Computer Science & IT Graduate and Undergraduate Courses

For students with no prior experience with computers. Organization and function of computer systems; application of computers in today's society; social impact of computers. Introduction to algorithms, various types of application packages, and the Internet. Not for computer science majors. Laboratory course.   [ 4 cr. ]

This course is a technically-oriented introductory survey of information technology. Students learn about basic computer information, different types of business systems and basic systems analysis, design and development. Students also study basic mathematics, software development and create simple Java programs.  [ 4 cr. ]

Introduction to problem-solving methods and algorithm development. Includes procedural and data abstractions, program design, debugging, testing, and documentation. Covers data types, control structures, functions, parameter passing, library functions, and arrays. Laboratory exercises in Python. Laboratory course.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS201) or instructor's consent - Covers the elements of object-oriented programming and the C++ language. Data types, control structures, functions, library functions, classes, inheritance, and multiple inheritance. Use of constructors, destructors, function and operator overloading, reference parameters and default values, friend functions, input and output streams, templates, and exceptions. Laboratory course.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS201) or instructor's consent - This course covers the elements of object-oriented programming and the Java Programming Language. Primitive data types, control structures, methods, classes, arrays and strings, inheritance and polymorphism, interfaces, creating user interfaces, applets, exceptions and streams. Laboratory course. Pre-req: METCS201 or instructor's consent. For undergraduate students: This course may not be taken in conjunction with METCS520. Only one of these courses can be counted towards degree requirements.   [ 4 cr. ]

Undergraduate Prerequisites: high school algebra. - Fundamentals of logic (the laws of logic, rules of inferences, quantifiers, proofs of theorems), Fundamental principles of counting (permutations, combinations), set theory, relations and functions, graphs, trees and sorting.  [ 4 cr. ]

This course introduces basic concepts in discrete mathematics, computer systems and programming that are necessary for modern computing systems. It also develops analytic and logical thinking and prepares students to take graduate-level courses in software development degree. This course first reviews the basic concepts in discrete mathematics including logic, sets, functions, relations and combinatorics. Then it discusses the fundamental concepts in computer systems such as computer organization, basic OS concepts, CPU scheduling, memory management, process management and synchronization. Concurrently with the above mathematics and systems studies, programming concepts are introduced and practiced throughout the whole course using Python. Restriction: Not for CS undergraduate students  [ 4 cr. ]

Undergraduate Prerequisites: (METCS231) or instructor's consent - Covers data structures, using the C++ language. Topics include data abstraction, encapsulation, the use of recursion, creation and manipulation of various data structures; bags, lists, queues, tables, trees, heaps and graphs, and searching and sorting algorithms. Laboratory course. Prereq: MET CS231 or instructor's consent.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS232) consent from instructor. - This course covers data structures using the Java Programming Language. Topics include data abstraction, encapsulation, information hiding, and the use of recursion, creation and manipulation of various data structures: lists, queues, tables, trees, heaps, and graphs, and searching and sorting algorithms. Laboratory course. Effective Fall 2020, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. Prerequisite: MET CS232 or instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS201) or consent of the instructor - Computer-based management information systems. Management's role in development and use of computer systems. Planning for a comprehensive information system; role in decision making, case studies.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS231 OR METCS232) or instructor's consent - This course focuses on building core competencies in web design and development. It begins with a complete immersion into HTML essentially XHTML and Dynamic HTML (DHTML). Students are exposed to Cascading Style Sheets (CSS), as well as Dynamic CSS. The fundamentals of JavaScript language including object-oriented JavaScript is covered comprehensively. AJAX with XML and JSON are covered, as they are the primary means to transfer data from client and server. Prereq: METCS231 OR METCS232 or instructor's consent.  [ 4 cr. ]

Comprehensive coverage of object-oriented programming with cooperating classes. Implementation of polymorphism with inheritance and interfaces and in Java library containers. Programming with exceptions, stream input/output and graphical AWT and Swing components. Threads, sockets, datagrams and database connectivity are also covered in this course. Laboratory course. Prerequisite: MET CS 341 or MET CS 342. Or instructor's consent. For undergraduates only. Effective Fall 2020, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS200) or instructor's consent. ; Undergraduate Corequisites: May not be taken in conjunction with CS 535 or CS 625. Only one of the se courses can be counted towards degree requirements. - Basic concepts of data communications and computer networks; hardware, software, and reference models; TCP/IP protocol suit. Overview of voice communication, LAN, network development life cycle, security, management IT Economic: Total Cost Ownership, Return on investment and IT Project Portfolio Management. Prereq: MET CS200 or instructor's consent. May not be taken in conjunction with CS 535 or CS 625. Only one of these courses can be counted towards degree requirements.  [ 4 cr. ]

This course provides comprehensive overview of IT Project Management and the key processes associated with planning, organizing and controlling of software Projects. The course will focus on various knowledge areas such as: project scope management, risk management, quality management, communications management and integration management. Students will be required to submit a term paper. Effective Fall 2020, this course fulfills a single unit in the following BU Hub area: Teamwork/Collaboration.  [ 4 cr. ]

