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. ]

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. ]

Prerequisite: MET CS 201 or consent of the instructor. Cover the elements of object-oriented programming and the C++ language, including data types, control structures, functions, library functions, classes, inheritance, and multiple inheritance. You will also study constructors, destructors, function and operator overloading, reference parameters and default values, friend functions, input and output streams, templates, and exceptions.  [ 4 cr. ]

Learn the fundamentals of object-oriented programming and the Java programming language, including primitive data types, control structures, methods, classes, arrays, and strings. You will also explore key concepts and tools such as inheritance, polymorphism, interfaces, exceptions, the Java collections framework, basic data structures, and recursion.  [ 4 cr. ]

Prerequisite: 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: (METCS 231) 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: METCS 231 or instructor's consent.  [ 4 cr. ]

Prerequisites: MET CS 232 or consent of instructor. Learn 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, graphs, and searching and sorting algorithms. 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: (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. ]

Prerequisite: MET CS 342 or consent of instructor. 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. For undergraduate students. 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. ]

Prerequisites: MET LB 102 or consent of instructor. 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, and management. IT Economics: Total Cost Ownership, Return on Investment, and IT Project Portfolio Management. Restrictions: May not be taken in conjunction with MET CS 535 or MET CS 625. Only one of these courses can be counted toward degree requirements.  [ 4 cr. ]

A comprehensive overview of the principles, processes, and practices of software project management, grounded in the latest standards from the Project Management Institute (PMI). You will gain hands-on experience in planning, organizing, scheduling, and controlling software projects, with a strong emphasis on both predictive and adaptive methodologies. In particular, you will explore agile project management with a focus on the Scrum framework and develop practical competencies in business analysis, defining requirements, leading and managing distributed teams, facilitating project communications, handling change management, and assessing risk and cost estimation. A key component of the course involves the design and development of AI-powered applications, equipping you with AI literacy and demonstrating how AI can enhance software project management practices. This course qualifies you to pursue CAPM and PMP credential. Also, this course fulfills the educational requirements necessary to pursue the Certified Associate in Project Management (CAPM)® and Project Management Professional (PMP)® certifications offered by the Project Management Institute (PMI). Effective Fall 2020, this course fulfills a single unit in the following BU Hub area: Teamwork/Collaboration.  [ 4 cr. ]

Learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. You will gain extensive hands-on experience using Oracle or Microsoft SQL Server and Structured Query Language (SQL). Topics include the relational and entity-relational models, data modeling, normalization, object modeling, SQL, advanced SQL, stored procedures, triggers, database design, database lifecycle, and transactions. Advanced topics, including performance tuning, distributed databases, replication, business intelligence, data warehouses, internet databases, database administration, security, backup, and recovery, will be introduced. You will design and implement a database system as a term project. Laboratory course. Restrictions: This course may not be taken in conjunction with MET CS 579 or MET CS 669. Only one of these courses can be counted toward degree requirements.  [ 4 cr. ]

Prerequisites: MET CS 232 or consent of instructor. 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.  [ 4 cr. ]

Prerequisites: MET CS 342 or consent of instructor. 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. 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. ]

Prerequisite: MET LB 102 or consent of instructor. Not recommended for students without a programming background. Explore the concepts of object-oriented approach to software design and development using the Java programming language. You will engage in 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, you 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. ]

Prerequisites: MET CS300 and either MET CS520 or MET CS521, or consent of instructor. 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.  [ 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. ]

Prerequisite: MET CS 575 or consent of instructor. Provides a robust understanding of networking. You will learn the fundamentals of networking systems, their architecture, function, and operation, and how these are reflected in current network technologies. As well as the principles that underlie all networks and their application (or not) to current network protocols and systems. Discover how layers of different scope are combined to create a network and receive 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. In addition, learn 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. 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 toward degree requirements.  [ 4 cr. ]

Prerequisites: MET LB 103, MET LB 104, and (METCS 520 or METCS 521), or equivalent knowledge, or consent of instructor. 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 methods. Data populations using discrete, continuous, and multivariate distributions are explored. Sampling methods and errors during measurements and computations are analyzed in the course. String manipulations and data wrangling methods are examined in detail. The concepts covered in the course are demonstrated using R. Laboratory Course.  [ 4 cr. ]

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. ]

Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. Learn the foundations of machine learning, regression, and classification. Topics include how to describe data, statistical inference, 1 and 2 sample tests of means and proportions, simple linear regression, multiple linear regression, multinomial regression, logistic regression, analysis of variance, and regression diagnostics. These topics are explored using the statistical package R, with a focus on understanding how to use these methods and interpret their outputs and how to visualize the 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 in order to help you learn when and how to deploy different methods.  [ 4 cr. ]

