QTM6600 Analytics for Decision-Makers
1.5 Credits (MSAEL core)
Data exploration and data-driven decision making are integral in identifying and validating business opportunities. Depending on the nature of the problem and the institutional 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, we encounter selection of these techniques and develop our ability to formulate analytics problems in ambiguous contexts, quantify performance of various solutions, and articulate the key results of our analysis to a non-technical audience, including using visualization methods.

Prerequisites: MOB6600 and EPS6600

  • Program: Graduate
  • Division: Mathematics Analytics Science and Technology
  • Level: MSAEL (Grad)
  • Course Number: QTM6600
  • Number of Credits: 1.5

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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Advanced Elective (UGrad),Advanced Liberal Arts (UGrad)
  • Course Number: QTM3610
  • Number of Credits: 4

NST1011: Astrobiology

4 foundation liberal arts credits


Introduction to the new science of astrobiology, study of the origin and evolution of life on Earth, and the search for microbial and intelligent life elsewhere in the Universe. Study of the information necessary to make estimates of the probability of extraterrestrial life, what characteristics it might have and how we might expect to communicate with it if it exists.

Prerequisites: None

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Foundation Liberal Arts (UGrad)
  • Course Number: NST1011
  • Number of Credits: 4

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

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Intermediate Liberal Arts (UGrad)
  • Course Number: NST2070
  • Number of Credits: 4

NST1010 Astronomy
4 Credits
The evolution and structure of the universe are explained using underlying basic physical principles along with the historical development of our present understanding. We will explore the instruments and data collection techniques used by astronomers and learn how they can be applied to solve problems in other disciplines.

Prerequisites: None

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Foundation Liberal Arts (UGrad)
  • Course Number: NST1010
  • Number of Credits: 4

SCN3600: Biomimicry Applying Natures Design for Business

4 advanced liberal arts credits

In this course we will investigate the tools and principles of biomimicry, which seek to sustainably solve current challenges by taking inspiration from how nature solves these same problems. Nature provides us with an incredible amount of research and development for effective problem-solving methodologies with the ultimate test for organisms being survival of the fittest. For the past 3.8 billion years, life has evolved strategies that are constantly integrated and optimized to create conditions conducive for life to continue. Successful examples of biomimicry include something as simple as Velcro (imitating burrs that stick to sheep) to cutting edge advancements like a bionic leaf producing hydrogen fuel from sunlight (imitating photosynthesis) and medical grade internal adhesives (imitating how mussels adhere underwater).

In this course we will begin by exploring design principles in biology, chemistry and physics and applying them to specific technological design strategies by asking questions like "How does nature make color?" and "How does nature water-proof something?" Then we will explore ecological design principles to understand how we can use nature's strategies of interconnectedness and cycling as a way to solve problems in businesses and organizations and move toward the circular economy. This course will emphasize the development of skills in critical thinking, synthesis of information, scientific literacy, hand-on exercises, and current topical issues in biomimicry.

Prerequisites: NST1XXX

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Advanced Elective (UGrad),Advanced Liberal Arts (UGrad)
  • Course Number: SCN3600
  • Number of Credits: 4

QTM7800 Business Analytics

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.

  • Program: Graduate
  • Division: Mathematics Analytics Science and Technology
  • Course Number: QTM7800
  • Number of Credits: 2

NST2030 Case Studies in Biomedical Science
4 Intermediate Liberal Arts Credits
An in-depth study of the process for developing and commercializing biomedical technologies. The course explores understanding the role of translational research as a foundation for diagnostic and therapeutic products. The mechanisms underlying selected biomedical devices will also be described.

Prerequisites: NST10%

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Intermediate Liberal Arts (UGrad)
  • Course Number: NST2030
  • Number of Credits: 4

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)

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Intermediate Liberal Arts (UGrad)
  • Course Number: QTM2000
  • Number of Credits: 4

NST2060 Case Studies in Drug Development Systems
4 Intermediate Liberal Arts Credits
Drug development is a dynamic, multidisciplinary industry that encompasses the discovery, scientific, clinical and economic assessment of a new compound's safety, efficacy, potential side effects and requires the collaboration and innovation of scientists, chemists, clinicians, statisticians, lawmakers, business leaders and entrepreneurs. Over the last 30 years, the idealized goal of drug discovery has been to identify a specific chemical substance that is highly specific for a single molecular target and arrests or stems the advancement of disease. Although the goal is highly specific and the process seems linear, there are many contributing, and often unforeseen factors that inform drug design, the drug development pipeline and the eventual success or failure of a given drug candidate. In this course, we will take a systems approach to identify and describe all of the contributing elements of identifying, characterizing and bringing a drug to market, to define the physiological, biological, economic and regulatory systems that characterize the process and to outline the social, economic and environmental considerations of a sustainable and productive model for drug development.

Prerequisites: NST10XX (NST 1)

  • Program: Undergraduate
  • Division: Mathematics Analytics Science and Technology
  • Level: Intermediate Liberal Arts (UGrad)
  • Course Number: NST2060
  • Number of Credits: 4