Undergraduate Corequisites: Undergraduate students can not take any combination of courses from th e list: CS 469, CS 579, CS 669. Only one of these courses can be coun ted toward their requirements. - Students learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Students gain extensive hands- on experience using Oracle or Microsoft SQL Server as they learn the Structured Query Language (SQL) and design and implement databases. Topics covered include: the relational and entity-relational models, data modeling, normalization, object modeling, SQL, advanced SQL, stored procedures, triggers, database design, database lifecycle, and transactions. Students are introduced to advanced topics including performance tuning, distributed databases, replication, business intelligence, data warehouses, internet databases, database administration, security, backup and recovery. Students design and implement a database system as a term project. Laboratory Class. Restrictions: This course may not be taken in conjunction with MET CS 669 or MET CS 579. Only one of these courses can be counted towards degree requirements.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS231 OR METCS232) or instructor's consent - Computer organization with emphasis on processors, memory, and input/output. Includes pipelining, ALUs, caches, virtual memory, parallelism, measuring performance, and basic operating systems concepts. Discussion of assembly language instruction sets and programming as well as internal representation of instructions. Prereq: MET CS 231 or MET CS 232; or instructor's consent  [ 4 cr. ]

Undergraduate Prerequisites: (METCS342) or instructor's consent - Techniques for the construction of reliable, efficient, and cost-effective software. Requirement analysis, software design, programming methodologies, testing procedures, software development tools, and management issues. Students plan, design, implement, and test a system in a group project. Laboratory course. Prereq: MET CS 342 and instructor's consent to verify programming coursework. Effective Fall 2020, this course fulfills a single unit in each of the following BU Hub areas: Digital/Multimedia Expression, Oral and/or Signed Communication, Teamwork/Collaboration.  [ 4 cr. ]

Undergraduate Prerequisites: consent of advisor. - Independent study on special projects under faculty guidance.   [ 4 cr. ]

Undergraduate Prerequisites: consent of advisor. - Independent study on special projects under faculty guidance.  [ Var cr. ]

This course provides graduate students with the opportunity to seek internships. The chosen internship must be related to the student's specialization of study. Students enrolled in the course will be individually supervised by a faculty member from the Department of Computer Science. This course may not be taken until the student has completed at least six courses towards their master's program. Graduate standing in MS programs offered by the MET Department of Computer Science is required. The internship credits cannot be applied toward the MS degree program.   [ Var cr. ]

Prerequisites: METCS 200 or 300 or Instructor's Consent. Not recommended for students without a programming background. - This course covers the concepts of object-oriented approach to software design and development using the Java programming language. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, applets, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, interfaces, creating user interfaces, exceptions, and streams. Upon completion of this course the students will be able to apply software engineering criteria to design and implement Java applications that are secure, robust, and scalable  [ 4 cr. ]

This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking.

Prerequisite: Programming experience in any language. Or Instructor's consent.  [ 4 cr. ]

This course covers and relates fundamental components of programs. Students use various data structures to solve computational problems, and implement data structures using a high-level programming language. Algorithms are created, decomposed, and expressed as pseudocode. The running time of various algorithms and their computational complexity are analyzed. Prerequisite: MET CS300 and either MET CS520 or MET CS521, or instructor's consent.  [ 4 cr. ]

Graduate Prerequisites: MET CS 248 and MET CS 341 or MET CS 342 or consent of the instructor - This course is primarily the study of design of graphic algorithms. At the end of the course you can expect to be able to write programs to model, transform and display 3- dimensional objects on a 2-dimensional display. The course starts with a brief survey of graphics devices and graphics software. 2-d primitives such as lines and curves in 2- d space are studied and a number of algorithms to draw them on a rectangular surface are introduced, followed by a study of polygons, scan conversion and other fill methods. Attributes of the primitives are studied as well as filtering and aliasing. Geometric transformations in 2 dimensions are introduced in homogeneous coordinates, followed by the viewing pipeline, which includes clipping of lines, polygons and text. Hierarchical graphics modeling is briefly studied. The graphics user interface is introduced and various input functions and interaction modes are examined. 3-d graphics is introduced through object representations through polygonal methods, spline techniques, and octrees. This is followed by 3-d transformations and the 3-d viewing pipeline. The course ends with a study of algorithms to detect the visible surfaces of a 3-d object in both the object space and the image space. Laboratory Course. Prereq: MET CS 248 and MET CS 341 or MET CS 342. Or instructor's consent.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS575) ; Undergraduate Corequisites: Undergraduate students can not take any combination of courses from th e list: CS 425, CS 535, CS 625. Only one of these courses can be coun ted toward their requirements. - This course provides a robust understanding of networking. It teaches the fundamentals of networking systems, their architecture, function and operation and how those fundamentals are reflected in current network technologies. Students will learn the principles that underlie all networks and the application of those principles (or not) to current network protocols and systems. The course explains how layers of different scope are combined to create a network. There will be a basic introduction to Physical Media, the functions that make up protocols, such as error detection, delimiting, lost and duplicate detection; and the synchronization required for the feedback mechanisms: flow and retransmission control, etc. Students will be introduced to how these functions are used in current protocols, such as Ethernet, WiFi, VLANs, TCP/IP, wireless communication, routing, congestion management, QoS, network management, security, and the common network applications as well as some past applications with unique design solutions. Prereq: MET CS 575 and MET CS 201 or MET CS 231 or MET CS 232. Or instructor's consent. Restrictions: This course may not be taken in conjunction with MET CS 625 or MET CS 425 (undergraduate). Only one of these courses can be counted towards degree requirements.  [ 4 cr. ]

