Data Science Courses

The goal of this class is to give students an introduction to and hands on experience with all phases of the data science process using real data and modern tools. Topics that will be covered include data formats, loading, and cleaning; data storage in relational and non-relational stores; data governance, data analysis using supervised and unsupervised learning using R and similar tools, and sound evaluation methods; data visualization; and scaling up with cluster computing, MapReduce, Hadoop, and Spark.

Prerequisite: Enrollment in the Data Science program. Other students may be admitted with instructor permission.

This course provides a broad introduction to the practical side of machine-learning and data analysis. This course examines the end-to-end processing pipeline for extracting and identifying useful features that best represent data, a few of the most important machine algorithms, and evaluating their performance for modeling data. Topics covered include decision trees, logistic regression, linear discriminant analysis, linear and non-linear regression, basic functions, support vector machines, neural networks, Bayesian networks, bias/variance theory, ensemble methods, clustering, evaluation methodologies, and experiment design.

Prerequisite: DATA 601: Introduction to Data Science and enrollment in the Data Science program. Non-Data Science students may be permitted with instructor permission.

The goal of this course is to introduce methods, technologies, and computing platforms for performing data analysis at scale. Topics include the theory and techniques for data acquisition, cleansing, aggregation, management of large heterogeneous data collections, processing, information and knowledge extraction. Students are introduced to map-reduce, streaming, and external memory algorithms and their implementations using Hadoop and its eco-system (HBase, Hive, Pig and Spark). Students will gain practical experience in analyzing large existing databases.

Prerequisite: Enrollment in the Data Science program and DATA 601. Other students may be admitted with program director’s permission.

This course introduces students to the data management, storage and manipulation tools common in data science. Students will get an overview of relational database management systems and various NoSQL database technologies, and apply them to real scenarios. Topics include: ER and relational data models, storage and concurrency preliminaries, relational databases and SQL queries, NoSQL databases, and Data Governance.

Prerequisite: Enrollment in the Data Science program. Other students may be admitted with instructor permission. Corequisite: DATA 601: Introduction to Data Science

This course provides a comprehensive overview of important legal and ethical issues pertaining to the full life cycle of data science. The student learns how to think through the ethics of making decisions and inferences based on data and how important cases and laws have shaped the data science field. Students will use real and hypothetical case studies across various domains to explore these issues.

Prerequisite: Enrollment in the Data Science program. Other students may be admitted with instructor permission. Corequisite: DATA 601: Introduction to Data Science

This is a semi-independent course that provides the advanced graduate student in the Data Science program the opportunity to apply the knowledge, skills and tools they’ve learned to a real-world data science project. Students will work with a real data set and go through the entire process of solving a real-world data science project. The project may be conducted with industry, government and academic partners, who can provide the data set, with guidance and feedback from the instructor.

Prerequisite: Completion of all other core courses.

Data science relies heavily on the principles of probability theory and inferential statistics for extracting meaningful insight from complex datasets. DATA 608 introduces students to the essential concepts and tools of probability theory and statistics that form the backbone of data-driven decision-making processes. The course emphasizes a combination of theoretical tools, and application-oriented analysis to enable students to utilize statistical methods effectively in real-world data science scenarios.

This course introduces students to the essential concepts and tools of probability theory and statistics that form the backbone of data-driven decision-making processes. The course emphasizes a combination of theoretical tools, and application-oriented analysis to enable students to utilize statistical methods effectively in real-world data science scenarios. This course consists of two major parts. In the first part, the key concepts of probability theory such as the probability space, different distribution functions, probability mass functions and densities, random variables, variance and covariance, expectation values and moments, conditional probability, independence, Bayes formula, laws of large numbers, and the central limit theorem are introduced. In the second part of the course, the basic concepts of statistical inference are covered.

The contemporary literature on data science and machine learning primarily focuses on algorithms and methodologies and assume that the reader is already equipped with the relevant mathematics and statistics background. We have found students who want to delve into the foundations of basic machine learning and data science methods typically struggle with the required mathematical knowledge. This course brings the mathematical foundations of data science and machine learning to the fore and collects the necessary skillset in a single place so that the skills gap is minimized. The covered contents in this course consist of two major parts: The necessary and relevant concepts of linear algebra and the relevant calculus skills needed for solving optimization problems. For the linear algebra part, first an overview of the vector analysis, treatment of linear systems, matrices, subspaces, and determinants are presented. The main step in the linear algebra portion of the course deals with various matrix decomposition techniques. Specifically, the QR-decomposition, the LU-decomposition, the eigen decomposition, and the Singular Value decomposition are discussed in details and some of their applications (in data science and machine learning) are introduced. For the optimization portion of the course, first an overview of the necessary tools such as partial and directional derivatives, definition of critical points of multivariable functions, and the Hessian is provided. Then the Lagrange multiplier method for optimizations in the presence of constraints is introduced. At the end of the course, we will touch upon convex optimization and the gradient descent approach. If time permits, basic tools of the information theory will be discussed.