This course presents an overview of modern investment topics. We will start with a survey of the financial markets and the common quantitative technique to value a range of financial instruments. Once the basic blocks of valuation tools are established, the course will discuss the portfolio construction process and risk management with derivative and time series analysis. The course will use Python Jupyter Notebook to illustrate the concept and build visualization for effective communication. By completing the course, students will be able to conduct exploratory data analysis independently and leverage their programming skillset with real-world financial case studies.  [ 4 cr. ]

Prerequisites: MET CS 342 or MET CS 526 or consent of instructor. Learn methods for designing and analyzing algorithms while practicing hands-on programming skills. Topics include divide-and-conquer, sorting, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, and NP-completeness.  [ 4 cr. ]

Designed for current and aspiring IT professionals preparing for healthcare-related IT (Health Informatics) careers, this course provides a high-level introduction to basic concepts of biomedicine and familiarizes students with the structure and organization of the American healthcare system and the role played by IT. Medical terminology, human anatomy and physiology, disease processes, diagnostic modalities, and treatments associated with common disease processes are introduced. IT case studies also 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. ]

Prerequisites: MET CS 232 and MET CS 472 or consent of instructor. 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.  [ 4 cr. ]

Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. 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.  [ 4 cr. ]

Prerequisite: MET CS 232 or consent of instructor. 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 include relational data modeling, SQL, and manipulating relational data; applications programming for relational databases; physical characteristics of databases; achieving performance and reliability with database systems; and object-oriented database systems. 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. ]

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. ]

The course changes from semester to semester. More than one special topics course 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.  [ 4 cr. ]

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. ]

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. ]

Prerequisite: MET WD 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. ]

Prerequisites: (MET CS 342 or equivalent knowledge of Java) or (MET CS 521 and MET CS 526) or consent of instructor. Polymorphism, containers, libraries, method specifications, large-scale code management, use of exceptions, concurrent programming, functional programming, programming tests. Java is used to illustrate these concepts. Students implement a project or projects of their own choosing, in Java, since some concepts are expressible only in Java. 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. ]

Prerequisites: MET LB 102 or consent of instructor. - 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. Restrictions: 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 toward degree requirements.  [ 4 cr. ]

A comprehensive overview of the principles, processes, and practices of software project management, grounded in the latest standards from the Project Management Institute (PMI). Gain hands-on experience in planning, organizing, scheduling, and controlling software projects, with a strong emphasis on both predictive and adaptive methodologies. In particular, the course explores agile project management with a focus on the Scrum framework. You will develop practical competencies in business analysis, defining requirements, leading and managing distributed teams, facilitating project communications, handling change management, and assessing risk and cost estimation. A key component of the course involves the design and development of AI-powered applications, equipping students with AI literacy and demonstrating how AI can enhance software project management practices. This course qualifies you to pursue CAPM and PMP credential. Also, this course fulfills the educational requirements necessary to pursue the Certified Associate in Project Management (CAPM)® and Project Management Professional (PMP)® certifications offered by the Project Management Institute (PMI). Effective Fall 2020, this course fulfills a single unit in the following BU Hub area: Teamwork/Collaboration.  [ 4 cr. ]

Examine software development and software engineering from a project and program management perspective, with a focus on leading agile and distributed teams. You will engage in a term project featuring peer-reviewed milestones and a working prototype. Topics include AI-driven quality assurance (QA), team leadership, and effective collaboration in distributed settings. Additional topics covered in the course include information systems security, ethics, and professional responsibility. No programming background required.  [ 4 cr. ]

This course provides a comprehensive overview of the principles, processes, and practices of agile software development. Students learn how to initiate, plan, and execute software projects using a variety of agile methodologies. The course covers multiple frameworks—including Scrum, Extreme Programming (XP), the Scaled Agile Framework (SAFe), and Lean—and incorporates agile games and simulations to reinforce key concepts. Students gain practical experience with agile tools and techniques across the software development lifecycle, from ideation to deployment. Emphasis is placed on building and leading agile teams, defining roles and responsibilities, fostering effective communication, managing change, and applying Lean principles to maximize value and reduce waste. AI-Powered business analysis is also a core focus, with students learning how to identify stakeholder needs, define and manage requirements, and ensure that solutions deliver business value in agile contexts.  [ 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. ]

Prerequisites: MET CS 566 or consent of instructor. Theory of finite automata, 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.  [ 4 cr. ]

Prerequisites: MET CS 248 and 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.  [ 4 cr. ]

