow-on courses in QTM and in Babson's integrated core business offerings. Statistical software and the use of spreadsheets are integrated throughout so that students better comprehend the importance of using modern technological tools for effective model building and decision-making. About two thirds of the course is data-oriented, exposing students to basic statistical methods, their conceptual underpinning, such as variability and uncertainty, and their use in the real world. Topics include data collection, descriptive statistics, elementary probability rules and distributions, sampling distributions, and basic inference. The last third of the course is dedicated to selected non-statistical quantitative techniques applied to business models. Topics include curve fitting, differential calculus applications to non-linear optimization, and introduction to the time value of money.

Prerequisites: None

QTM1010 Quantitative Methods for Business Analytics II
4 CreditsThis course explores decision-making problems in a managerial context using algebraic, spreadsheet, graphical, and statistical models. The focus is on understanding basic mathematical and modeling principles through the analysis of real data. The course emphasizes communicating in-context interpretations of the results of analysis in written, visual, and oral form. A foundation in introductory statistics and use of spreadsheets is essential because these concepts are extended and reinforced throughout the course. Topics include introductions to linear regression, time series analysis, linear programming, decision analysis and simulation. It emphasizes the use of appropriate software and the latest technological methods for accessing and analyzing data.

Prerequisites: QTM1000 or AQM1000

QTM3605 Quantitative Analysis of Structural Injustice
4 Advanced Liberal Arts Credits

This course provides a survey of current quantitative methods for analyzing structural disparities. Using philosophies from interdisciplinary fields, we follow examples from education, housing, and other topics to document the direction and size of social and economic disparities. The course begins with a discussion on the philosophies of major data issues. We then learn to analyze disparities using a wide range of data types - spatial, panel, experimental, and observational - through the use of raw, real-world data sets. Discussions will center on biases resulting from data, models, and algorithms. The course uses R and QGIS. Prior to enrolling, students should have a foundation in regression analysis

Prerequisites: AQM 2000 OR QTM 2000

QTM3635 Quantitative Methods for Machine Learning
4 Advanced Liberal Arts CreditsThe ease of data collection coupled with plummeting data storage costs over the last decades have resulted in massive amounts of data that many business organizations have at their fingertips. Effective analysis of those data followed by sound decision-making is what makes a company an analytical competitor. This course is dedicated to learning and applying advanced quantitative tools for solving complex machine learning problems. The course will build on analytical tools learned during AQM 2000 (Predictive Business Analytics) course, introducing modern advanced tools ranging from random forests to support vector machines and artificial neural networks. Each topic covered in this course will be discussed in the context of wide-ranging real-world applications such as email spam prediction; handwritten digit recognition; topic modeling/text mining; etc. The implementation of the introduced topics will be carried out in R/RStudio.

Prerequisites: QTM2000 or AQM2000

OIM3536 Scaling Lean Ventures
4 Advanced Management Credits

Students who took this as MOB3536 cannot take this course

How do you enable an organization to overcome the constraints and risks posed by the nascent & uncertain operating environment found in an entrepreneurial venture? Scaling Lean Ventures is a capstone course for Operations concentrators and elective course for others targeted to 3rd and 4th year undergraduate students with an interest in strategic operations in small to medium sized organizations.

The approach to the course is driven by Lean Principles of Management including "learn by doing". The well-studied Toyota Production System serves us as the root file for many of these principles. Students will be assigned to a high priority project with an organization and will be expected to conceive & implement Lean Start-up principles to relieve the organization of a deeply embedded operating constraint on growth. This is not a consulting experience, but a learn-by-doing partnership for fourteen weeks. The students will be expected to be on site with the partner organizations regularly to make implementation progress.

In addition to their on-site time, the course will have an in-class component. During each in-class session, the students will be exposed to a new TPS concept and discuss how to implement it at their project. The students will also provide and receive feedback from their peers, instructors, and guest lecturers to gain insights on their implementation attempts to-date, thus better understanding their assigned problem and charting a path forward to success.

The partner organizations are from a wide variety of industries, including technology, consumer products, food, legal services, and socially-oriented manufacturing and service companies.