Formerly titled CS 544 Foundations of Analytics with R.
The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: MET CS546 and (MET CS520 or MET CS521), or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: Academic background that includes the material covered in a standard c ourse on college algebra. - The goal of this course is to provide students with the mathematical fundamentals required for successful quantitative analysis of problems. The first part of the course introduces the mathematical prerequisites for understanding probability and statistics. Topics include combinatorial mathematics, functions, and the fundamentals of differentiation and integration. The second part of the course concentrates on the study of elementary probability theory, discrete and continuous distributions. Prereq: Academic background that includes the material covered in a standard course on college algebra or instructor's consent. For undergraduate students: This course may not be taken in conjunction with MET MA 213, only one of these courses will count toward degree program requirements. Students who have taken MET MA 113 as well as MET MA 123 will also not be allowed to count MET CS 546 towards degree requirements.  [ 4 cr. ]

Undergraduate Prerequisites: Basic knowledge of Python or R; or consent of instructor. - Mathematics is fundamental to data science and machine learning. In this course, you will review essential mathematical concepts and fundamental procedures illustrated by Python and/or R code and visualizations. Computational methods for data science presented through accessible, self-contained examples, intuitive explanations, and visualization will be discussed. Equal emphasis will be placed on both mathematics and computational methods that are at the heart of many algorithms for data analysis and machine learning. You will also advance your mathematical proficiency enabling you to effectively apply your skills to data analytics and machine learning.  [ 4 cr. ]

Formerly titled CS 555 Data Analysis and Visualization with R.
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

This course presents financial algorithms used in applications of computer science in financial decision analysis, risk management, data mining and market analysis, and other modern business processes. The course covers theoretical background on probabilistic methods used for financial decision making and their application in number of fields such as financial modeling, venture capital decision making, operational risk measurement and investment science. Number of financial applications and algorithms are being presented for portfolio risk analysis, modeling real options, venture capital decision making, etc. The course concludes with algorithms for financial risk assessment and presents the security concepts and challenges of financial information systems.   [ 4 cr. ]

Undergraduate Prerequisites: (CS341 or CS342 or CS526) or instructor's consent - earn basic methods for designing and analyzing efficient computer algorithms and practice hands-on programming skills. Topics include sorting, searching, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, string matching, and NP-completeness.  [ 4 cr. ]

This course is designed for IT professionals, and those training to be IT professionals, who are preparing for careers in healthcare-related IT (Health Informatics). This course provides a high-level introduction into basic concepts of biomedicine and familiarizes students with the structure and organization of American healthcare system and the roles played by IT in that system. The course introduces medical terminology, human anatomy and physiology, disease processes, diagnostic modalities, and treatments associated with common disease processes. IT case studies demonstrate the key roles of health informatics and how IT tools and resources help medical professionals integrate multiple sources of information to make diagnostic and therapeutic decisions.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS472) and (CS 231 or CS 232) or instructor's consent - Overview of operating system characteristics, design objectives, and structures. Topics include concurrent processes, coordination of asynchronous events, file systems, resource sharing, memory management, security, scheduling and deadlock problems. Prereq: MET CS472, and MET CS231 or MET CS232, or instructor's consent.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS231 OR METCS232) or consent of instructor. ; Undergraduate Corequisites: Restrictions: This course may not be taken in conjunction with CS 669 or CS 469 (undergraduate). Only one of these courses can be counted to wards degree requirements. - This course provides a theoretical yet modern presentation of database topics ranging from Data and Object Modeling, relational algebra and normalization to advanced topics such as how to develop Web-based database applications. Other topics covered - relational data model, SQL and manipulating relational data; applications programming for relational databases; physical characteristics of databases; achieving performance and reliability with database systems; object- oriented database systems. Prereq: MET CS 231 or MET CS 232; or instructor's consent. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 669. Refer to your Department for further details.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS570) - This course presents the fundamental principles, concepts, and technological elements that make up the building blocks of Health Informatics. It introduces the characteristics of data, information, and knowledge in the domain, the common algorithms for health applications, and IT components in representative clinical processes. It presents the conceptual framework for handling biomedical data collection, storage, and optimal use. It covers the concepts of population health and precision medicine and the information systems that support them. It introduces basic principles of knowledge management systems in biomedicine, various aspects of Health Information Technology standards, and IT aspects of clinical process modeling. Students design a simple Health Informatics solution as a term project.  [ 4 cr. ]

Health Information Systems are comprehensive application systems that automate the activities of healthcare delivery including clinical care using electronic health records (EHRs), coordination of care across providers, telehealth, management of the business of healthcare such as revenue cycle management, and population health management. The course covers the functionality of these systems, the underlying information technology they require and their successful operations. It addresses challenges in this rapidly changing field such as complex data, security, interoperability, mobile technology and distributed users. The course emphasizes applied use of health information systems through case studies, current articles, and exercises.  [ 4 cr. ]