Prerequisites: METCS342 and METCS565 or consent of instructor - Software design principles, the object-oriented paradigm, unified modeling language; creational, structural, and behavioral design patterns; OO analysis and design; software architectures and frameworks; code refactoring. Laboratory course.  [ 4 cr. ]

Learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. You will gain extensive hands-on experience using Oracle or Microsoft SQL Server as you learn the Structured Query Language (SQL) and design and implement databases. You will 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. ]

Prerequisites: At least two programming-intensive courses. Or consent of instructor. Familiarity with OO design concepts and proficiency in at least one high-level programming language is required. Familiarity with web or mobile application development preferred. A comprehensive overview of the entire software development lifecycle, emphasizing modern software architectures, methodologies, practices, and tools. Key topics include agile principles and methodologies such as Scrum and XP, DevOps concepts and practices, CI/CD pipeline, modern software architectures including microservices, REST, and MVC, design patterns, refactoring, software testing, secure software development, and software project management. This course features a semester-long group project where students will design, develop, build, and deploy a real-world software system, applying Agile methodology, DevOps pipeline, and various software tools. This course is better taken as a capstone course towards the end of your program study. 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. ]

The course provides a strong foundation in database security and auditing by utilizing Oracle scenarios and step-by-step examples. The following topics are covered: security, profiles, password policies, privileges, roles, Virtual Private Databases, and auditing. The course also covers advanced topics such as SQL injection, database management, and security issues, such as securing the DBMS, enforcing access controls, and related issues.  [ 4 cr. ]

Prerequisites: Basic programming knowledge or consent of instructor. - 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.  [ 4 cr. ]

Prerequisites: MET CS 342 OR MET CS 520 OR MET CS 521. Or consent of instructor. - Learn the principles, techniques, and issues associated with modern 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), declarative UI design, data storage, asynchronous processing, Android sensing, 2D graphics, and Android security. You will use Kotlin as the main language for Android development and the latest Jetpack APIs. You will also develop your own app in Kotlin using Android Studio as your semester-long project.  [ 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. ]

Prerequisites: METCS535 or METCS625 or consent of instructor. 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.  [ 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. ]

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.  [ 4 cr. ]

Prerequisites: (MET CS 535 or MET CS 625) and (MET CS 595 or MET CY 100) or consent of instructor. This course is designed to provide students with a comprehensive understanding of the fundamental concepts, principles, technologies, and best practices to secure both computer networks and clouds. Topics include an overview of network threats, SSL/TLS, Kerberos, PKI, IPsec, DNSsec, SSH, Firewall, IDS, VPD, electronic mail security, wireless network security, Blockchain, TOR, Cloud architecture, an overview of cloud threats, architecture protection, and data protection in Cloud, IAM, security best practices, etc. Upon the completion of the course, students are expected to know the threats and vulnerabilities that networks and cloud systems face, along with the strategies and tools used to mitigate those risks. Hands-on labs based on existing tools are provided and required.  [ 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. ]

This course, 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.  [ 4 cr. ]

Prerequisites: MET CS 521, MET LB 103 and MET LB 104; and either 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. You will learn underlying theories of data mining algorithms in the class and practice those algorithms through assignments and a semester-long class project using R. After finishing this course, you 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. ]

Prerequisites: MET CS 248 or consent of instructor - Overview of techniques and tools to develop secure software. Focus on the application of 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.  [ 4 cr. ]

Prerequisites: MET CS 577 or consent of instructor. - This course focuses on reinforcement learning, covering fundamental concepts and advanced techniques. It begins with an introduction to reinforcement learning and key concepts, such as exploitation versus exploration and Markov Decision Processes. As the course progresses, it delves into state transition diagrams, the Bellman equation, and solutions to the Multi-Armed Bandits problem. Students will explore challenges and methods related to control and prediction. Then, they learn tabular methods, including Monte Carlo, Dynamic Programming, Temporal Difference Learning, SARSA, and Q-Learning. Afterwards, the course also extends into reviewing neural network concepts, covering convolutional and recurrent neural networks, and moves on to approximation methods for both discrete and continuous spaces, including DQN and its variants. Policy gradient methods, actor-critic methods. Finally, ethical considerations in AI and safety issues are also discussed.  [ 4 cr. ]

Prerequisites: MET CS 521; MET CS 622, MET CS 673 or MET CS 682; MET CS 577 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, Transformers, Recurrent Neural Nets, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptron's, backpropagation, attention, and transformers. Each student creates a term project.  [ 4 cr. ]

Prerequisites: MET CS 535 or consent of 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.  [ 4 cr. ]