This course is designed to equip students with the essential skills to convey their findings in a compelling and accessible manner. Students will learn how to craft narratives that translate intricate data analyses into meaningful stories and engaging narratives for diverse audiences, including stakeholders, executives, and non-technical professionals.

This course reviews modern methods used in deep learning and neural network design from a practical perspective. Both a broad set of techniques that are commonly used in state-of-the-art neural network architectures and network styles prevalent in specific sub-domains like computer vision, natural language processing, and social network analysis are discussed. Students learn how to use these techniques in modern frameworks and how to apply these methods to new problems.

Prerequisite: Enrollment to MPS Data Program and DATA 602 or CMSC 678.

This course aims to teach the use of natural language processing (NLP) as a set of methods for exploring and reasoning about text as data. The focus will be on the applied side of NLP. Students will use existing NLP methods and libraries in Python to textual problems. Topics include language modeling, text classification, sentiment analysis, summarization, and machine translation.

Prerequisites: Enrollment in the MPS DS program; DATA 602 or CMSC 478/678.

This course introduces Generative AI (GenAI) by focusing on practical applications and hands-on experience with cutting-edge GenAI models. Students will learn to implement and apply GenAI models to generate text, images, music, and videos while addressing the ethical challenges inherent in GenAI.

Prerequisites: Enrollment to MPS Data Program and DATA 602 or CMSC 678.

This course provides a comprehensive introduction to applying data science techniques in the finance industry. It begins with an overview of financial data science and the various sources of financial data, moving through data cleaning, visualization, and analysis methods. Students will learn to apply supervised and unsupervised machine learning techniques, time series analysis, and natural language processing to real-world financial datasets. The course also covers essential financial models, portfolio optimization, anomaly detection, and algorithmic trading.

Prerequisites: Enrollment to MPS Data Program and DATA 602 or CMSC 678.

The special topics courses will cover emerging or specialized Data Science topics on an as-needed basis.

Supervision of student internship/co-op activities in the cybersecurity discipline. A short technical report that describes the activities conducted relevance to theoretical or operational concepts learned in other coursework and lessons gained through the internship/co-op experience is required at the end of the course. The course grade will be based on the technical report. The report will be submitted to the student’s Graduate Program Director by the last day of the semester.

Individualized research activities under faculty supervision related to data science.

The course provides an introduction to systems engineering and systems architecture with an emphasis on software/communications systems. It introduces systems engineering activities, artifacts and milestones, as well as key systems engineering-related references and tools. Although the course focuses principally on requirements elaboration and analysis, design synthesis and architecture modeling (DOD architecture framework, structured analysis, UML and SysML) and requirements document development and traceability, other topics include: life cycle models, DOD acquisition process, systems engineering process, quality management systems engineering planning and team-building, configuration management, risk management, technical performance measures, analysis and simulation, design and development, verification, validation and testing. Homework and exams are designed to provide the opportunity to practice the concepts learned in class.

Prerequisite: Prerequisites: B.S. in Computer Science, Information Systems or consent of instructor

Performance evaluation methods, Markovian queuing models, open networks of queues, closed product form queuing networks, simulation and measurement of computer systems, benchmarking and workload.

Prerequisite: Prerequisite: CMSC 411, CMSC 421 or consent of instructor.

This course covers fundamental concepts, methodologies, algorithms and the research challenges related to wearable computing, including the following: Emotional Design, Convergent Design Processes, Wearability Considerations, Wearable Sensors Networks, Wearable Networks, Physiological Wearable Sensors, Innovation Processes, Marketing and business considerations, Human Aware Computing, Context Awareness, Wearable Communities, Future Mobility and Wearable Systems Applications.

This course will introduce students to the techniques and research issues involved with mobile computing, which deals with access to the networked information and computation resources from wirelessly connected palmtop/laptop devices. Topics covered deal with both networking (MAC protocols, ad-hoc routing and mobile IP) and data management (proxy-based systems, mobile DBMS, mobile transactions, sensor networks and stream data) issues.

