OIM3565 Agile Experimentation
(Formerly MIS3565)
4 Advanced Management Credits

**Students who took this as MIS3565 cannot take this course**

Business leaders and entrepreneurs should be Agile digital experimenters, capable of innovating by combining available technologies and services into digital experiences. In this course, students will learn about Agile digital entrepreneurship and follow an Agile methodology to conceive and create an internet of things (IoT) solution with a clear value proposition.


Agile Experimentation (AgileEx) is an experiential course in which teams of students use agile methodologies to design and prototype viable innovations combining hardware and software elements. The course involves:
- Practicing Agile project management methodologies and software, and learning how to scale Agile environments from small startups to large organizations
- Designing and building IoT devices with sensors and actuators, and programming hardware (i.e., Arduino microcontrollers)
- Designing digital interfaces (e.g., interactions, app mockups, information flows) with software tools
- Running experiments and surveying customers to test hypotheses and improve the prototype
- Building an innovation that is a feasible and responsible market solution
- Presenting your work in a final pitch that showcases your prototype and its market viability
- Learning about emerging technologies


The course aims to train business graduates who are confident life-long learners of technology, can work in Agile environments, and can participate in the development of innovative and responsible technological solutions.

Prerequisites: SME2012

  • Program: Undergraduate
  • Division: Operations and Information Management
  • Level: Advanced Elective (UGrad),Advanced Management (UGrad)
  • Course Number: OIM3565
  • Number of Credits: 4

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

OIM3580 Artificial Intelligence in Business
(Formerly MIS3580)
4 Advanced Management Credits

**Students who took this as MIS3580 cannot register for this course**

This elective is intended to introduce you to a variety of different types of artificial intelligence and to many of the issues involved in their business application. We will cover a variety of AI tools, from machine learning to natural language processing to "deep learning." We will learn about both the functions performed by these technologies and the business issues they generate - including the roles to be performed by humans in organizations of the future.
Some introductory material is provided by online videos on AI in general. We will have several external experts as guest speakers during sessions. No programming or detailed technology background is required, although you should be interested in new technology and will need to study materials about how AI works.


The objective is to equip you to be a manager or professional who makes use of this technology, not a developer of it-or a translator of business requirements to professional data scientists. The course is also intended to encourage some students to go on for more technical training in AI. Specific learning objectives are listed for each session.

Prerequisites: SME2012

  • Program: Undergraduate
  • Division: Operations and Information Management
  • Level: Advanced Elective (UGrad),Advanced Management (UGrad)
  • Course Number: OIM3580
  • 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

OIM3560 Blockchain and Cryptocurrencies
(Formerly MIS3560 The Blockchain: Bitcoin, Smart Contracts, and Other Applications)
4 Advanced Management Credits

**Students who took this as MIS3560 cannot take this course**

MIS3505 and MIS3605 significantly overlap topics covered in MIS3560. Students who take MIS3505 OR MIS3605 cannot take MIS3560.

This course is about an exciting new technology called the blockchain. The blockchain is the technology behind bitcoin and other forms of digital cash. In this course, you will learn about the algorithms and protocols that enable blockchain creation, the theory behind and the potential of cryptocurrencies, how blockchains are used to enforce smart contracts, and how many other blockchain applications work.

Prerequisites: None

  • Program: Undergraduate
  • Division: Operations and Information Management
  • Level: Advanced Elective (UGrad),Advanced Management (UGrad)
  • Course Number: OIM3560
  • Number of Credits: 4

OIM3545 Business Intelligence and Data Analytics
4 Credits

**Students who took this as MIS3545 cannot register for this course**

This course is about how organizations, and their employees can successfully collect, evaluate and apply information to become better decision makers. It starts with basic concepts regarding business data needs and ends with hands-on experience using Business Intelligence (BI) tools. It takes a variety of experts to start and run a business - financial, operational, marketing, accounting, human relations, managerial, etc. Each knowledge base requires up-to-date information to plot strategy or keep it on track. Our ability to capture large volumes of data often outstrips our ability to evaluate and apply the data as management information. These are the challenges we will address in this course so that you can become an intelligent gatherer and user of data in your chosen field.

Prerequisites: SME2012 or OIM2000

  • Program: Undergraduate
  • Division: Operations and Information Management
  • Level: Advanced Elective (UGrad),Advanced Management (UGrad)
  • Course Number: OIM3545
  • Number of Credits: 4

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