QTM1000 Quantitative Methods for Business Analytics I
4 Credits
The 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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Foundation Liberal Arts (UGrad)
  • Course Number: QTM1000
  • Number of Credits: 4

QTM1010 Quantitative Methods for Business Analytics II
4 Credits
This 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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Foundation Liberal Arts (UGrad)
  • Course Number: QTM1010
  • Number of Credits: 4

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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Advanced Elective (UGrad),Advanced Liberal Arts (UGrad)
  • Course Number: QTM3605
  • Number of Credits: 4

QTM3635 Quantitative Methods for Machine Learning
4 Advanced Liberal Arts Credits
The 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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Advanced Elective (UGrad),Advanced Liberal Arts (UGrad)
  • Course Number: QTM3635
  • Number of Credits: 4

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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Foundation Liberal Arts (UGrad)
  • Course Number: NST1090
  • Number of Credits: 4

SCN3640 Science and Innovation
4 Advanced Liberal Arts Credits
An 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%%

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Advanced Elective (UGrad),Advanced Liberal Arts (UGrad)
  • Course Number: SCN3640
  • Number of Credits: 4

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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Intermediate Liberal Arts (UGrad)
  • Course Number: NST2085
  • Number of Credits: 4

SES2000 Socio-Ecological Systems

4 Credits

Special Topic Descriptions: https://babson.sharepoint.com/:w:/s/SESTeachingFaculty/EWYrFfzN_uZDhS_m8w-TmAcBP35aZg1XkbeRQAjsQ7HapQ?e=pc4LSt&CID=4F7F0C1A-ED6A-4E61-9AB9-A0476C8E2B98

This co-taught course will integrate across the social sciences and ecological sciences to focus on socio-ecological systems(SES), which are linked systems of people with nature, emphasizing that humans must be seen as a part of, not apart from nature. These connected systems are complex, adaptive, and are governed by feedbacks within and between social and bio-physical processes. Studying SES allows for the development of important skills desperately needed for future business leaders, such as approaches for incorporating uncertainty, nonlinearity, and self-reorganization from instability. Students will be taught systems thinking and how to identify and develop an understanding of the interdependent and interrelated structures and feedbacks of dynamic systems. Transdisciplinary approaches will be employed to address complex temporal, spatial, and organizational scales to investigate real world challenges. Beyond just social impact businesses or corporate social responsibility, teaching system dynamics for sustainability allows students to develop as system change leaders.

This course will directly address the new integrated sustainability theme and will provide a strong background for all of our students in integrative systems thinking, ecological integrity, and structural injustice. Students will be introduced to the UN Sustainable Development Goals, Planetary Boundaries Framework, resilience strategies, and leverage points for systems-based change for sustainability. Students will also learn concept mapping techniques as a way of visually representing complex systems, their relationships, and indirect connections and feedback effects. The skills learned can then be expanded and built from in subsequent elective courses. There are multiple content versions of this course including Climate Systems, Food Systems, Natural Disaster and Resilience Systems, Prairie Systems, Urban Systems, and Water Systems that are offered across different semesters.

Prerequisites: NST 10XX and FCI 1000 and WRT 1001

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Intermediate Liberal Arts (UGrad)
  • Course Number: SES2000
  • Number of Credits: 4

QTM2622 Sports Applications of Mathematics
4 Advanced Liberal Arts Credits
Mathematicians and statisticians are playing an increasing role in shaping how athletic contests are played and how they are judged. This course examines some of the underlying quantitative principles that are routinely used. Students will apply some statistical techniques (expectations, probability and risk/reward judgments) and some that are deterministic (optimization, ranking and validation.) A variety of software packages will be used to demonstrate the many ways that a mathematical point of view can inform athletes, trainers, administrators and fans.

Prerequisites: QTM1010 or AQM2000

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Advanced Elective (UGrad),Advanced Liberal Arts (UGrad)
  • Course Number: QTM2622
  • Number of Credits: 4

NST2040 Case Studies in Sustainable Food Systems
4 Intermediate Liberal Arts Credits
What is food - where does it come from, how is it grown, what resources does it use, what's the difference between a GMO and an organic product, what do labels mean, is it sustainable? This course looks to take a scientific and systems based look at the food we eat and deeply examine all of the steps that occur between "farm to table". We need food to survive and food must be grown, cultivated, harvested, processed, and distributed so that we can benefit from it. These steps take place in different ways all across the globe, across the country, and among our neighbors. In this class, we'll look at what it means to be a sustainable food system, look at historical approaches that worked to meet/deviate from this goal, and look at how the future aims to feed a growing world with increasingly diminishing resources.

By the end of this course, you will recognize the importance of sustainable food systems and know the different areas that comprise this system. You will be able to distinguish between sustainable and non-sustainable food systems. Through this design, this course meets the college learning goals of Rhetoric, Quantitative and Information Analysis, Ethics and SEERS, and Critical and Integrative Thinking.

Prerequisites: NST10%%

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Intermediate Liberal Arts (UGrad)
  • Course Number: NST2040
  • Number of Credits: 4