Laws, regulations, and ethics guide the practice of health information management (HIM) and health informatics (HI). This course introduces students to the workings of the American legal system and concepts and theories of ethics, examines the legal, ethical, and regulatory issues that impact the protection of confidentiality and integrity of patient information, and, on the other hand, the improvement of accessibility of patient information to enable healthcare providers to make informed decision based on complete patient data. We will cover laws and regulations that are central to the HIM and HI professions, including Privacy Act of 1974, the Health Insurance Portability and Accountability Act (HIPAA), the Genetic Information Nondiscrimination Act of 2008 (GINA), the Health Information Technology for Economic and Clinical Health (HITECH) Act, the Food and Drug Administration Safety and Innovation Act (FDASIA), the 21st Century Cures Act, and the Confidentiality of Alcohol and Drug Abuse Patient Records Regulations, and more. The goal is to enable HIM and HI practitioners to make effective and informed decisions that prompt patient safety and care quality improvement.   [ 4 cr. ]

Spring 2024 Course Description: The course introduces basic business concepts in biomedical, biotech and health information technology entrepreneurship and provides a hands-on experience in creating, proposing and justifying a business model for a healthcare or a biotech startup. Foundational study and research of entrepreneurship, business models, international healthcare systems and innovation compose the first three modules of the course. For the final two modules, students work in teams to propose founder roles, business ideas and analysis leading to a business plan. After providing market needs and competitive analysis of proposals, they visualize and assess overall business models, including strengths, weaknesses, opportunities and threats analysis. Finally, they present their business models including the empathy map and the canvas blocks, defending their business proposal.  [ 4 cr. ]

In this course we will study the fundamental and design applications of various biometric systems based on fingerprints, voice, face, hand geometry, palm print, iris, retina, and other modalities. Multimodal biometric systems that use two or more of the above characteristics will be discussed. Biometric system performance and issues related to the security and privacy aspects of these systems will also be addressed.   [ 4 cr. ]

Prerequisites: WAD 100 - Learn essential front-end development skills, starting with foundational JavaScript techniques, such as DOM manipulation and event handling, and advancing to interactive web technologies like HTML's Drag and Drop, Canvas, and SVG. You will be exposed to asynchronous operations, including AJAX, the Fetch API, and Web Workers, and learn to craft responsive designs using Flexbox, CSS Grid, and advanced CSS selectors. A comprehensive exploration of TypeScript and its main feature, static typing, and capabilities will also be covered.  The course concludes with a comprehensive dive into ReactJS, covering its core architectural concepts, component-based structure, and state management techniques  [ 4 cr. ]

Prerequisite: MET CS 601 Or instructor's consent. - The Server-Side Web Development course concentrates primarily on building full stack applications using the state of the art tools and frameworks. The course is divided into various modules covering in depth the following topics: NodeJS, Express, React, MongoDB, Mongoose ODM, Sequelize ORM, REST and GraphQL APIs, and application security. Along with the fundamentals underlying these technologies, several applications will be showcased as case studies. Students work with these technologies starting with simple applications and then examining real world complex applications. At the end of this course, students would have mastered developing the full stack applications using the MERN stack and related technologies.  [ 4 cr. ]

Polymorphism, containers, libraries, method specifications, large-scale code management, use of exceptions, concurrent programming, functional programming, programming tests. Java will be used to illustrate these concepts. Students will implement a project or projects of their own choosing, in Java, since some concepts are expressible only in Java. Prerequisite: MET CS 342 or equivalent knowledge of Java. Or MET CS 521 and MET CS 526. Or instructor's consent. Effective Fall 2020, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking.  [ 4 cr. ]

Undergraduate Prerequisites: On Campus Prerequisites: MET CS 200 Fundamentals of Information Techno logy. Or instructor^s consent. ; Undergraduate Corequisites: Restrictions: MS CIS only. This course may not be taken in conjunction with CS 425 (undergraduate) or CS 535. Only CS 535 or CS 625 can be c ounted towards degree requirements. - This course presents the foundations of data communications and takes a bottom-up approach to computer networks. The course concludes with an overview of basic network security and management concepts. Prereq: MET CS 200, or instructor's consent. This course may not be taken in conjunction with MET CS 425 (undergraduate) or MET CS 535. Only one of these courses can be counted towards degree requirements.  [ 4 cr. ]

This course provides students with a comprehensive overview of the principles, processes, and practices of software project management. Students learn techniques for planning, organizing, scheduling, and controlling software projects. There is substantial focus on software cost estimation and software risk management. Students will obtain practical project management skills and competencies related to the definition of a software project, establishment of project communications, managing project changes, and managing distributed software teams and projects. Effective Fall 2020, this course fulfills a single unit in the following BU Hub area: Teamwork/Collaboration.  [ 4 cr. ]

Theory and practice of security and quality assurance and testing for each step of the software development cycle. Verification vs. validation. Test case design techniques, test coverage criteria, security development and verification practices, and tools for static and dynamic analysis. Standards. Test-driven development. QA for maintenance and legacy applications. From a project management knowledge perspective, this course covers the methods, tools and techniques associated with the following processes -- Plan Quality, Perform Quality Assurance, and Perform Quality Control.   [ 4 cr. ]

This course provides students with a comprehensive overview of the principles, processes, and practices of agile software development. Students learn techniques for initiating, planning and executing on software development projects using agile methodologies. Students will obtain practical knowledge of agile development frameworks and be able to distinguish between agile and traditional project management methodologies. Students will learn how to apply agile tools and techniques in the software development lifecycle from project ideation to deployment, including establishing an agile team environment, roles and responsibilities, communication and reporting methods, and embracing change. We also leverage the guidelines outlined by the Project Management Institute for agile project development as a framework in this course.   [ 4 cr. ]