This course addresses the theoretical and practical issues in creating visual representations of large amounts of data. It covers the core topics in data visualization: data representation, visualization toolkits, scientific visualization, medical visualization, information visualization and volume rendering techniques. Additionally, the related topics of applied human perception and advanced display devices are introduced. Open to computer science students with a background in computer graphics or students in data-intensive fields and a familiarity with computers for data collection, storage or analysis.

Prerequisite: CMSC 435, CMSC 634 or consent of instructor.

An introduction to the theory of error-correcting codes, with an emphasis on applications and implementations. Shannon’s theorems, bounds on code weight distributions, linear codes, cyclic codes, Hamming and BCH codes, linear sequential circuits and encoding/decoding algorithms. Other topics may be drawn from Goppa, Reed Solomon, QR codes, non-linear codes and convolutional codes.

Numerical algorithms and computations in a parallel processing environment. The architecture of supercomputers, vectorizing compilers and numerical algorithms for parallel computers.

Prerequisites: CMSC 411 and MATH 221 or consent of instructor.

Advanced topics in the area of database management systems: data models and their underlying mathematical foundations, database manipulation and query languages, functional dependencies, physical data organization and indexing methods, concurrency control, crash recovery, database security and distributed databases.

Prerequisite: CMSC 461 or consent of instructor.

This course addresses the revolutionary concepts of service-oriented architectures (SOA) and Cloud Computing for web service applications arising in science, engineering and commerce. Service oriented computing is a style of loosely coupled multi-tier computing that helps organizations share logic and data among multiple applications and usage modes. The basic suite of web service protocols are presented for invoking autonomic computer-to-computer interactions. A brief introduction to parallel computing principles and languages such as MPI and the Cloud Computing Map Reduce paradigm will be presented. High-end clusters will be available for student projects as well as a tiled hyperwall for visualization services.

Prerequisites: CMSC 341 & CMSC 421.

A study of topics central to artificial intelligence, including logic for problem-solving, intelligent search techniques, knowledge representation, inference mechanisms, expert systems and AI programming.

Prerequisite: CMSC 471 or consent of instructor.

Natural language processing (NLP), the first non-numerical application of computing, was first studied more than 50 years ago. The ultimate goal of NLP is to enable computers to communicate with people the same way that people communicate among themselves. To do so, the computers must be able to understand and generate text. The course will introduce the students to the problems, methods, and applications of NLP.

Prerequisite: CMSC 331.

Computer vision has the broad goal of understanding visual signals (images and videos) for low/mid/high-level perceptual tasks. This course offers a comprehensive introduction to computer vision, covering first principles, analytical as well as learning-based algorithms, and frontier topics in contemporary computer vision research. This course covers the following topics: understanding the basics of cameras and image formation, image transformations and stereo vision, image filtering and feature extraction, basics of machine learning and neural networks for computer vision, image and video understanding including recognition, detection, and tracking, and a selection of advanced topics such as representation learning, multimodal learning, generative models, and reliability and ethics in computer vision. In addition to lectures by the instructor, this course also includes invited talks by external speakers to give students a glimpse into new findings, innovative ideas, and trends in computer vision.

A comprehensive study of fundamentals of neural networks. Topics include feed forward and recurrent networks; self-organizing networks and thermodynamic networks; supervised, unsupervised and reinforcement learning; and neural network application in function approximation, pattern analysis, optimization and associative memories.

The course begins with a brief overview of those topics in quantum mechanics and mathematics needed to understand quantum computation. It then will focus on quantum algorithms, covering such topics as quantum superposition and quantum entanglement, quantum decoherence, quantum teleportation, quantum Turing machines, Shor’s algorithm, Grover’s algorithm, Hallgren’s algorithm, quantum information theory, quantum data compression, quantum cryptographic protocols, quantum error-correcting codes and implementation issues. Various research-level problems will be discussed.

Prerequisite: CMSC 641, CMSC 651 or consent of instructor.

This course is an introduction to the theory and implementation of software systems designated to search through large collections of text. This course will have two main thrusts. The first is to cover the fundamentals of IR: retrieval models, search algorithms and IR evaluation. The second is to give a taste of the implementation issues through the construction and use of a text search engine.

Prerequisite: CMSC 341

This course will cover fundamental concepts, methodologies, and algorithms related to machine learning, including the following: decision trees, perceptrons, logistic regression, linear discriminant analysis, linear and non-linear regression, basis functions, support vector machines, neural networks, genetic algorithms, reinforcement learning, naive Bayes and Bayesian networks, bias/variance theory, ensemble methods, clustering, evaluation methodologies, and experiment design.

