Online MS in Engineering Management: Courses

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 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.

Data science enables us to process big amounts of structured and unstructured data to detect patterns and perform in-depth and conclusive analysis. This course covers the main techniques involved in the data processing pipeline, including data capture (scraping, cleaning, and filtering), feature engineering (representation, selection, and transformation), data augmentation (knowledge-based and corpus-based), data mining (regression analysis and predictive modeling), and data visualization (search and exploration). Real-life applications will be considered including search engines, text summarization, text auto-correction, chat bots, personal assistants, social network analysis, sentiment analysis, and event detection, among others. Students will learn about advanced AI techniques and tools used on these applications.

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.

Data science enables us to process big amounts of structured and unstructured data to detect patterns and perform in-depth and conclusive analysis. This course covers the main techniques involved in the data processing pipeline, including data capture (scraping, cleaning, and filtering), feature engineering (representation, selection, and transformation), data augmentation (knowledge-based and corpus-based), data mining (regression analysis and predictive modeling), and data visualization (search and exploration). Real-life applications will be considered including search engines, text summarization, text auto-correction, chat bots, personal assistants, social network analysis, sentiment analysis, and event detection, among others. Students will learn about advanced AI techniques and tools used on these applications.

This course covers essential topics in data science including data collection and preprocessing, exploratory data analysis and data visualization. The course also covers supervised and unsupervised machine learning techniques including regression, classification, clustering and dimensionality reduction, as well as model selection and assessment, and resampling techniques.

This course covers global business, ethics, social responsibility, and legal systems. Business in the global environment is comprised of many factors: the parts played by governments, international institutions, regulations, world monetary and trade systems, global ethics, social responsibility, cultural diversity, differences in opportunities, environmental concerns, economic development, technology, economy, and education among others. The course discusses the conflicts among corporate beneficiaries and socially responsible corporate responses.

This course addresses the design and management of effective and efficient systems for the proper conveyance of services. In contrast to traditional operations and production management in manufacturing, services are co-created through interaction between both the customer and the service provider. This course will give students the tools to design service systems that obey standard principles in layout, queuing, inventory, and quality against the backdrop of human interactions in healthcare.

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.

Construction dispute prevention strategies. Partnering. Negotiations and dispute review boards. Nonbinding dispute resolution. Binding dispute resolution: mediation and arbitration. Court alternatives and litigation. Conflict management plan. International aspects of construction dispute resolution. Case studies.