Graduate Prerequisites: (METCS231 & METCS232 & METTC535) CS 231 or CS 232 and TC 535 or consent of the instructor. - The purpose of this course is to provide students with a deeper understanding of Media-specific Technologies not only so that they will be able to use the ones covered in this course, but more importantly be able to analyze and evaluate new technologies. This course applies the principles from CS 535 to understand the engineering that lead to them as well as the special problems that confront network technologies that operate directly over the physical media. These Media specific layers have three problems to solve: the usual one of multiple users of a common resource, accommodating the particular characteristics of the media, and providing (to the degree possible) a media- independent service to the layers above. While CS 535 provides a high-level view of some of these technologies, in this course, they are considered in much greater detail as to how these technologies address their requirements and take advantage of the assumptions made. The emphasis is on those technologies that are either representative of a type or take a unique perspective on the problem. Hence, the traditional data link protocols, such as HDLC, modern Ethernet (primarily VLANs), WiFi (802.11) represent the first type, while media technologies, such as DOCSIS, RFIDs, IoT, and cellular mobile networks are representative of the second. The course will consider how these technologies solve mobility, routing, congestion, QoS (multi-media), security, etc. A major project is part of this course. Prereq: MET CS 231 or MET CS 232 and either MET CS 625 or MET CS 535; or instructor's consent.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS566) or instructor's consent - Theory of finite automata and regular expressions and properties of regular sets. Context- free grammars, context-free languages, and pushdown automata. Turing machines, undecidability problems, and the Chomsky hierarchy. Introduction to computational complexity theory and the study of NP-complete problems. Prerequisite: MET CS 248 or instructor's consent.  [ 4 cr. ]

Graduate Prerequisites: MET CS 248 and MET CS 341 or MET CS 342. - Study of the ideas and techniques that enable computers to behave intelligently. Search, constraint propagations, and reasoning. Knowledge representation, natural language, learning, question answering, inference, visual perception, and/or problem solving. Laboratory course. Prereq: MET CS 341, MET CS 342, MET CS 520 or MET CS 521. Or instructor's consent.  [ 4 cr. ]

Graduate Prerequisites: (METCS341 or METCS342 and METCS565) or consent of the instructor - Software design principles, the object-oriented paradigm, unified modeling language; creational, structural, and behavioral design patterns; OO analysis and design; implementation of semester project. Laboratory course. Prereq: (MET CS 526 or MET CS 622) and one of the following (MET CS 341, MET CS 342, MET CS 520, or MET CS 521). Or instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: Restrictions: Only for MS CIS. This course may not be taken in conjunc tion with MET CS 469 (undergraduate) or MET CS 579. Only one of these courses can be counted towards degree requirements. - Students learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Students gain extensive hands- on experience using Oracle or Microsoft SQL Server as they learn the Structured Query Language (SQL) and design and implement databases. Students design and implement a database system as a term project. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 579. Only one of these courses can be counted towards degree requirements.   [ 4 cr. ]

Undergraduate Prerequisites: MET CS342 and at least one 500-level computer programming-intensive sc ience course (or instructor's consent). MET CS 564 or MET CS 565 are r ecommended. - Overview of techniques and tools to develop high quality software. Topics include software development life cycle such as Agile and DevOps, requirements analysis, software design, programming techniques, refactoring, testing, as well as software management issues. This course features a semester-long group project where students will design and develop a real world software system in groups using Agile methodology and various SE tools, including UML tools, project management tools, programming frameworks, unit and system testing tools , integration tools and version control tools.

Graduate Prerequisites: CS 579 or CS 669 or consent of the instructor - The course provides a strong foundation in database security and auditing. This course utilizes Oracle scenarios and step-by-step examples. The following topics are covered: security, profiles, password policies, privileges and roles, Virtual Private Databases, and auditing. The course also covers advanced topics such as SQL injection, database management security issues such as securing the DBMS, enforcing access controls, and related issues. Prereq: MET CS 579 or MET CS 669; or instructor's consent.  [ 4 cr. ]

Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent.   [ 4 cr. ]

Undergraduate Prerequisites: Basic programming knowledge or instructor's consent. - Object-oriented methods of information systems analysis and design for organizations with data- processing needs. System feasibility; requirements analysis; database utilization; Unified Modeling Language; software system architecture, design, and implementation, management; project control; and systems-level testing. Prerequisite: Basic programming knowledge or instructor's consent.  [ 4 cr. ]

Graduate Prerequisites: (METCS342) or instructor's consent. - This course discusses the principles and issues associated with mobile application development using Android as the development platform. Topics covered will include Android application components (Activities, Services, Content Providers and Broadcast Receivers), ICC (Inter-component Communication), UI design, data storage, asynchronous processing, 2D graphics, and Android security. Students will develop their own apps in Java and/or Kotlin using Android Studio in their semester-long projects. Prior knowledge of Java programming is required. Prerequisite: MET CS 342 OR MET CS 520 OR MET CS 521. Or instructor's consent.  [ 4 cr. ]

