Computer Science
College of Arts & Sciences
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Introduction to Internet Technologies and Web Programming
CAS CS 103
Introduction to the basic architecture and protocols underlying the operation of the Internet with an emphasis on web design, web application programming, and algorithmic thinking. General familiarity with the Internet is assumed. Carries MCS divisional credit in CAS. Effective Fall 2022, this course fulfills a single unit in each of the following BU Hub areas: Digital/Multimedia Expression, Quantitative Reasoning II, Creativity/Innovation. 4 cr.
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Introduction to Computer Science 1
CAS CS 111
Online offering. This course is a rigorous introduction to programming for students intending to major or minor in Computer Science, Data Science, and related disciplines. The course introduces numeric, string, and list data, functions, decisions, recursion, iteration, and object- orientation. Applications include matrix operations, image manipulation, games, rules-based and generative artificial intelligence, and searching. Learning to program is a skill that can only be learned through practice -- it cannot be acquired from merely watching a series of lectures. Rather, students will learn through a combination of short readings; mini-lecture videos; interactive examples; and complex problem sets. Students must actively engage with these examples and problem sets to develop both the muscle memory of programming as well as a mental model of how programs execute and interact with data. Students will learn new concepts independently and attend regular workshop sessions to develop debugging skills and to obtain assistance with problem sets. The structure of the online class demands that students be intrinsically motivated to acquire programming skills, so that they will be motivated to keep up with a demanding schedule of learning activities and problem sets. To be successful in this course, students must be prepared to dedicate approximately 25-30 hours per week to the learning objectives. Students must have a Mac or Windows computer on which they can install the required software for the course. Carries MCS divisional credit in CAS. Effective Fall 2018, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. 4 cr.
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Introduction to Computer Science 2
CAS CS 112
Undergraduate Prerequisites : (CAS CS 111) or equivalent. Covers advanced programming techniques and data structures. Topics include recursion, algorithm analysis, linked lists, stacks, queues, trees, graphs, tables, searching, and sorting. Students must register for two sections: lecture and laboratory. Carries MCS divisional credit in CAS. Effective Fall 2018, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. 4 cr.
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Combinatoric Structures
CAS CS 131
Representation, analysis, techniques, and principles for manipulation of basic combinatoric structures used in computer science. Rigorous reasoning is emphasized. Effective Fall 2019, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking. 4 cr.
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Probability in Computing
CAS CS 237
Undergraduate Prerequisites: (CAS CS 131). Introduction to basic probabilistic concepts and methods used in computer science. Develops an understanding of the crucial role played by randomness in computing, both as a powerful tool and as a challenge to confront and analyze. Emphasis on rigorous reasoning, analysis, and algorithmic thinking. Students must register for two sections: lecture and laboratory. Effective Fall 2018, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking. 4 cr.
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Topics in Computer Science
CAS CS 391
Topic for summer 2025: Algorithms to Live By. The course will be based on the algorithmic principles described in the popular science book “Algorithms to Live By: The Computer Science of Human Decisions” by Brian Christian and Tom Griffiths. We will cover concepts such as (1) optimal stopping (2) Explore vs. Exploit (3) Sorting and Searching (4) Caching and Memory (5) Game theory and Decision making (6) Handling overwhelm and staying sane. We will discuss the main algorithmic techniques and results related to these concepts and how we can apply them to everyday life. 4 cr.
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Topics in Computer Science
CAS CS 391
Topic for summer 2025: Competitive Programming. Modern Compiler Construction in Python is a course that introduces students to some basics in the design and implementation of compilers. In this course, we teach the theory behind various components of a compiler as well as the programming techniques involved to put the theory into practice. In particular, we adopt a style of modern compiler construction that builds a compiler by stringing a sequence of translations sharing a common closure-based interpreter-like structure. The chosen programming language for implementation is Python 3. However, you can seek the instructor's approval to choose a functional programming language as your implementation language if you so wish. 4 cr.
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Topics in Computer Science
CAS CS 392
Topic for summer 2025: Algorithms for Competitive Programming. Prerequisites: CS112 and CS131. Strong performance in CS 112 and CS 131 is expected. An assessment test might be administered in the first week to provide feedback on readiness to take this class. This course covers essential algorithms necessary to compete in the ACM International Collegiate Programming Contest (ICPC) and similar contests. Active involvement in weekly contests is a mandatory component of the course. Topics covered include standard library classes and data structures, competitive programming contest strategies, string manipulation, divide and conquer, dynamic programming, graph algorithms, number theory, computational geometry, and combinatorics. 4 cr.