Prerequisite: CMSC 471 or 671, or permission of the instructor.

A set of CMSC 691 courses on various specialized computer science topics are typically offered each semester. Requires permission from Graduate Program Director.

A course in graduate-level microeconomic theory. This course presents the theory and analytical methods needed to bring economic analysis to bear on policy issues. Topics will include theories of consumer and firm behavior, market failure and the role of government in regulating the economy. Analytical techniques will include optimization, game theory, duality and dynamic optimization.

Prerequisites: ECON 311 and ECON 490 or equivalent.

This course covers both tools and models used in macroeconomics. The course focuses on static and dynamic analysis of the commonly used deterministic and stochastic models in the macroeconomics literature; both long-run models of economic growth and short-run models of economic fluctuations will be covered.

Prerequisites: ECON 312 and ECON 601 are recommended.

This course teaches basic econometric analysis and shows how it can be applied to examine policy issues. The course will provide the student with the skills needed to work with large data sets, to apply econometric techniques such as Ordinary Least Squares (OLS), Two-Stage Least Squares (2SLS), maximum likelihood estimation and the analysis of panel data. Students will be assigned problem sets that use data provided by the instructor and will learn how to use econometric packages such as SAS, STATA and SPSS.

Prerequisites: STAT 351 or STAT 355, ECON 421 and ECON 490 or equivalents.

Students get hands-on experience working on policy questions using real data. Students will analyze a selected policy issue by applying econometric methods to data sets provided by the professor. For example, students may use current population surveys to examine the relationship between education and earnings. Students will learn to construct variables from raw data and apply appropriate econometric techniques to answer policy questions. May be repeated as ECON 613 – Advanced Topics in Econometric Methods with a different instructor.

Prerequisite: ECON 611.

This course is a general survey of the field of health economics. Topics to be covered include medical care price indices; analysis of the markets for insurance; physician services; hospital care and nurses; and discussion of current policy debates, including cost inflation, uninsured populations and new forms of insurance.

This course introduces students to the interdisciplinary field of cybersecurity by discussing the evolution of information security into cybersecurity, cybersecurity theory, and the relationship of cybersecurity to nations, businesses, society, and people. Students will be exposed to multiple cybersecurity technologies, processes, and procedures, learn how to analyze the threats, vulnerabilities and risks present in these environments, and develop appropriate strategies to mitigate potential cybersecurity problems.

Prospective students who have earned the CISSP designation within the past 5 years may, if admitted, substitute another course for CYBR 620 “Introduction to Cybersecurity” in their first semester of the CYBR MS program. Students should provide evidence of successful completion of the CISSP exam within that timeframe (such as a transcript or official documentation from the certifying authority) to UMBC as part of their application.

Prerequisite: Enrollment in the CYBR program or in at least the second semester of graduate study. Other students may be admitted with instructor permission.

This course takes an operational approach to implementing and managing effective cybersecurity in highly networked enterprises. Topics include an evaluation of government and commercial security management models; security program development; risk assessment and mitigation; threat/vulnerability analysis and risk remediation; cybersecurity operations; incident handling; business continuity planning and disaster recovery; security policy formulation and implementation; large-scale cybersecurity program coordination; management controls related to cybersecurity programs; information-sharing; and privacy, legal, compliance, and ethical issues.

Prerequisite: Completion of CYBR 620 and in at least the second semester of graduate study. Other students may be admitted with instructor permission.

This course focuses the student on a broad range of topics relative to risk-based planning for enterprise cybersecurity. The intent is focusing on creating risk assessment and modeling approaches to solve cybersecurity issues so that organizations can build security framework and sustain a healthy security posture. This course analyzes external and internal security threats, failed systems development and system processes and explores their respective risk mitigation solutions through policies, best practices, operational procedures, and government regulations. Risk frameworks covered include NIST SP 800-12, SP 800-37, SP 800-39, and CERT/CC risk analysis guidelines.

This course is an introduction to text analytics using Python programming language. Students are introduced to programming and data mining using Python as a primary language. Principles of program design, programming structures, data structures, program testing, and debugging and text analytics is covered. Students will gain the skills necessary to implement Python-based solutions to Health IT problems and bio-medical (BMI) research challenges. This course not only teaches programming, scripting and text processing, but also provides an understanding of the broader context regarding how these programming techniques are deployed to address Health IT/BMI challenges. Upon completion, students will be able to use Python programming techniques and commands to write scripts to perform various user and administrative tasks, and to utilize advanced features of the language. Student will also be able to articulate an understanding of how Python is used in real Health IT/BMI use cases. As an end of course project students will implement, evaluate and refine a solution to explore biomedical data.