This course covers important topics that students need to understand in order to effectively manage a successful cybersecurity and privacy program, including governance, risk management, asset classification and incidence response. Students are first introduced to cybersecurity & privacy policy frameworks, governance, standards, and strategy. Risk tolerance is critical when building a cybersecurity and privacy program that supports business goals and strategies. Risk management fundamentals and assessment processes will be reviewed in depth including the methodology for identifying, quantifying, mitigating and controlling risks. Asset classification and the importance of protecting Intellectual Property (IP) will prepare students to understand and identify protection mechanisms needed to defend against malicious actors, including industry competitors and nation states. Incident Response programs will cover preparation and responses necessary to triage incidents and respond quickly to limit damage from malicious actors.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS535 OR METCS625) or instructor's consent - . This course will cover contemporary integrated network management based on FCAPS (Fault, Configuration, Administration, Performance, and Security management) model. The introduction to the course will be an overview of data transmission techniques and networking technologies. The middle part of the course will be on Network Management Model, SNMP versions 1, 2 and 3, and MIBs. In the second part of the course, particular focus and emphasis will be given to current network management issues: various wireless networks technologies (WLAN, WiFi, WiMax), Voice-over-IP, Peer-to-Peer Networks, networking services, Identity Management, and Services Oriented Architecture Management. Prereq: MET CS 535 or MET CS 625. or instructor's consent.  [ 4 cr. ]

Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. - The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms, link analysis and community detection based on graphs. Laboratory Course.  [ 4 cr. ]

Graduate Prerequisites: CS 579 or CS 669 or consent of the instructor - This course surveys state-of-the art technologies in DW and Big Data. It describes logical, physical and semantic foundation of modern DW infrastructure. Students will create a cube using OLAP and implement decision support benchmarks on Hadoop/Spark vs Vertica database. Upon successful completion, students will be familiar with tradeoffs in DW design and architecture. Prereq: MET CS 579 or MET CS 669 and either MET CS 520 or MET CS 521. Or instructor's consent.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS535 OR METCS625) or instructor's consent. - This course will cover advanced network security issues and solutions. The main focus on the first part of the course will be on Security basics, i.e. security services, access controls, vulnerabilities, threats and risk, network architectures and attacks. In the second part of the course, particular focus and emphasis will be given to network security capabilities and mechanisms (Access Control on wire-line and wireless networks), IPsec, Firewalls, Deep Packet Inspection and Transport security. The final portion of the course will address Network Application security (Email, Ad-hoc, XML/SAML and Services Oriented Architecture security. As part of our course review we will explore a number of Network Use Cases. Prereq: MET CS 535 or MET CS 625; Familiarity with OSI and TCP/IP protocol stack; Background-familiarity with binary numbers, prime numbers, binary- hexadecimal-decimal conversions, etc; Familiarity with computer programming concepts; or instructor's consent.  [ 4 cr. ]

Provides a comprehensive understanding of digital forensics and investigation tools and techniques. Learn what computer forensics and investigation is as a profession and gain an understanding of the overall investigative process. Operating system architectures and disk structures are discussed. Studies how to set up an investigator's office and laboratory, as well as what computer forensic hardware and software tools are available. Other topics covered include importance of digital evidence controls and how to process crime and incident scenes, details of data acquisition, computer forensic analysis, e-mail investigations, image file recovery, investigative report writing, and expert witness requirements. Provides a range of laboratory and hands-on assignments either in solo or in teams. With rapid growth of computer systems and digital data this area has grown in importance. Prereq: Working knowledge of windows computers, including installing and removing software. Access to a PC meeting the minimum system requirements defined in the course syllabus.  [ 4 cr. ]

Overview of mobile forensics investigation techniques and tools. Topics include mobile forensics procedures and principles, related legal issues, mobile platform internals, bypassing passcode, rooting or jailbreaking process, logical and physical acquisition, data recovery and analysis, and reporting. Provides in-depth coverage of both iOS and Android platforms. Laboratory and hands-on exercises using current tools are provided and required.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS625) or instructor's consent - This course introduces fundamental concepts, principles of cybersecurity and their use in the development of security mechanisms and policies. Topics include basic risk assessment and management; basic legal and ethics issues, various cyber attacks, defense methods and tools; security principles, models and components; different crypto protocols, techniques and tools, including symmetric and asymmetric encryption algorithms, hashing, public key infrastructure, and how they can be used; security threats and defense to hardware, operating systems, networks and applications in modern computing environments. Hands-on labs using current tools are provided and required. Prerequisite: METCS535 or METCS625 or instructor's consent.  [ 4 cr. ]

The course MET CS 697 Special Topics in Computer Science changes from semester to semester. More than one CS697 can be offered in a given semester. Course descriptions for all sections are listed below. For more information, please contact MET Department of Computer Science.

Fall 2020: Topic: IoT Security
This course overviews the security issues in IoT. We will learn the IoT architecture, the interaction among the three major components of IoT, mobile, IoT device and cloud, and general threats in IoT. For each component, we will study the platform architecture, existing threats and countermeasures. Regarding mobile, we will discuss its platform architecture, followed by the security model and the common threats. Regarding IoT devices, we will focus on protocol (OAuth and MQTT) analysis and firmware analysis. Regarding cloud, we will first overview its cornerstone, virtualization and its security issues, and then discuss existing challenges in building trusted cloud platform. Finally, case studies of connect vehicle and smart home will be discussed. The course aims to provide foundations for students to understand the threats, vulnerabilities and defense mechanisms in IoT. Hands on lab exercises are included. Prereq: MET CS 695. Or, instructor's consent. The students are expected to have background on operating system internals and security fundamentals. This course is not a programming-intensive course. However, hands-on labs will be assigned. Students can also choose programming-intensive project.   [ 4 cr. ]