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Topics in Computer Science
CAS CS 392
Topic for summer 2025: Web Application Development. Web Application Development is a comprehensive course that equips students with practical skills to build dynamic and immersive web applications. Through hands-on exercises and projects, students learn to structure and style web pages using HTML and CSS, create interactive experiences with JavaScript, develop reusable components with React, interact with relational databases using decoupling tools such as ORM and DAO. Additionally, students explore the exciting world of Web-XR, enabling them to build virtual reality experiences with React-VR. By the end of the course, students have the necessary tools and knowledge to develop robust web applications with seamless integration of databases, interactive functionality, and immersive VR experiences. Students are expected to have basic knowledge of OOP principles, coding conventions, and I/O subsystems. 4 cr.
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Full-Stack Application Design and Development
CAS CS 412
Undergraduate Prerequisites: (CASCS111 & CASCS112 & CASCS411) or consent of instructor. - Prereq: (CAS CS 111 & CAS CS 112 & CAS CS 411) or consent of instructor. Introduction to design and development of full-stack web applications. Topics include asynchronous programming; non-relational data stores; use of APIs; serverless (cloudbased) applications; decoupled client/server architectures; performance; testing; packaging; and deployment. Examines current and proposed technology stacks. 4 cr.
College of Engineering
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Introduction to Software Engineering
ENG EC 327
Undergraduate Prerequisites: (ENGEK125) - Introduction to software design, programming techniques, data structures, and software engineering principles. The course is structured bottom up, beginning with basic hardware followed by an understanding of machine language that controls the hardware and the assembly language that organizes that control. It proceeds through fundamental elements of functional programming languages, using C as the case example, and continues with the principles of object-oriented programming, as principally embodied in C but also its daughter languages Java, C#, and objective C. The course concludes with an introduction to elementary data structures and algorithmic analysis. Throughout, the course develops core competencies in software engineering, including programming style, optimization, debugging, compilation, and program management, utilizing a variety of Integrated Development Environments and operating systems. 4 cr.
Metropolitan College
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Introduction to Computer Information Systems
MET CS 200
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.
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Introduction to Programming
MET CS 201
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.
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Discrete Mathematics
MET CS 248
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.
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Introduction to Web Application Development
MET CS 401
Undergraduate Prerequisites: (METCS231 OR METCS232) or instructor's consent - Prereq: (MET CS 231 or MET CS 232) or instructor's consent. Focuses on building core competencies in web design and development. 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 are covered comprehensively. AJAX with XML and JSON are covered, as they are the primary means to transfer data from client and server. 4 cr.
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Introduction to Database Design and Implementation for Business
MET CS 469
Studies the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Provides extensive hands-on experience using Oracle or Microsoft SQL Server as students learn the Structured Query Language (SQL) and design and implement databases. 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. Introduces 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 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.
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Computer Architecture
MET CS 472
Undergraduate Prerequisites: (METCS231 OR METCS232) or instructor's consent - Prereq: (MET CS 231 or MET CS 232) 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. 4 cr.
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Introduction to Software Engineering
MET CS 473
Undergraduate Prerequisites: (METCS342) or instructor's consent - Prereq: (MET CS 342) 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. 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.
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Information Structures with Java
MET CS 520
Prerequisites: MET CS 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.
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Information Structures with Python
MET CS 521
Covers the concepts of the object-oriented approach to software design and development using Python. Includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeds to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course, students are 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. 4 cr.
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DATA STRUC&ALGO
MET CS 526
DATA STRUC&ALGO 4 cr.
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Computer Networks
MET CS 535
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. - Prereq: (MET CS 575 and (MET CS 201 or MET CS 231 or MET CS 232)) or instructor's consent. Provides a robust understanding of networking. Covers the fundamentals of networking systems, their architecture, function, and operation and how those fundamentals are reflected in current network technologies. Students learn the principles that underlie all networks and the application of those principles (or not) to current network protocols and systems. Explains how layers of different scope are combined to create a network. There is 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. Introduces 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.