As the first required course in the series, Health Informatics I starts with introductory topics and proceeds with an overview of the essential topics of Health IT. Consistent with the interdisciplinary nature of Health IT, the course touches people and organizational aspects of health information systems as well as technology. While covering the essentials of Health Informatics, the course also achieves depth by engaging students in a semester- long study of a particular topic in Health IT. Some of the topics covered in this course include electronic health records, practice management, health information exchange, data standards, consumer health informatics and mobile health.

This course provides an overview of quality measurement and process improvement as they relate specifically to the health care industry. The course will focus on the tools, techniques, and resources available to health care professionals through effective use of health IT. Students will learn how to create quality benchmarks, gather data, and analyze results. They will learn how to design specific processes that directly address analytical findings and have the potential to improve outcomes. Students will understand a variety of implementation strategies for new processes, and be able to use health IT and other tools to measure the overall effectiveness. They will also learn how to prioritize improvement efforts across complicated business and practice systems. Students will work in groups during certain exercises, explore real and hypothetical case studies, and make a final presentation of an improvement process and implementation which utilizes health IT as their course project.

This course will cover the key components of public health practice, and include topics such as disease surveillance, outbreak, detection, investigation, vital records, and dissemination of information. The course will include data collection, data analysis, data cleaning, ways to provide data to customers, improve data quality and access to care, and develop and evaluate interventions. Students will develop an understanding of the use of IT to support public health practice, increase individual effectiveness, and improve the effectiveness of the public health enterprise.

This graduate level course provides an introduction to the theoretical and practical aspects of creating and maintaining databases within a healthcare setting. This is a beginner’s course and no previous programming or technical experience is required. Topics include: relational databases, normalization, data integrity, database design, data querying, and data forms/reports. The class includes applied lab and project components to provide hands-on experience with creating and maintaining databases; using Access and SQLite as our database systems. This course is intended for students interested in databases within the context of healthcare informatics, health information technology, and healthcare.

As the second required course in the health informatics series, Health Informatics II extends the coverage of the health informatics issues into areas such as online medical resources and search engines, evidence-based medicine and clinical practice guidelines, disease management, disease registries and quality improvement, patient safety and health IT, electronic prescribing, telemedicine, and bioinformatics.

Prerequisite: Health Informatics I.

A broad overview of decision making and the systems that are designed to support the process. The management process, computer support for management, the technology of management, decision technology system types, including artificial intelligence, decision support systems, executive and geographic information systems, and idea processing systems, system architectures, system integration considerations, system design and development methodologies, system performance measurement and evaluation, management of decision technology systems, organizational and user issues.

The focus of this course is on advanced topics in healthcare information systems. Examples of topics include the implications of the administrative simplification provisions (e-commerce standards, privacy and security) of the Health Insurance Portability and Accountability Act, the workflow management aspects of cancer center information systems and information retrieval aspects of cancer research libraries.

Prerequisite: IS 660.

Providing access to large amounts of information is an important function of information systems. This course discusses the designs of user interfaces that allow users to search for, browse and interact with information. Specifically, students will be introduced to human information-seeking behavior and its implications for user interfaces, including user interfaces for information retrieval systems and a wide variety of information visualization tools. Information retrieval systems enable users to search for and browse information. Information visualization is the application of computer-supported graphical tools to presentation of large amounts of abstract information.

Prerequisite: IS 629 or consent of the instructor.

Intelligent technologies explore the fundamental roles and practical impacts of artificial intelligence and knowledge management in various paradigms. The purpose of this course is to offer students an in-depth understanding of concepts, methodologies, techniques, applications, and issues of a variety of intelligent technologies. The topics include, but are not limited to, intelligent agents, semantic Web, ontology, information retrieval and reasoning, social network analysis, and Web mining. Intelligent technologies will be discussed in the context of popular information system applications such as search engines, e-commerce, computer-mediated communication, and intelligent user interface.

Prerequisite: Graduate student standing and permission of the instructor.

This course offers understanding of the latest technologies to manage semi-structured data such as XML and provides hands-on experience on managing and querying semi-structured data using relational database management systems. This course also introduces students to two important application areas of semi-structured data: data sharing and data privacy. Topics include, but are not limited to basic concepts of XML, XML Schema (XSD), XML query languages such as XPath amd XQuery, storing XML in databases, querying XML in databases, publishing XML from databases, privacy issues for data sharing, solutions to privacy issues including Platform for Privacy Preferences and XML encryptions, privacy preserving data mining, and economis aspects of data privacy. Students will keep abreast of the latest technologies and research innovations in the field of semi-structured data management, data sharing, and data privacy. There will be database programming assignments to familiarize students with the course topics. In addition, a group project will be part of the course to expose students to real life application of semi-structured data management technologies.