Prerequisites: MET CS 521 & MET CS 546; MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. Students learn underlying theories of data mining algorithms in the class and they practice those algorithms through assignments and a semester-long class project using R. After finishing this course, students will be able to independently perform data mining tasks to solve real-world problems.  [ 4 cr. ]

Undergraduate Prerequisites: MET CS 520 or MET CS 601 and programming experience, or instructor's c onsent - The Rich Internet Application (RIA) Development course concentrates primarily on building rich client web applications in the browser for desktop and mobile devices. The course is divided into various modules covering in depth the following technologies: HTML5, AngularJS, and Ionic framework. Along with the fundamentals underlying these technologies, several applications will be showcased as case studies. Students work with these technologies starting with simple applications and then examining real world complex applications. At the end of this course, students would have mastered the latest and widely used RIA methodologies. Course Prerequisites: METCS520 (Information Structures) and METCS601 (Web Application Development), or instructor's consent.  [ 4 cr. ]

Graduate Prerequisites: MET TC 250 or MET CS 248 or MET OM 501. - Overview of techniques and tools to develop secure software. Focus on the application security. Topics include secure software development processes, threat modeling, secure requirements and architectures, vulnerability and malware analysis using static code analysis and dynamic analysis tools, vulnerabilities in C/C++ and Java programs, Crypto and secure APIs, vulnerabilities in web applications and mobile applications, and security testing. Hands-on lab and programming exercises using current tools are provided and required. Prerequisite: At least two 500- level (or above) programming-intensive computer science courses; or instructor's consent.   [ 4 cr. ]

Graduate Prerequisites: MET CS 521; MET CS 622, MET CS 673 or MET CS 682; MET CS 677 strongly recommended; or consent of instructor. - Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN’s, Neural Nets and Deep Learning, Recurrent Neural Nets, Rule-learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptrons, backpropagation, attention, and transformers. Each student focuses on two of these approaches and creates a term project.  [ 4 cr. ]

Graduate Prerequisites: (METCS535) or consent of the instructor - This seminar course provides a strong foundation in networking and Internet architecture, data transfer protocols, including TCP, SCTP, QUIC, and IPv6, and a deep look at network resource allocation with an emphasis on protocol- independent hardware for Deep Packet Inspection (DPI) and congestion management. The course goes into greater depth of current topics such as: naming and addressing, synchronization, congestion management and resource allocation (routing) and how they manifest in different environments. There will be assigned readings from the professor that require considerable class participation, both in presenting material and discussing it.

Prereq: MET CS 535 OR MET CS 625,or instructor's consent required.  [ 4 cr. ]

This course is an introduction to large-scale data analytics. Big Data analytics is the study of how to extract actionable, non-trivial knowledge from massive amount of data sets. This class will focus both on the cluster computing software tools and programming techniques used by data scientists, as well as the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, we will cover the basics systems and techniques to store large-volumes of data, as well as modern systems for cluster computing based on Map-Reduce pattern such as Hadoop MapReduce, Apache Spark and Flink. Students will implement data mining algorithms and execute them on real cloud systems like Amazon AWS, Google Cloud or Microsoft Azure by using educational accounts. On the data mining models side, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side.
Prerequisite: MET CS 521, MET CS 544 and MET CS 555. Or, MET CS 677. Or, Instructor's consent.   [ 4 cr. ]

Graduate Prerequisites: (METCS579 OR METCS669) or consent of the instructor - This course covers advanced aspects of database management including normalization and denormalization, query optimization, distributed databases, data warehousing, and big data. There is extensive coverage and hands on work with SQL, and database instance tuning. Course covers various modern database architectures including relational, key value, object relational and document store models as well as various approaches to scale out, integrate and implement database systems through replication and cloud based instances. Students learn about unstructured "big data" architectures and databases, and gain hands-on experience with Spark and MongoDB. Students complete a term project exploring an advanced database technology of their choice. Prereq: MET CS 579 or MET CS 669; or instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS570) - This course presents the details of information processing in hospitals, hospital information systems (HIS), and more broadly health information systems. It presents the architecture, design, and user requirements of information systems in health care environment. It focuses on Information Technology aspects of Health Informatics specifically addressing the design, development, operation, and management of HIS. The first part of this course covers the introductory concepts including information processing needs, and information management in health care environment. The second part covers detailed description of HIS including hospital process modeling, architecture, quality assessment, and applicable tools. The final part of the course covers management of HIS and related issues and extension of this topic to other health care organizations. The course will have a term project providing students a hands-on experience in design and research of HIS. Prereq: MET CS 580; or instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: Restrictions: Only for MS CIS students. - This course describes and compares contemporary and emerging information technology and its management. Students learn how to identify information technologies of strategic value to their organizations and how to manage their implementation. The course highlights the application of I.T. to business needs. CS 782 is at the advanced Masters (700) level, and it assumes that students understand IT systems at the level of CS 682 Systems Analysis and Design. Students who haven't completed CS 682 should contact their instructor to determine if they are adequately prepared. Prereq: MET CS 682, or instructor's consent.   [ 4 cr. ]