Prerequisite: (MET CS 521 & MET CS 544 & MET CS 555) or MET CS 577 or consent of instructor. An introduction to large-scale data analytics, focusing on both the foundational concepts and practical tools used in the field. Big Data analytics involves extracting meaningful, non-trivial insights from vast and complex datasets. You will explore key software tools and programming techniques commonly used by data scientists working with distributed systems. You will also learn core technologies for storing and processing large volumes of data, with a particular emphasis on cluster computing frameworks that follow the MapReduce paradigm, including Hadoop MapReduce and Apache Spark. Through hands-on assignments and projects, you will gain practical experience by implementing data processing algorithms and running them on real-world cloud platforms such as Amazon Web Services (AWS) and Google Cloud, utilizing educational credits and accounts provided for the course.  [ 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. ]

Prerequisites: MET CS 580 or consent of instructor. This course studies health care data and information, health care information systems (HCIS), and explores the challenges of managing information technology (IT). You will learn the architecture, design, and user requirements of information systems in health care, with a focus on IT aspects of Health Informatics, specifically the design, development, operation, and management of HCIS. The first part of the course introduces foundational concepts, including information processing needs and information management in health care environments. Next, you will engage in a detailed examination of HCIS, including hospital process modeling, architecture, quality assessment, and applicable tools. The course concludes by addressing the management of HCIS and related issues, and the extension of these topics to other healthcare organizations. Throughout the course, you will gain hands-on experience by participating in a term project focused on HCIS research and development.  [ 4 cr. ]

Prerequisites: MET CS 682 or instructor's consent. Restrictions: Only for MS CIS students. - Explore and analyze contemporary and emerging information technology and its management. You will learn to identify information technologies that offer strategic value to organizations and acquire skills to manage their successful implementation. The course highlights the application of IT solutions to address business needs. This advanced Master's (700) level course assumes students understand IT systems equivalent to those taught in METCS 682.  [ 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 577 or consent of instructor. This course provides an in-depth exploration of the critical intersection between Artificial Intelligence (AI) and cybersecurity, focusing on two interconnected themes: protecting AI systems from vulnerabilities and harnessing the power of AI to tackle cybersecurity challenges. As AI becomes a cornerstone of modern technology, ensuring the security of AI-powered systems against adversarial attacks, backdoor attacks, and model theft is essential. Simultaneously, AI offers transformative capabilities for malware detection, intrusion prevention, and malware analysis. Through a combination of theoretical foundations, hands-on exercises, and real-world case studies, students will delve into topics such as adversarial machine learning, backdoor injection and defense, IP protection, and privacy-preserving AI. They will also learn how to design and implement AI-driven tools for identifying and mitigating cyber threats in dynamic environments. The course emphasizes practical applications, encouraging students to build resilient AI systems and utilize advanced AI techniques to enhance system security and detect emerging threats. Hands-on labs based on existing tools are provided and required.  [ 4 cr. ]

Prerequisites: MET CS 577, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or consent of instructor. - 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. ]

Prerequisites: (MET CS 248 & MET CS 566) or consent of instructor - The course covers the main concepts and principles of cryptography, with the main emphasis 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.  [ 4 cr. ]

Prerequisites: MET CS 566 or instructor's consent. - Students enrolled in this course will gain comprehensive insights into fundamental and advanced concepts within the dynamic realm of computer vision. The curriculum will focus on cutting-edge applications of deep neural networks in computer vision. Through hands-on experiences and practical exercises, students will learn to leverage computer vision and machine learning techniques to solve real-world challenges. This course not only equips students with theoretical knowledge but empowers them to apply these concepts effectively, fostering a deep understanding of how computer vision can be harnessed to address complex problems in diverse industries.  [ 4 cr. ]

The course changes from semester to semester. More than one special topics course 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.  [ 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. ]

Prerequisites: MET CS 789 or consent of instructor - 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.  [ 4 cr. ]

This is the first course of the two-part thesis option available to Master’s degree program candidates in the Department of Computer Science. You must have completed at least four courses toward your degree and have a grade point average (GPA) of 3.7 or higher. You are responsible for finding a thesis advisor and a principal reader within the department. Please refer to the Department for further details on the application process. Both MET CS 810 Master’s Thesis 1 and MET CS 811 Master’s Thesis 2 must be completed within 12 months.  [ 4 cr. ]

This is the second course of the two-part thesis option available to Master’s degree program candidates in the Department of Computer Science. You must have completed at least four courses toward your degree and have a grade point average (GPA) of 3.7 or higher. You are responsible for finding a thesis advisor and a principal reader within the department. Please refer to the Department for further details on the application process. Both METCS 810 Master’s Thesis 1 and METCS 811 Master’s Thesis 2 must be completed within 12 months.  [ 4 cr. ]