Online MS in Engineering Management: Courses

This course covers how to design and manage operations in production and service operations. Subjects covered are process design, strategic operations, forecasting, aggregate planning, inventory management, facilities planning and logistics, scheduling, and other principles of Industrial Management.

Covers statistical and business analytics tools useful for making effective managerial decisions in a disorganized and uncertain environment in all functional areas of business. Students learn the essential statistical topics of description, probability, inference and regression, and how to apply them using Microsoft Excel. They learn how to choose appropriate statistical methods in realistic business contexts and how to interpret and effectively communicate results. Students also learn how to use data visualization tools, pivot tables and charts, data tables, optimization models and Monte Carlo simulation.

This course covers essential probability and statistics concepts for decision analysis, as well as Bayesian decision theory, game theory, and utility theory.

Risk assessment and management is the identification, analysis, and prioritization of risks; as well as the coordinated treatment of risk to prevent, minimize, monitor, and control the probability and/or impact of undesirable events and consequences. Areas covered include the principles and applications of risk assessment and management in the context of engineering management and systems engineering. This course is about the systematic approach to the management of risk as applied to engineering, operations, and management decisions Students will be prepared to function in a business environment, developing an awareness of the challenges, the tools, and the process of designing and implementing risk assessment and management strategies.

This course covers supply chain strategy, the role of information in supply chains and the bullwhip effect; network design in a supply chain; transportation network problem; routing in supply chains; sourcing; risk pooling; incentives in the supply chain, and global supply chain management.

This course covers random number generation, random variety generation, components of discrete event simulation, learning simulation software, and the simulation of simple systems: queuing, inventory, manufacturing, QC, transportation, layout.

This course covers the essential machine learning techniques and algorithms and their applications. Topics include supervised and unsupervised learning, clustering, classification algorithms, linear regression, support vector machines, decision trees, random forests, neural network, deep learning, and reinforcement learning. Throughout the course, students will be exposed to real-world industrial, business, medical and social problems, where the obtained skills are employed to handle data and develop machine learning based solutions. The material and structure of the course are designed with a preference for the practical knowledge of AI more than mathematical or theoretical concepts. Different Machine Learning applications will be discussed including computer vision, natural language processing, time-series prediction, speech recognition, sentiment analysis, cybersecurity, among others.

(*May be substituted with an elective if the student has Computer Science or Computer Engineering major with the required math background)

This course covers the mathematical principals required for the various concepts in the area of applied artificial intelligence. This course aims at delivering the mathematical topics in a balanced manner based on solid theoretical foundation while focusing on the computational aspects and application to data problem. Topics covered include linear algebra, multivariate calculus, optimization, regression, statistics of datasets, orthogonal projections, principal component analysis, and probability. The course provides computational and practical examples of the covered topics.

(**May be removed as prerequisite requirement if the student has the python programming and required libraries background.)

This course covers programming techniques used in AI applications. Topics include programming constructs, I/O, conditional constructs, iterative control structures, structured decomposition, method call and parameter passing, classes, 1-D and 2-D arrays, libraries, APIs, and Data Structures. The course will use Python with several tools where students learn programming with a beginner-friendly introduction to Python and AI libraries including learning how to analyze data, integrate and use basic machine learning algorithms and APIs, create visualizations, implement and test some models, and analyze results.

This course examines real world examples of how insights gained through analytics to significantly improve a business or industry. Through our tour of real-world transformations driven by analytics, students will gain knowledge in the use of descriptive, diagnostic, predictive, and prescriptive analytics models.

The applications studied in this course rely heavily on predictive and prescriptive analytics tools. Students will learn how to define business problems requiring prediction and then select the most appropriate forecasting strategy to meet the application. Similarly, students will learn how to frame a decision problem and then select and apply the appropriate data driven decision making strategy.

This course introduces the Python programming language along with data analysis and exploration techniques. Topics covered include the fundamentals of Python programming, visualization, and exploratory data analysis using key libraries such as NumPy, Seaborn, Pandas, and Matplotlib.

The objective of this course is to provide an understanding of financial accounting fundamentals for prospective consumers of corporate financial information, such as managers, stockholders, financial analysts, and creditors. The course focuses on understanding how economic events like corporate investments, financing transactions and operating activities are recorded in the three main financial statements (i.e., the income statement, balance sheet, and statement of cash flows).

This course introduces students to corporate finance principles and applications. It covers the following topics: (1) Financial Statements; (2) Cash Flow Estimation; (3) Time Value of Money; (4) Capital Budgeting Methods; (5) Valuation of Bonds and Stocks; (6) Risk and Return; and (7) Cost of Capital.

This course is an overview of microeconomics from a managerial decision-making standpoint, emphasizing and applying the basic concepts to selected problems. Topics include the firm’s behavioral and managerial theories, determination of national income, demand estimation, cost determination, forecasting, and government regulation.

Theory of quantity takeoffs. Computerization of cost estimates, CSI divisions and applying personal judgement. Integration of VE into the construction planning and management process, use of quality modeling and developing a VE job plan. Scheduling basics and CPM, resource management, alternative and advanced scheduling techniques.