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 or AQM2000

QTM6300 Machine Learning for Business
(Formerly Data Exploration and Analytics)
3 Blended CreditsThis 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?

QTM7571 Introduction to Machine Learning Methods for Business
(Formerly Business Intelligence, Analytics & Visualization)
3 CreditsThis course introduces statistical and machine learning methods for business intelligence. Given the ease of data collection and storage, extracting meaningful information from data has become an essential trait for competitiveness, for companies large and small. In this course, you will learn a variety of statistical and machine learning methods that companies use to turn data into insights. You will get hands-on experience with the implementation of statistical and machine learning methods, from data pre-processing to generating predictions, and evaluating model accuracy. Your learnings will be in practical contexts, such as:

- Predicting prices of homes as provided by the leading real estate database company realtor.com
- Predicting the statuses of the loans for the peer-to-peer loan lending company LendingClub
- Building a recommendation engine for the music website Last.fm

The various methods covered in this course will be implemented using the RStudio coding language. No prior knowledge of RStudio is required.

Prerequisites: QTM 7200 OR QTM7800

NST1060 Oceanography
4 CreditsOver 70% of the globe is covered by ocean. Marine systems are a nexus of life - crucial sources of protein for human populations, reservoirs of minerals, and regulators of the global climate. However, human populations have increased demand for ocean resources in greater numbers than is ecologically sustainable. In addition, the ocean serves as a dumping ground for many types of waste, resulting in waters degraded by pollution. The objective of this course is to give you a basic understanding of the physical, biological, and chemical processes driving ocean fundamentals. In addition, we will examine how human demand on marine resources impacts ocean communities.

This course will stress the importance of the scientific method - both in principle and in practice. Extensive discussion of human environmental impacts on the ocean (e.g., climate change, marine pollution, overfishing) will enhance perspectives of self-awareness and ethical decision-making related to social, economic and environmental responsibility and sustainability (SEERS). Critical analysis is emphasized in class discussions, exam questions, lab reports, written assignments, and the group project. Assignments facilitate development of logical communication skills, appropriate use of graphs and tables, and organizing, synthesizing, evaluating and interpreting scientific information. Through lab and group activities, this course fosters team work and ability to work with others. International and multicultural perspectives are integral to the course, since the oceans influence on human populations is global, both directly on the coasts, and indirectly away from the coasts (via weather, climate, and seafood production).

Prerequisites: None

QTM3620 Optimization Methods and Applications
(Formerly Operations Research)
4 Advanced Liberal Arts Credits

This course provides an introduction to optimization techniques for decision making with spreadsheet implementation. Topics covered include: linear programming, sensitivity analysis, networks, integer programming, nonlinear programming, and multiple objective optimization. Models discussed span different business disciplines including finance, accounting, marketing, human resources, economics, operations, and project management. Throughout the course, learning is reinforced via hands-on computer experience using problems and cases.

Prerequisites: AQM 2000 or (QTM 1010 and QTM2000)

QTM9510 Optimization Methods and Applications

1.5 Credits

This is a hands-on course in quantitative business modeling designed to give you a practical approach to the main mathematical techniques necessary to make better business decisions. 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 modeling, and on best practices for creating optimization models.

NST1080 Paradigms of Scientific Invest
4 Foundations Liberal Arts Credits

A multidisciplinary examination of the principles of scientific research and routes to discovery with examples from the history of the subject from its Greek beginnings to modern times. The course will provide insight into the sources, motivations, and methods of approach utilized by the developers of modern science. Topics from biology, physics, and engineering will be used to discover how we unravel the mysteries of the natural world and address the question of how do we know what we know is true by critically examining how the science community has resolved conflicting interpretations of the natural world and analyzing the consequent paradigm shifts from previously accepted theories. These concepts will be applied to addressing societal challenges in developing a national science policy, why things go wrong and mitigating man-made disasters. Finally, the real-world utility of these concepts is applied to applications within an entrepreneurship context in terms of evaluating and managing technology ventures.

Prerequisites: None

AQM2000 Predictive Business Analytics

4 Foundation Liberal Arts Credits

This course is only open to students who started Fall 2021 or after

This course introduces students to the foundational ideas of modern data science through a hands-on implementation in modern statistical software. Students will encounter key conceptual ideas like the importance of holdout data, the dangers of overfitting, and the most common performance indicators for various model types through a tour of popular and practical predictive analytics algorithms: linear regression, k-nearest neighbors, logistic regression, classification and regression trees, naive Bayes', and others. In addition to these supervised learning models, students will investigate unsupervised learning models like association rules and clustering, which are designed to uncover structure in data rather than predict a particular target. Throughout the course, students will practice communicating the results of their analyses to a variety of stakeholders.

Prerequisites: AQM1000

QTM3675 Probability for Risk Management

4 CreditsThe 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 for 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.

Prerequisites: QTM1010 or AQM 2000

QTM2623 Programming with R for Business Analytics
4 CreditsThis 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