Course Descriptions

We have provided a summary of the core Engineering Management courses below. You also have access to hundreds of courses across Cornell’s campus using your specialization elective credits. View Cornell’s course catalog.

Core Engineering Management Courses

ENMGT 5900: Project Management 

Fall and Spring, 4 credits

Core graduate course in project management for people who will manage technical or engineering projects. Focuses both on the “technical” tools of project management (e.g., methods for planning, scheduling, and control) and the “human” side (e.g., forming a project team, managing performance, resolving conflicts), with somewhat greater emphasis on the latter.

ENMGT 5910: Engineering Management Project

Fall and Spring, 4 credits 

As Engineering Managers, you need to embrace both technical and business skills to tackle complex, sociotechnical challenges, while staying on top of the current pace of technological change. In this Engineering Management project course, we are bridging from your coursework to your role as an engineering manager. To get there, you will practice the tools, themes, and techniques learned in your Engineering Management coursework through the scaffolding of a large project. In ENMGT 5910, you will work in teams to participate in a project in collaboration with an industry partner. You will perform an intensive evaluation of some mixture of the technological and management aspects of a major engineering project or system, conducted with a team of students. This project typically incorporates some combination of economic and financial analysis, integration of components into a large-scale system, or technology feasibility.

ENMGT 5920: Product Management

Spring, 3 credits

Product Management is one of the fastest growing careers in engineering and technology-based industries. In this course, you will learn the foundations of product management including (i) preparing for success as a product manager, (ii) identifying and targeting customer needs, (iii) prioritizing your project needs, and (iv) designing, developing, and deploying your product across the product life cycle.  Using skills developed through course lectures and discussions, you will complete a project where you will practice the sprint model utilized in most product teams. This course is for students interested in pursuing a career as a product manager in engineering or technology-based companies, learning about the product management competency, or working in a non-traditional tech setting to apply these skills on complex systems.

ENMGT 5930: Data Analytics

Fall, 4 credits 

Prerequisites: CEE 3040 or equivalent. 
Methods for managing data and transforming data into information. Modeling as a means to synthesize information into knowledge that can form the basis for decisions and actions. Application of statistical methods and optimization to managerial problems in project design, scheduling, operations, forecasting, and resource allocation.

ENMGT 5940: Economics and Finance for Engineering Management

Spring, 4 credits

An engineering case-based exploration of economic models and methods used in analysis, comparisons, and decision making by engineers and engineering teams. Emphasis will be placed not only on the important calculations, but also on understanding, communicating, and recording their findings, related assumptions, risks, external considerations, and situational awareness.

ENMGT 5960: Negotiations and Contracts for Engineering Managers

Fall, 3 credits

An exploration of negotiation types, skills, and tactics relevant to engineers and engineering managers, and a study in contract types, details, and clauses common to engineering fields. Studies will include human factors and behavior in negotiations, understanding and managing the end game, and legal terminology engineers should know.

ENMGT 5980: Decision Framing and Analytics 

Fall, 3 credits 

Prerequisite: introduction to probability and statistics course such as CEE 3040, ENGRD 2700, ILRST 2100, BTRY 3010, or AEM 2100. Enrollment is limited to seniors and graduate students; or permission of instructor. 
Framework to structure the way we think about decision situations that are complicated by uncertainty, complexity, and competing objectives. Specific decision analysis concepts and tools, such as decision trees, sensitivity analysis, value of information, and utility theory. Applications to all areas of engineering and life. Includes a group project to analyze a real-world decision.

ENMGT 5990: Contemporary Challenges for Engineering Managers

Fall, 3 credits 

This course will focus on major modern challenges faced by Engineering Managers, and how our responses are guided and confined by our value systems, external pressures, and available resources. The topics covered will be of a contemporary nature looking at the factors that have affected managers in the recent five years, and that will affect us in the next five to ten years. Key areas will include Climate Change, Sustainability, Diversity, Remote work forces, Technology Strategy, Data Privacy, Ethics in Global Engineering, and others. 

ENMGT 6020: Managing a Culture of Innovation

Fall, 3 credits 

Innovation is not just ideas, but getting ideas to measurable impact for your customers or employees. While the word ‘innovation’ is pervasive throughout engineering and business, developing and managing a culture of innovation has only been mastered by a few organizations. In fact, no company has remained on the Dow Jones Industrial Average since its inception. Why? Because implementing a culture of innovation is very difficult and is moored by a lack of understanding of proven innovation strategies, competencies, and tools. In this course, you will learn a systematic approach for developing and managing a culture of innovation. You will learn how to develop an innovation strategy to better meet your organization’s goals and customer needs. In addition, we will take time to dive into innovation competencies, such as design thinking, lean start-up, and making, along with learn several innovation tools including hackathons, open innovation strategies to deliver impact for your customers and organization.

ENMGT 6090: Professional and Leadership Development Seminar

Fall, 1 credit (not counted towards 30 credit program requirements)

In the Professional and Leadership Development Course, Engineering Management Master of Engineering students will identify the goals for their career and engage in several career development support services, such as networking, interviewing, resume and cover letter writing, and negotiation workshops. In addition, they will engage in a leadership development series to help them further build their engineering management and leadership competencies. Through a variety of tools, one-on-one coaching, workshops, events, and other resources, this course will help students develop and practice critical career management skills, along with build their confidence to find a career opportunity best suited to their interests and needs.

ENMGT 6091: Seminar: Project Management

Spring, 1 credit (not counted towards 30 credit program requirements)

Weekly seminar aimed at M.Eng. students, in particular those in the engineering management program. Weekly speaker will come from different engineering applications and discuss insights into project management.

ENMGT 6095- 102: Artificial Intelligence for Engineering Managers 

Spring, 1 credit

As AI continues to revolutionize various industries, it is imperative for engineering managers to acquire a deep understanding of AI concepts and be able to apply techniques to stay at the forefront of technological advancements.

This seminar will investigate the practice of artificial intelligence to solve technical problems, increase efficiency and productivity, and improve systems. Emphasis is placed on the practical applications of these concepts and the use of existing AI tools in engineering domains.

The course is a mix of theory and hands on practical work with a focus on assembling and using a modern AI toolchain to empower engineers and engineering managers. Throughout the course, students will engage in hands-on projects, working with industry-standard tools and frameworks. The goal is to equip engineers with the skills and knowledge needed to leverage AI effectively.

This course is designed for graduate-level students seeking to enhance their expertise in Artificial Intelligence. Tutorials and assignments will be in Python, and an introductory understanding of linear algebra is important. We will be using Colab, GitHub and Docker, so a basic knowledge of these tools will also be helpful.

Topics covered:

  • Cognition, consciousness and intelligence
  • Fundamentals of neural networks and large language models
  • Using large language models
  • Security and exploits
  • Improving performance (eg: retrieval augmented generation)
  • Ethics, bias and societal implications
  • Modern tools, including Langchain, vector databases, Autogen and Pytorch
  • Near term future developments

If there is time and interest, we may also select from the following topics: knowledge representation, representational capacity of transformer based language models, natural language processing, machine learning evaluation metrics, LSTMs, GANs, temporal fusion transformers, debugging AI toolchains.