This course focuses on the theory and practice of integrating systems and information. The problem of integrating information is extremely common nowadays when an organization buys another and inherits an entire IT department which may not be compatible with its own one. Data systems and information should easily interoperate for the success of the organization. This course investigates the various technologies in the field of information integration with an emphasis on semantics. Topics that are covered include: Data Integration Architectures, Data Warehouses, Modeling Data Semantics, Semantic Interoperability, Metadata, Semantic Integration Patterns, Context-Awareness, Semantic Networks, Mediation and Wrapper techniques, Web Services and Service Oriented Architectures (SOA), Integration Servers, etc.

Prerequisite: IS 620

Information extraction (IE) is the problem of distilling structured information from text. Example Information Extraction tasks range from finding mentions of Named Entities such as people and places or relationships between entities, finding opinions about products to deep semantic understanding of a sentence. Information Extraction has emerged as an essential building block for applications that leverage information from text, including social media analytics, healthcare analytics, financial risk analysis, semantic search, regulatory compliance, legal discovery and many others. The course will provide an overview of IE techniques developed in the past 20 years and discuss their advantages and limitations. Especially, we focus on two types IE paradigms: (1) rule-based Information Extraction and (2) machine learning-based Information Extraction.

Social interaction via the Internet is becoming increasingly important. People are gathering in online communities of interest and communities of practice to discuss health, hobbies, games, education, politics and professional issues. In this class, students will analyze the technology and social support needed to make these social interactions successful. They also will discuss and debate current research in this field and either develop an online community or carry out a small research project.

This course will analyze how organizations are using electronic commerce to streamline operations, reach customers, and increase profitability. The technologies involved in electronic commerce will be examined. The organizational, behavioral, social, legal, security, and international aspects of EC will be discussed. The primary emphasis will be on Web based technologies and issues. This course will reflect the most current research and application.

The purpose of this course is to provide a comprehensive discussion of using organizational databases to enable decision support through mining data. This course will provide an in-depth understanding of the technical, business and research issues in the area of data mining. Areas of data mining will include justifying the need for knowledge recovery in databases and data mining methods such as clustering, classification, Bayesian networks, association rules and visualization. The course will provide a brief introduction to issues in data warehousing which include designing multi-dimensional data models; cleansing and loading of data; reporting; ad hoc querying and multi-dimensional operations, such as slicing, dicing, pivoting, drill-down and roll-up operations. New areas of research and development in data mining will also be discussed.

Prerequisite: IS 620.

IS 777 has the objective of introducing students to the essential concepts related to analyzing data for statistical learning. The fundamental building blocks, principles, and ideas related to analyzing data and building statistical models will be discussed. Furthermore, the course will involve application of the concepts by including various assignments, exercises, and activities in the statistical environment, R. In addition, the teams of students will be engaged in a semester-long project involving a particular topic selected by students.

Students learn the fundamentals of managing projects in a systematic way. These fundamentals can be applied within any industry and work environment and will serve as the foundation for more specialized project management study. Principles and techniques are further reinforced through practical case studies and team projects in which students simulate project management processes and techniques.

Students analyze leadership case studies across a wide range of industries and environments to identify effective leadership principles that may be applied in their own organizations. Students learn how to influence people throughout their organization, lead effective teams, create an inclusive workplace, use the Six Sigma process, implement and manage change and develop a leadership style.

Prerequisite: ENMG 652: Management, Leadership and Communication

This course focuses on analysis and interpretation of financial statements with an emphasis on the measurement of results of operations and financial position of business organizations. The course covers the fundamentals of reading and analyzing financial statements and reports and applying to a business or work setting. The course will cover budgeting, profit planning, return on investment, risk and return, strategy and other financial information used in business decision-making. Students will discuss various types of contracts based on cost structure and prepare budgets as used in grant funding proposals.

This course is intended to integrate the learning from the previous engineering management courses and to focus it on the perspective and problems of the Chief Executive Officer and other “C-suite” organizational strategic managers. The focus is on understanding the Strategic Management Process (SMP) in large organizations, which includes both strategy formulation and strategy implementation. There is a particular focus on strategic management of technology and innovation. The theme of the course is that large organizations do better when they formulate a strategic action plan based on their strategic management process. In addition to case studies and textbook readings, working in groups, students will complete a Business Plan to develop and demonstrate their strategic management skills.