Prerequisites: FME and SME ; Juniors and Seniors status

NST1090: Science of Sport

4 NST1 Credits

From the first recorded event at the ancient Olympic Games in 760 BC to the present, humans have long been captivated by sports. Humans are competitive by nature, and while sports are thrilling to both watch and play, sports are also a powerful demonstration of science. Every sport from soccer to cricket, baseball to softball, football, swimming and track and field all involve a complex symphony of science, technology, engineering, and math. This course will explore the science that underlies sport, specifically incorporating the traditional scientific disciplines of anatomy and physiology, physics, psychology, biomechanics and math. We will explore the systems of the human body that make it possible for a pitcher to throw a baseball at 100 mph, a marathoner to run 26.2 miles in just under 2 hours or a figure skater to land a quadruple axle. We will explore how science contributes to the limits of human speed, strength and endurance. We have accumulated considerable amount of information that contributes to our understanding of health, the human body and human performance in relation to sport and exercise. We will explore a range of topics from the effects of exercise on heart rate, oxygen consumption, muscle function and fatigue, joint mechanics, metabolism and concussion. Importantly, we will put the concepts we learn in class into practice in the lab and on the field to test them and collect and use data to critically analyze athletic performance and the underlying scientific principles that define it.

Prerequires: None

SCN3640 Science and Innovation
4 Advanced Liberal Arts CreditsAn examination of the concepts, principles and policies related to research and development activities with examples from the history of the subject from its Greek beginnings to modern times. Successful and failed R&D projects from multiple disciplines will be explored as a driving force for innovation. The complex relationships that the scientific and engineering enterprises have to the innovation process will be examined with respect society, industry, and political motivations.

Prerequisites: NST10%%

OIM3519 Simulation Modeling in Operations Management
(Formerly MOB3519)
4 Advanced Management Credits

Students who took this as MOB3519 cannot register for this course

This course exposes students to simulation modeling techniques of various operational challenges. Simulations imitate realistic business environment and enable participants to explore the impact of their operational decisions. Decision making in simulation models enables decision makers to evaluate alternative decisions, before the changes are implemented in actual operations and prevents potentially costly mistakes. The real value of simulations is actually revealed after the decision is made, which is the critical component of this course.

In this course, students will first identify a problem, collect or analyze the data, formulate and validate the simulation model, and finally simulate alternative outcomes to recommend the appropriate decision. Once the decision is implemented in the model, the future condition of the business environment is randomly changed, and impact of the decision is analyzed and re-assessed. The analysis will use simulation model to evaluate and predict impact of the decision making on profit, society and environment, combined with regulatory and ethical considerations.

The course is composed of four independent simulation building modules, and a final project. Students will work in groups and individually to create four guided simulation models. Final project is a semester-long activity where students will have the opportunity to build simulation model in the field of their interest or chose from a list of topics proposed by Babson community. During the semester, students will spend approximately equal amount of time on advanced data analytics and operations management topics. The underlying principle of the course is to learn by experience, learn practical model building skills, and emphasis on the analysis of the simulation results, and the impact of various decision alternatives.

Prerequisites: (QTM1000 or AQM1000) and (SME2002 or OIM2001)

OIM3504 Social Innovation Design Studio: Innovating for the Future of Business and Society

4 Advanced Management Credits

This experiential studio course offers students a unique opportunity to integrate entrepreneurial leadership with social design and learn by doing as they create and implement solutions to some of the world's pressing challenges - in partnership with innovative client sponsors. Students work collaboratively in teams supported by faculty, mentors, lecturers and their own self-initiated research. Three sections guide learners through the process of self-discovery, understanding the landscape and potential of social design in business, and hands-on application of the process to a real-world challenge. The mindsets, skillsets and processes mastered will serve students in creating the future they want throughout their lives.

Prerequisites: (FME 1000 and FME1001) or (EPS1000 and MOB1010)

NST2085 Socio-Ecological Prairie Systems
4 Intermediate Liberal Arts Credits

**NST2085 AND LVA2085 are two separate courses and students are held responsible to register for the course that they would like to receive credit for.**

Socio-ecological systems (SES) are linked systems of people with nature, emphasizing that humans must be seen as a part of, not apart from nature. This course will explore the nature of the prairie, both as a socio-ecological system and as a subject for exploration and contemplation for visual and literary artists. Before the Euro-American (un)settlement of the North American middle west-about 150 years ago-the tallgrass prairie extended for approximately 145 million acres from Canada to Texas. Now, after several generations of overgrazing, plowing, and the intensities of agricultural production, there remains less than 5% of what some scientists call our most endangered ecosystem. We will investigate how prairies function, study the causes and consequences of related ecological patterns and processes in prairie landscapes, describe both the loss and restoration of prairie environments, and appreciate the potential for the role of the arts in naming, analyzing, and imagining solutions relating to the examination and repair of prairie systems. Studying SES allows for the development of important skills for future leaders, such as approaches for incorporating uncertainty, nonlinearity, and self-reorganization from instability. Transdisciplinary approaches will be employed to address complex temporal, spatial, and organizational scales to investigate real world challenges.

Prerequisites: NST1 and FCI1000 and WRT1001