Course Listings

Undergraduate

NST2070 - ASTROBIOLOGY AND THE EMERGENCE OF COMPLEX SYSTEMS

ASTROBIOLOGY AND THE EMERGENCE OF COMPLEX SYSTEMS

NST2070 ASTROBIOLOGY AND THE EMERGENCE OF COMPLEX SYSTEMS
4 Intermediate Liberal Arts Credits


The prospects for simple and intelligent life beyond earth are discussed in terms of planetary science, molecular biology, complexity theory, evolution and thermodynamics. Discussions will focus on the processes leading to the emergence of complex systems as well as the biological and physical interdependencies of life and the environment.

Prerequisites: NST10XX

4 credits

NST2080 - SOCIO ECOLOGICAL URBAN SYSTEMS

SOCIO ECOLOGICAL URBAN SYSTEMS

HSS2080 SOCIO-ECOLOGICAL URBAN SYSTEMS
4 Credits


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 begin by discovering the nature within cities. Many of the vital ecosystem services for which human life depends are derived from under-appreciated urban habitats. We will also investigate the history, human demography trends and socio-economic patterns within cities in various parts of the world, including the land, water, and energy resources cities consume as well as air, water, and solid waste pollution produced and distributed widely. We will discuss the limitations and problems within much of the current built environment, but also explore new sustainable and inclusive urban planning strategies that include innovative architectural design and green technologies. 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: RHT1000 AND RHT1001 AND AHS1000 AND NST10&&

4 credits

NST2085 - SOCIO-ECOLOGICAL PRAIRIE SYSTEMS

SOCIO-ECOLOGICAL PRAIRIE SYSTEMS

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

4 credits

NST2090 - SOCIO-ECOLOGICAL SYSTEMS:FEEDING THE MODERN UNITED STATES

SOCIO-ECOLOGICAL SYSTEMS:FEEDING THE MODERN UNITED STATES

NST2090/HSS2090 SOCIO-ECOLOGICAL SYSTEMS: FEEDING THE MODERN UNITED STATES
4 Intermediate Liberal Arts Credits


The sustainability of the global food system hinges on the full scope of the system's environmental resilience and safety. This course will be co-taught by a U.S. historian and a biologist and it focuses on the history, science, and future sustainability of the food system in the United States and across the globe. Students will study food security and food deserts, the origins of our plant and animal food products, and the labor required to bring food to our tables. They will learn about the social and environmental stressors across the entirety of the food system - from the use of the world's resources and the impact of climate change, to the communities nearby to where the food is grown, raised, processed or sold; they will study the health and safety of the agricultural and food service labor force, comprised first of enslaved people and later of im/migrant workers, many of whom lack official documentation. This interdisciplinary course on sustainability is designed to teach students about the social, historical, and environmental dimensions of a sustainable food system.

Prerequisites: RHT1000 and RHT1001 and NST10%%

4 credits

QTM1000 - QM FOR BUSINESS ANALYTICS I

QM FOR BUSINESS ANALYTICS I

QTM1000 QUANTITATIVE METHODS FOR BUSINESS ANALYTISC 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

4 credits

QTM1010 - QM FOR BUSINESS ANALYTICS II

QM FOR BUSINESS ANALYTICS II

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

4 credits

QTM2000 - CASE STUDIES IN BUSINESS ANALYTICS

CASE STUDIES IN BUSINESS ANALYTICS

QTM2000 CASE STUDIES IN BUSINESS ANALYTICS
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, 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.

Prerequisites: QTM1010 (or QTM2420)

4 credits

QTM2600 - LINEAR ALGEBRA

LINEAR ALGEBRA

QTM2600 LINEAR ALGEBRA

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.

Prerequisites: QTM1010

4 credits

QTM2601 - DISCRETE MATH

DISCRETE MATH

QTM2601 APPLICATIONS OF DISCRETE MATH
4 Advanced Liberal Arts Credits


Discrete Mathematics is used whenever objects are counted, when relationships between finite sets are studied, and when processes involving a finite number of steps are analyzed. The kind of problems solved may include: How many ways are there to choose a valid password on a computer network? What is the shortest path between two cities using a transportation system? How can a circuit be designed that adds integers? You will learn about the discrete structures and techniques found in Mathematical Logic, Combinatorics, Graph Theory, and Boolean Algebra that are needed to understand and solve these and other problems. You will develop mathematical maturity and problem solving skills by studying models in such diverse areas as Computer Science, Communications Networks, Business, Engineering, Chemistry, and Biology.