This course provides the foundational framework to understand the system engineering (SE) process, selection of specialized SE tools and the execution of SE under differing design or acquisition philosophies. the courses addresses: (1)SE principles (2)SE processes and methodologies (3) integration of technical disciplines and (4) SE management.

This course can be counted as either a management course or an engineering course for the M.S. in Engineering Management.

This course is designed to help the student apply managerial concepts and skills to managing and leading virtual and/or global work teams. Geographically dispersed work teams have great challenges: tone is difficult to convey electronically, time zones limit audio communication opportunities, work oversight requires more reposting, and team building is exceedingly difficult using technological – rather than in-person – tools. Language and culture differences in multinational teams compound these challenges. Students will learn to empower others, build credibility, communicate appropriately and adapt quickly across cultures and technologies.

This advanced course in project management builds on the beginner level project management courses to expand the hands-on applications, with a focus on critical evaluation of project performance and ultimately creating an environment for maximizing one’s own project management performance. With a strong emphasis on the importance of learning through application, the course will bridge academia with the professional business environment to provide opportunities for students to interact with industry professionals as the students execute their course work. Students will also confront the real challenges facing project managers associated with the growing global and virtual workforce through the use of online learning tools and methods of collaboration. At the successful completion of the course, students will have the requisite skills and experiences necessary to function effectively, and artfully, as skilled project managers.

This course provides an overview of the basic principles and tools of quality and their applications from an engineering perspective. The primary quality schools of thought or methodologies, including Total Quality Management, Six Sigma and Lean Six Sigma, and quality approaches from key figures in the development and application of quality as a business practice, including W. Edwards Deming and Joseph M. Juran will be analyzed. Some of the key mathematical tools used in quality systems will be discussed, including Pareto charts, measurement systems analysis, design of experiments, response surface methodology, and statistical process control. Students will apply these techniques to solve engineering problems using the R software. Reading assignments, homework, exams, and the project will emphasize quality approaches, techniques, and problem solving.

This course will cover fundamental project control and systems engineering management concepts, including how to plan, set up cost accounts, bid, staff and execute a project from a project control perspective. It provides an understanding of the critical relations and interconnections between project management and systems engineering management. It is designed to address how systems engineering management supports traditional program management activities to break down complex programs into manageable and assignable tasks.

This course provides an overview of decision and risk analysis techniques. It focuses on how to make rational decisions in the presence of uncertainty and conflicting objectives. This course covers rational decision-making principles and processes; competing objectives, multi-attribute analysis and utility theory; modeling uncertainty and decision problems using decision trees and influence diagrams; solving decision trees and influence diagrams; uses of Bayes’ Theorem; defining and calculating the value of information; regression analysis; incorporating risk attitudes into decision analyses; and conducting sensitivity analyses. A significant portion of the course is devoted to the use of various applications of analytic, empirical, and subjective probability theory to the modeling of uncertain events. As such, students will find it useful to have some experience with basic probability.

This course can be counted as either a management course or an engineering course for the M.S. in Engineering Management.

This course offers an overview of innovation and its role in entrepreneurial ventures, both in new companies and within existing corporations. The basics of entrepreneurship with specific emphasis on technology-based business start-up are investigated. For the purposes of this course, technologies include IT, engineering and biotech. The course covers where to find innovative ideas and how to determine if a business idea is feasible along with an overview of the critical success factors in a new venture start-up.

A course in political economy dealing with the implications and consequences for policy outcomes of different models of economic analysis, including an introduction to microeconomic theory. Note: May not be counted toward the concentration in economics.

This course is designed to introduce students to the processes by which policy is made in the United States. It introduces students to the policymaking system, including the institutional, structural and political contexts, as well as the policy making environment. The various stages of the policymaking process from problem definition and agenda-setting to implementation are examined and discussed, and important theories and models of policy making are presented. Significant concepts relating to the political analysis of public policy are discussed, such as the social construction of problems, group demands, political influence and resources, motivations and incentive for political behavior and political feasibility.

An overview of the basic principles and elements of policy analysis. The course focuses on the activities and elements of policy analysts. In addition, the relationship between policy analysis and policy making, along with emerging professional and ethical issues, are addressed.

Advanced course in analyzing and evaluating data. Focuses on interpreting statistical procedures for assessing the impact of programs and policies based on a variety of experimental and quasi-experimental designs, including true experiments, non-equivalent control group designs and interrupted time-series designs.

