Development and management of engineering and technology projects. Project proposal preparation; resource and cost estimating; and project planning, organizing, and controlling: network diagrams and other techniques. Role of project manager: team building, conflict resolution, and contract negotiations. Discussion of typical problems and alternative solutions. Case studies and student projects.
Principles and application of statistical methodology, integrated with considerable use of major statistical computing system. Probability and probability distributions, forming and testing hypotheses using parametric and nonparametric inference methods. Matrix-based simple linear regression and correlation.
Development and application of fundamental deterministic optimization methods including linear programming, dynamic programming, introductory integer programming, basic game theory, and classical optimization theory applied to constrained and unconstrained models.
An introduction to applied data science, including machine learning and data mining tools. Topics include supervised and unsupervised algorithms, techniques for improving model performance, evaluation techniques and software packages for implementation. Emphasis will be put on real-world applications in various domains, including healthcare, transportation systems, etc.
Technology course that will examine theoretical foundations of General System Theory applied to engineering and organizational enterprises addressing issues concerning systems, the effectiveness of organizations in the context of traditional management related issues, as well as incorporating the critical impact of systems thinking on the socio-technical environment. Among the topics to be covered in the course are: the meaning of General Systems Theory (GST); GST and the unity of science; the concept of Equifinality; the characteristics and modeling of open systems; the concepts of the Learning Organization; the principle of Leverage; building Learning Organizations; and issues related to Socio-Technical Systems. Systems Engineering focuses on defining customer needs and required functionality early in the development cycle, documenting requirements, then proceeding with design synthesis and system validation while considering the complete problem including operations, performance, test, manufacturing, cost, and schedule. This subject emphasizes the links of systems engineering to fundamentals of decision theory, statistics, and optimization.
Strategic planning process and strategic management in practice; corporate vision and mission; product, market, organizational, and financial strategies; external factors; commercialization of new technologies; and competition and beyond.
Modeling and simulation of business and industry systems to enhance management, strategic, and operational decision-making. Hands-on experiences of simulation software package (e.g., Arena) will be provided with case studies in manufacturing, supply chain and logistics, healthcare, etc.
Principles of engineering management, including: business and organization design, culture, leadership, marketing and competition in global economy, motivation and performance management, empowerment, organizational behavior, and diversity. Systems thinking, learning organizations, and systems dynamics modeling. Principle application to work settings and case studies.
This course will include a survey of the state-of-the-art in business analytics, exploring how companies use it for competitive advantage, and identifying factors for successful strategic and operational support as business intelligence tools. Topics will include business metrics used for performance measurement and incentives, effective information communication, visualization, process improvement, the development and application of algorithms, and ethical implications. The course will also include a series of opportunities for problem-solving using business analytics in different areas of an organization, such as operations, supply chain management, human resources, finance and marketing.
Decision modeling provides a framework on how to develop models that improve decision-making under uncertainty. Using a variety of statistical techniques, students will learn how to apply decision models within a variety of business contexts. The topics include: discrete choice models, simulation models and forecasting models.
This course covers fundamental topics related to developing and using databases and data warehouses to provide business intelligence (BI). The course includes: in-depth coverage of structured query language (SQL), stored programs, and database design and implementation, data warehousing concepts, dimensional modeling (star schemas), the extraction/transformation/load (ETL) process, and online analytical processing (OLAP)/ BI functionalities. As a final project, students build a small data warehouse, populate it with data, and create a dashboard to analyze and communicate the data to a broad audience.
Optimization is a prescriptive analytics methodology designed to yield the best solution to a given problem. This applied optimization course will examine optimization through a business analytics lens. Students are exposed to the theory and application of optimization, including linear programming, nonlinear programming, discrete optimization, specialized networks and heuristics. Students will develop an understanding of algebraic formulations, develop spreadsheet model prototypes and use large-scale optimization software to solve challenging business problems.
This course is designed to provide students with a deep understanding of the theory and practice of supervised and unsupervised learning, including regression, classification, and clustering. An important part of this course is the use of statistical software, which is used extensively in labs and assignments in this class and may reappear in other classes throughout the program.
This course is designed to provide students with a deep understanding of big data and cloud computing. The data storage and retrieval techniques that have served the information processing industry for decades have proven inadequate in the face of the huge collections of data. Businesses are requiring a new set of technologies that are specifically designed to deal with these huge data sets. This course will focus on applications of big data and cloud computing techniques that will be used to process large-scale data sets.
This course focuses on artificial intelligence (AI) applications in business, and covers implementations of contemporary AI techniques, such as deep learning, natural language processing, and planning for solving business problems.
This experiential-based practicum course will include a comprehensive business analytics project that students will complete in small work teams. The projects will be hosted by businesses, government, or non-profit organizations. The students will complete the project from start to finish integrating the skills that have been acquired from the previous course work in the business analytics program. They will define and frame a complex problem, develop a systematic approach to solving it using analytics, identify methodologies that are suited to the problem, quickly prototype solutions with those methodologies to identify the best approach and, ultimately, generate an innovative solution and persuasively convey that solution using data visualization techniques and communication skills.