Prerequisites: QTM1000 or equivalent (such as QTM1300, QTM1301, or QTM2300 from the old curriculum)

This course is typically offered every 3rd semester

4 credits

QTM2622 - SPORTS APPLICATION OF MATHEMATICS

SPORTS APPLICATION OF MATHEMATICS

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

4 credits

QTM2623 - PROGRAMMING WITH R FOR BUSINESS ANALYTIC

PROGRAMMING WITH R FOR BUSINESS ANALYTIC

QTM2623 PROGRAMMING WITH R FOR BUSINESS ANALYTICS
4 Credits


This course provides experience in developing, testing, and implementing business analytics software using the R language. R has become the leading tool for analytics software design, statistical computing, and graphics. The language is greatly enhanced by numerous open-source contributed packages and textbooks submitted by users, and it is used almost exclusively in most of the leading-edge analytics applications, such as statistical analysis and data mining. No prior programming experience is assumed. Students will become proficient in programming in the R language with datasets of all kinds with an emphasis on statistical exploration, data mining, graphics, and advanced programming concepts. The course will be case-oriented. The intent is to further enhance the learning experience from other analytics courses, such as QTM1010 and QTM2000.

Prerequisites: QTM1010 and QTM2000 or permission from the instructor

4 credits

QTM3605 - QUANT ANALYSIS OF STRUCTURAL INJUSTICE

QUANT ANALYSIS OF STRUCTURAL INJUSTICE

QTM3605 QUANITATIVE ANALYSIS OF STRUCTURAL INJUSTICE
4 Advanced Liberal Arts Credits

Quantitative Analysis of Social Injustice provides a survey of current quantitative methods for analyzing structural social and economic injustice/disparities. Using philosophies and interdisciplinary methods popular across sociology, economics, computer science, psychology, and applied statistics, we follow topical examples from health, housing, education, workplace, and management topics to document gender, racial, socioeconomic, education, and other disparities.
The course is centered around data. We will use raw real-life large datasets with all its strengths and limitations. We begin with (a) an understanding of the taxonomy of applied statistics and the appropriate research questions they answer, and (b) have discussions around the treatment and philosophies of major data issues such as how missing data and covariates affect your analysis, data ethics, and selection bias. Afterwards, we learn to document and analyze disparities through a wide range of data types - spatial, panel, (and spatial x panel!), imagery, experimental, and observational - each with nuances that affect our analysis and interpretation. Our discussions throughout the semester will largely center around biases resulting from data, algorithms, and selection and how one might alleviate such biases. Major topics will include data visualization with spatial and time data, issues with associated prediction models, and a foundation in causal inference.
Students will be expected to be fast learners of R and QGIS, both of which are open-source. The major deliverables will be a group project that includes the development of data visualizations, a paper documenting and quantitatively analyzing a hypothesized disparity, and a presentation on said topic. Students must be comfortable with dealing with big data sets and R (at the QTM 2000 level) at a minimum.

Prerequisites: QTM2000

4 credits

QTM3610 - APPLIED MULTIVARIATE STATISTICS

APPLIED MULTIVARIATE STATISTICS

QTM3610 APPLIED MULTIVARIATE STATISTICS

(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

This course is typically offered in the following semesters: Spring


Prerequisites: QTM1010

4 credits

QTM3615 - TIME SERIES AND FORECASTING

TIME SERIES AND FORECASTING

QTM3615 TIME SERIES AND FORECASTING
4 Advanced Liberal Arts Credits


This course is about the analysis of time series data in the context of various decision-making situations pertaining to areas such as healthcare, banking, and engineering. The objectives of the course are to: provide practical experience when working with time series data to predict future trends; to provide a framework for comparing alternative models in terms of predictive accuracy; to cultivate an appreciation of various types of times series modeling approaches; to provide exposure and experience in using statistical software to build time series models; and to develop skills at communicating statistical results effectively in a managerial context. The software used throughout the course will be Minitab and R/RStudio. Effective teamwork and professional presentation of analyses and recommendations will be required during this course.

Prerequisites: QTM1010 or permission from instructor

4 credits

QTM3620 - OPTIMIZATION METHODS AND APPLICATIONS

OPTIMIZATION METHODS AND APPLICATIONS

QTM3620 OPTIMIZATION METHODS AND APPLICATIONS
(FORMERLY OPERATIONS RESEARCH)
4 Advanced Liberal Arts Credits

The focus of this course is upon the development, solution, analysis, and implementation of optimization models and their applications within business, government, education, and sports. The topical emphasis is primarily upon mathematical programming, optimization of flows across networks, and the interrelationships between these two classes of methodologies. The learning process is oriented toward problem solving. There typically is a problem statement leading into each topic followed by the construction of a mathematical model, solution of the model, and the resulting analysis. Many of these illustrative examples are supplemented with the discussion of a journal article relating how a larger-than-classroom scaled model has been successfully implemented in practice.

Prerequisites: QTM1010

4 credits