An introduction to the practical application of widely used basic multivariate regression techniques. Experience in the use of these techniques is provided through hands-on exercises and the preparation of an original regression analysis of real-world data in an area of interest selected by the student. Methods covered include multiple linear regression, models with binary dependent variables, analysis of pooled data, and methods for assessing and comparing the performance of alternative models. Rather than focusing on the mechanics of regression computation, the course emphasizes the basic concepts involved in constructing and estimating regression models, and in interpreting their results. Consent of instructor.

Topics selected on the basis of the background and interests of the faculty member and students.

Students learn the fundamentals of managing projects in a systematic way. These fundamentals can be applied within any industry and work environment and will serve as the foundation for more specialized project management study. Principles and techniques are further reinforced through practical case studies and team projects in which students simulate project management processes and techniques.

This advanced course in project management builds on the beginner level project management courses to expand the hands-on applications, with a focus on critical evaluation of project performance and ultimately creating an environment for maximizing one’s own project management performance. With a strong emphasis on the importance of learning through application, the course will bridge academia with the professional business environment to provide opportunities for students to interact with industry professionals as the students execute their course work. Students will also confront the real challenges facing project managers associated with the growing global and virtual workforce through the use of online learning tools and methods of collaboration. At the successful completion of the course, students will have the requisite skills and experiences necessary to function effectively, and artfully, as skilled project managers.

This course will address the methods and processes for developing new products, defining market opportunities, product planning, product design and manufacturing. Topics covered will include market research and collecting user requirements, translation of user needs into product specifications, prototyping/market testing to evaluate product concepts, product design, manufacturing planning, and product launch. This should be the first course a student takes in the certificate program.

Note: Prior to Fall 2024, this course was listed as ENME 615.

This course is designed to help the student apply managerial concepts and skills to managing and leading virtual and/or global work teams. Geographically dispersed work teams have great challenges: tone is difficult to convey electronically, time zones limit audio communication opportunities, work oversight requires more reposting, and team building is exceedingly difficult using technological – rather than in-person – tools. Language and culture differences in multinational teams compound these challenges. Students will learn to empower others, build credibility, communicate appropriately and adapt quickly across cultures and technologies.

This course provides an overview of the basic principles and tools of quality and their applications from an engineering perspective. The primary quality schools of thought or methodologies, including Total Quality Management, Six Sigma and Lean Six Sigma, and quality approaches from key figures in the development and application of quality as a business practice, including W. Edwards Deming and Joseph M. Juran will be analyzed. Some of the key mathematical tools used in quality systems will be discussed, including Pareto charts, measurement systems analysis, design of experiments, response surface methodology, and statistical process control. Students will apply these techniques to solve engineering problems using the R software. Reading assignments, homework, exams, and the project will emphasize quality approaches, techniques, and problem solving.

Understanding and grappling with considerations of diversity, equity, and inclusion (DE/I) within technical project management is growing in both relevance and importance. This course addresses this imperative through equipping the student with the knowledge, skills, and attitudes to develop a DE/I mindset in the management of technology-based projects. Centered on exploring how to incorporate and advance DE/I within the five (5) major project management process groups, this course provides a balanced overview of both the science and art of inclusive technical project management. A particular focus of this course is on developing the professional skills, growth mindset, and systems perspective that underpin the DE/I mindset in technical project management. This course combines lecture presentations, group project-based assignments, group discussions, individual case study, and exams.

This course provides an overview of decision and risk analysis techniques. It covers modeling uncertainty, the principles of rational decision-making, representing and solving decision problems using influence diagrams and decision trees, sensitivity analysis, Bayesian decision analysis, deductive and inductive reasoning, objective and subjective probabilities, probability distributions, regression analysis. This course can be counted as either a management course or an engineering course for the M.S. in Engineering Management.

This course explores the best management practices of international projects, emphasizing the importance of leadership skills and virtual teamwork to successfully navigate through managing an international project. International projects differ from domestic projects by their complexity of culture, increased communications and collaboration requirements, local customs and practices, differing languages and currencies, processes, and the type of resources that may be available. The course describes how to conduct project planning in each of the life cycle acquisition process phases and then to execute the plan through recommended international organizational structures.

Acquisition and Execution of Technical Contracts is designed for professionals in the public and private sectors. The course provides coverage of global government and commercial sector acquisition practices, industry standards for business acquisition, current issues in business, contracting, legal and finance, and policy issues associated with business acquisition and contract execution.