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Data Science

Course Descriptions

Note: Not all courses are offered every semester, and new courses may be added at any time. Check the schedule of classes, for the latest offerings.

DATA 601: Introduction to Data Science [3]

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.

DATA 602: Introduction to Data Analysis and Machine Learning [3]

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

DATA 603: Platforms for Big Data Processing [3]

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.

DATA 604: Data Management [3]

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

DATA 605: Ethical and Legal Issues in Data Science [3]

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

DATA 606: Capstone in Data Science [3]

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 will be conducted with industry, government and academic partners, who will be responsible for providing the data set, with guidance and feedback from the instructor. Prerequisite: Completion of the required courses.

ENMG 652: Management, Leadership and Communication [3]

Students learn effective management and communication skills through case study-analysis, reading, class discussion and role-playing. The course covers topics such as effective listening, setting expectations, delegation, coaching, performance, evaluations, conflict management, negotiation with senior management and managing with integrity.

Spatial Analytics Pathway

GES 773: GIS Modeling Techniques [3]

This course addresses the concepts, tools, and techniques of GIS modeling, and presents modeling concepts and theory as well as provides opportunities for hands-on model design, construction, and application. The focus is given to model calibration and validation.

GES 774: Spatial Statistics [3]

This course investigates statistical techniques for exploring and characterizing spatial phenomena. The course covers local/global cluster analysis, spatial autocorrelation, interpolation, kriging, as well as exposure to prominent GIS statistical packages. An emphasis is placed on exploratory spatial data analysis (ESDA)to develop spatial cognition and analytical skills with practical applications to modeling spatial phenomena in computer environments.

GES 778: Advanced Visualization and Presentation [3]

Web technologies are providing increasingly sophisticated environments for visualization of spatial data. This course explores advanced techniques for visualizing multivariate and multidimensional data. Topics include advanced cartographic techniques, 3D, dynamic data update, and temporal modeling. Students will learn to create geospatial data-driven Web apps with modern technologies and open source software, including HTML5, JavaScript, and D3. Project-based learning will allow students to advance through the course at a pace that's tailored to their backgrounds. Although the course requires no advanced knowledge of Web technologies, students with previous programming experience will have a wider range of project options.

Data Science Analysis Pathway

IS 661: Biomedical Informatics Applications [3]

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.

IS 706: Interfaces For Info. Visualization & Retrieval [3]

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.

IS 707: Applications of Intelligent Technologies [3]

This course provides a survey of artificial intelligence concepts, technologies, applications, techniques, methodologies and issues. The first half of the course will focus on expert systems and the knowledge engineering life cycle. The second half of the course will highlight various knowledge technologies, including case-based reasoning, genetic algorithms, fuzzy logic, neural networks, hybrid intelligent systems, data mining and knowledge management. The course also will discuss management implications of use, non-use and misuse of AI technologies. Prerequisites: Graduate student standing and consent of the instructor.

IS 721: Semi-Structured Data Management [3]

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 additional, a group project will be part of the course to expose students to real life application of semi-structured data management technologies.

IS 722: Systems and Information Integration [3]

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

IS 728: Online Communities [3]

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.

IS 731: Electronic Commerce [3]

This course analyzes the role of Web design in electronic commerce (e-commerce) from organizational and operational perspectives, and is focused on user-related (front-end) issues in e-commerce. One of the goals of Human-Computer Interaction (HCI) is to solve real-world problems in design and use of technology in the e-commerce environment from the user's perspective. Tools and techniques for creating and improving e-commerce sites are emphasized, as well as developing guidelines, heuristics and testing methods. Structure, navigation and information sharing-related HCI issues are covered within the context of e-commerce.

IS 733: Data Mining [3]

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: Data Analytics for Statistical Learning [3]

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.

Project Management Pathway

ENMG 650: Project Management Fundamentals

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.

ENMG 661: Leading Virtual Global Teams

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.

ENMG 663: Advanced Project Management Applications

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 on-line 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.

Management Science Pathway

ENMG 650: Project Management Fundamentals

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.

ENMG 654: Leading Teams and Organizations

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

ENMG 656: Engineering Law and Ethics

This course provides a comprehensive overview of important legal principles affecting engineers, engineering sciences and corporate management, with a focus on the intersection of these legal principles with business ethics. The student learns how to think through and process legal problems consistent with ethical norms, and how to analyze business risks in light of operative legal constructs, taking into consideration ethical issues, to arrive at a range of correct business decisions. Throughout the course, the student will learn substantive legal principles including an overview of constitutional, contract, tort, corporate and regulatory law. Students will work in groups during certain exercises, role play in real and hypothetical case studies, and make a final presentation of a comprehensive legal and ethical engineering problem.

ENMG 658: Financial Management

This course will cover the fundamentals of setting up, reading and analyzing financial statements and reports in a business setting.  Course topics will include: project budgeting, profit planning, return on investment and basic corporate finance. Students will analyze case studies from specific industries.

ENMG 659: Strategic Management

This course is intended to integrate the learning from the previous management courses and to focus it on the perspective and problems of the Chief Executive Officer and other organizational strategic managers. The theme of the course is that any organization improves its chances of sustained success when its managers formulate an action-oriented strategic business plan based on the strategic management process. Case studies are included to illustrate the concepts and their applications.

Prerequisite: Minimum of three engineering management courses

ENMG 660: Systems Engineering Principles

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 metholodogies (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.

ENMG 661: Leading Virtual Global Teams

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.

ENMG 663:  Advanced Project Management Applications

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 on-line 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.

ENMG 664: Quality Engineering & Management

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

ENMG 668: Project and Systems Engineering Management

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.

ENMG 672: Decision and Risk Analysis

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.

ENMG 690: Innovation and Technology Entrepreneurship

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.

Cybersecurity Pathway

CYBR 620 : Introduction to Cybersecurity

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.

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

CYBR 650: Managing Cybersecurity Operations

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.

CYBR 658: Risk Analysis and Compliance

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.



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