Course
Curriculum

MEML@RICE

Core Curriculum

MEML@Rice students will complete a combination of required coursework (18 credit hours), elective courses (9 credits) and a Capstone project. The required engineering management courses (descriptions below) equip future engineering leaders with academic and management skills to succeed in a wide range of professional industries. Elective courses enable program participants to gain real-world experience in specializations including Data Science and Computer Science.

Need to brush up on technical programming or math skills? Check out our Online CS/DS Bridge Course below.

Required Courses: Engineering Manager Leadership (18 Credit Hrs)

RCEL 501: ENGINEERING MANAGEMENT & LEADERSHIP THEORY AND APPLICATION [3 CREDITS]

Technology-based innovation is the grand driver of economic progress, which hinges on strong technical leadership guiding engineering teams in mid-to-large corporate organizations and startup to small companies. By surveying and learning about the different type of EML approaches, this course outlines a framework for engineering professionals to progress from engineering manager to engineering executive (e.g., Vice President of Engineering, Chief Technology Officer). Practical methods from the engineering management literature that addresses technology-based innovation issues that have engineering management implications will be introduced. Seminal technology management principles, such as disruptive innovation, leaderless technology development, and digital platform strategy, found in companies ranging in size from start-up to large, will be examined.

RCEL 502: ENGINEERING PROJECT MANAGEMENT [3 CREDITS]

Engineering Project Management will provide instruction on the tools, techniques, and leadership characteristics required to successfully execute a project. The course will address the phases of project execution—initiating, planning, executing, monitoring and controlling, and closing. The course is designed to use a combination of video presentations, case studies, and project related exercises.

RCEL 503: ENGINEERING PRODUCT MANAGEMENT IN INDUSTRY 4.0 [3 CREDITS]

The fourth and latest industrial revolution, Industry 4.0, is comprised of intelligent automated machines and devices being developed by unconstrained manufacturing technologies (e.g., 3D printing), which can give them unprecedented sensing and communication capabilities. The internet of things (machines and sensors and the ‘big data’ they output) is creating new avenues for the remote collection of data from these new products. Engineering leaders will have a unique opportunity to guide engineering teams to create products that can leverage and evolve based on data from the supply chain to customer usage.

RCEL 504: ETHICAL-TECHNICAL LEADERSHIP [3 CREDITS]

Technology-based companies are powered by teams of engineers who create products and services that create value and competitive advantages for organizations that can turn into profits. However, the matrices of technical and user related decision paths that engineering leaders make to guide the team are not always constrained by ethics in a formal way. This course will help students understand the impact of ethics on engineering and technology in order to apply ethics concepts to decision making on issues that emerge in the workplace during one’s career.

RCEL 505: ENGINEERING ECONOMICS FOR LEADERS [3 CREDITS]

This course will explore economic analysis of capital expenditure decisions, financial mathematics, microeconomics, and decision-making under risk and uncertainty. Topics covered in this course include time value of money, analysis of alternatives using net present value and internal rate of return, depreciation, taxes, and inflation. Computational approaches, such as probabilistic design in engineering designs, which connect randomly varying design parameters to economic impact, will sometimes be considered based on course composition. Engineering ethics case studies that involve engineering economics will be explored as well.

RCEL 506: APPLIED STATISTICS AND DATA SCIENCE FOR ENGINEERING LEADERS [3 CREDITS]

Modern engineering leaders face myriad projects and problems that involve the generation, collection, analysis, visualization and interpretation of data, much of which is now known as big data (upwards of millions of observations) and/or high-dimensional (upwards of millions of variables). In turn, engineering leaders must be able to leverage the abundance of data to generate new knowledge and be proficient in data-driven decision making. This course will provide a foundation in statistics and data science with a view toward preparing engineering leaders to engage and direct teams in data based solutions to engineering problems.

Required Engineering Management Capstone Project (3 Credit Hrs)

RCEL 507: MASTER'S IN ENGINEERING MANAGEMENT AND LEADERSHIP CAPSTONE [3 CREDITS]

This course represents the capstone of the MEML program. It is a project-based and discussion-based course where students develop economically-sustainable, technological solutions to society’s most complex grand challenges using the methods and competencies taught in the MEML program. Students are expected to devise Industry 4.0 relevant solutions, with mechanisms for continuous learning and improving the solution from end-user data, while bounding all approaches with a demonstrable ethical-technical framework.

Elective Courses: Engineering Disciplinary Specialization (9 Credits)

Enhance your skill set by selecting one nine-credit specialization. Online program participants can currently choose from Computer Science or Data Science.

Computer Science

Computer Science courses include:

• COMP 614: Computer Programming for Data Science (3 credit hours) (recommended)
• COMP 643: Big Data (3 credit hours)
• COMP 665: Data Visualization (3 credit hours)
• Any other COMP course where prerequisites are met

Data Science

Recommended Data Science courses include:

• COMP 614: Computer Programming for Data Science (3 credit hours) (recommended)
• COMP 680: Statistics for Computing and Data Science (3 credit hours) (recommended)

Select one additional course from the following options:

• COMP 643: Big Data (3 credit hours)
• COMP 665: Data Visualization (3 credit hours)
• COMP 642: Machine Learning (3 credit hours)

Optional: Online CS/DS Bridge Course

Need to brush up on technical skills? The 6-week online bridge course is designed to equip you with the math and programming skills needed to succeed in the online Master of Engineering Management & Leadership program. You can join our non-credit bridge courses anytime: before you apply, after you submit your application, or upon acceptance into the program. Enrollment is managed by The Glasscock School of Continuing Studies at Rice University. All courses are taught by Rice Faculty.