Graduate Prerequisites: (METCS682) or strategic IT experience or instructor's consent - This course builds upon the strong technical foundation of our MSCIS and MSCS curricula, by providing students with the CIO-level management perspective and skills of an enterprise architect, in the context of the technologies that implement those architectures. Current technologies and processes explored in the enterprise architecture context include blockchain, microservices, multimodal/analytic databases, DevOps, SAFe (Scaled Agile Framework), containers/Docker, and some leverage of AI techniques. We cover both the migration of legacy enterprise systems and de novo enterprise architecture development, vendor selection and management, cybersecurity in the enterprise, and complex system integration. Enterprise architecture decisions are presented in the context of the business goals and alignment that are critical for success, given globalization and the reality that "all companies are now technology companies." The course content is rich with case studies that illustrate practical application of enterprise architecture approaches and lessons learned. The course also includes a number of realistic enterprise architecture assignments and an incremental term project with components spanning the course, to provide students with hands on enterprise architecture experience. Students develop the understanding and skills needed to define and implement successful enterprise architectures that provide real strategic and concrete value to organizations, such as substantially reducing IT costs while improving performance, agility and alignment of information technology to business goals. On-campus classrooms follow a "flipped classroom" format, where significant class time is devoted to in-class group workshops. Prereq: MET CS 682. Or strategic IT experience. Or instructor's consent.  [ 4 cr. ]

Prerequisites: MET CS 677, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or instructor’s consent. - The first part of the course covers statistical concepts required for generative artificial intelligence. We review regressions and optimization methods as well as traditional neural network architectures, including perceptron and multilayer perceptron. Next, we move to Convolutional Neural Networks and Recurrent Neural Networks and close this part with Attention and Transformers. The second part of the course focuses on generative neural networks. We start with traditional self-supervised learning algorithms (Self Organized Map and Restricted Boltzmann Machine), then explore Auto Encoder architectures and Generative Adversarial Networks and move toward architectures that construct generative models, including recent advances in NLP, including LLMs, and Retrieval Augmented Methods. Finally, we describe the Neural Radiance Field, 3D Gaussian Splatting, and text-2-image models.  [ 4 cr. ]

Graduate Prerequisites: (METCS248 & METCS566) or consent of the instructor - The course covers the main concepts and principles of cryptography with the main emphasis put on public key cryptography. It begins with the review of integers and a thorough coverage of the fundamentals of finite group theory followed by the RSA and ElGamal ciphers. Primitive roots in cyclic groups and the discrete log problem are discussed. Baby-step Giant-step and the Index Calculus probabilistic algorithms to compute discrete logs in cyclic groups are presented. Naor -- Reingold and Blum -- Blum -- Shub Random Number Generators as well as Fermat, Euler and Miller-Rabin primality tests are thoroughly covered. Pollard's Rho, Pollard's and Quadratic Sieve factorization algorithms are presented. The course ends with the coverage of some oblivious transfer protocols and zero-knowledge proofs. There are numerous programming assignments in the course. Prereq: MET CS 248, or instructor's consent.  [ 4 cr. ]

Fall 2023 Topic: Generative AI

This course focuses on recent advances in generative AI. It starts by reviewing statistics and regression models related to generative models, then common deep learning methods described. Later, models for designing new content, such as images, music, or text, will be explored, including GAN, VAE, Autoregressive and Diffusion Models. MLP, CNN, RNN, and Transformer models covered in CS 767 are reviewed. Students should be fluent in Python programming and CS 555 and CS 677  [ 4 cr. ]

Prereq: Consent of advisor. Requires prior approval of student-initiated proposal. Independent study on special projects under faculty guidance.
  [ Var cr. ]

Prereq: consent of the instructor. Requires prior approval of student-initiated proposal. Independent study on special projects under faculty guidance. variable cr  [ Var cr. ]

Undergraduate Prerequisites: (METCS789) or instructor's consent - This course builds on the material covered in CS 789 Cryptography. It begins with the coverage of commutative rings, finite fields, rings of polynomials, and finding of the greatest common divisor in the ring of polynomials. Irreducible polynomials are discussed. Field extensions and fields Fᴩ [x]/P are thoroughly covered. The main emphasis is put on elliptic curves over Fᴩ and F₂ and the ElGamal cipher on elliptic curves is presented. Block ciphers DES and double and triple DES are introduced. AES and WHIRLPOOL block ciphers and modes of operation are covered. The course continues with the introduction of message integrity and message authentication. In the last part of the course cryptographic hash functions SHA-512 and WHIRLPOOL as well as various digital signatures are introduced. Finally, entity authentication and key management issues are discussed. Prereq: MET CS 789; or instructor's consent.  [ 4 cr. ]

This thesis must be completed within 12 months. Students majoring in Computer Science may elect a thesis option. This option is available to Master of Science in Computer Science candidates who have completed at least seven courses toward their degree and have a GPA of 3.7 or higher. Students are responsible for finding a thesis advisor and a principal reader within the department. The advisor must be a full-time faculty member; the principal reader may be part-time faculty member with a doctorate. Permission must be obtained by the department. 4cr.  [ 4 cr. ]

This thesis must be completed within 12 months. Students majoring in Computer Science may elect a thesis option. This option is available to Master of Science in Computer Science candidates who have completed at least seven courses toward their degree and have a GPA of 3.7 or higher. Students are responsible for finding a thesis advisor and a principal reader within the department. The advisor must be a full-time faculty member; the principal reader may be part-time faculty member with a doctorate. Permission must be obtained by the department. 4cr.  [ 4 cr. ]