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Foundations of Analytics and Data Visualization
MET CS 544
Prereq: (MET CS 546 and (MET CS 520 or MET CS 521)) or equivalent knowledge or instructor's consent. Formerly titled Foundations of Analytics with R. Provides students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts are 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. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory course. 4 cr.
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Introduction to Probability and Statistics
MET CS 546
Undergraduate Prerequisites: Academic background that includes the material covered in a standard c ourse on college algebra. - Prereq: academic background that includes the material covered in a standard course on college algebra or instructor's consent. Provides 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 and discrete and continuous distributions. Restrictions 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 toward degree requirements. 4 cr.
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Computational Mathematics for Machine Learning
MET CS 550
Undergraduate Prerequisites: MET TC 250 and a knowledge of calculus, linear algebra, and an introduction to probability theory and stochastic processes. - Mathematics is fundamental to data science and machine learning. This course reviews essential mathematical concepts and procedures which are fundamental. These concepts are illustrated by Python and/or R code and by many visualizations. This course discusses mathematical concepts and computational methods for data science using simple self-contained examples, intuition and visualization. These examples will help develop intuitive explanations behind mathematical concepts. Extensive visualizations will be used to illustrate core mathematical concepts. The emphasis is both on mathematics and computational algorithms that are at the heart of many algorithms for data analysis and machine learning. This course will advance students mathematical skills that can be used effectively in data analytics and machine learning. Prerequisite: Basic knowledge of Python or R. 4 cr.
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Foundations of Machine Learning
MET CS 555
Prereq: (MET CS 544) or equivalent knowledge or instructor's consent. Formerly titled Data Analysis and Visualization with R. 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. 4 cr.
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Analysis of Algorithms
MET CS 566
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.
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Biomedical Sciences and Health IT
MET CS 570
Provides students with a graduate introduction to the American healthcare system and the roles played by IT in that system. Explores the structure and functions of healthcare information systems, medical terminology, human anatomy and physiology, disease processes, diagnostic modalities, and treatments associated with common disease processes. IT case studies trace the workflows and show how information systems are used in diagnosing and treating diseases. 4 cr.
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Operating Systems
MET CS 575
Undergraduate Prerequisites: (METCS472) and (CS 231 or CS 232) or instructor's consent - Prereq: (MET CS 472 and (MET CS 231 or MET 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. 4 cr.
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Database Management
MET CS 579
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. - Prereq: (MET CS 231 or MET CS 232) or instructor's consent. 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 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.
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Health Informatics
MET CS 580
Undergraduate Prerequisites: (METCS570) - Presents the technological fundamentals and integrated clinical applications of modern Biomedical IT. The first part of the course covers the technological fundamentals and the scientific concepts behind modern medical technologies, such as digital radiography, CT, nuclear medicine, ultrasound imaging, etc. It also presents various medical data and patient records, and focuses on various techniques for processing medical images. This part also covers medical computer networks and systems and data security and protection. The second part of the course focuses on actual medical applications that are used in health care and biomedical research. 4 cr.
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ETHICS&LEGAL HI
MET CS 584
ETHICS&LEGAL HI 4 cr.
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Web Application Development
MET CS 601
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.
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Server-Side Web Development
MET CS 602
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.
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Advanced Programming Techniques
MET CS 622
Prereq: (MET CS 342 or equivalent knowledge of Java) or (MET CS 521 and MET CS 526) or instructor's consent. 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.
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Business Data Communication and Networks
MET CS 625
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. - Prereq: (MET CS 200) or instructor's consent. Presents the foundations of data communications and takes a bottom-up approach to computer networks. 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.
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Information Technology Project Management
MET CS 632
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 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.
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Agile Software Development
MET CS 634
A comprehensive overview of the principles, processes, and practices of Agile software development. Students learn techniques for initiating, planning, and executing software development projects using Agile methodologies. Students obtain practical knowledge of Agile development frameworks and distinguish between Agile and traditional project management methodologies. Students 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. Also leverages the guidelines outlined by the Project Management Institute for Agile project development as a framework. 4 cr.
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Database Design and Implementation for Business
MET CS 669
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. - Studies the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Provides extensive hands-on experience using Oracle or Microsoft SQL Server as students 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 toward degree requirements. 4 cr.
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Software Engineering
MET CS 673
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. - Prereq: At least two 500-level or above programming-intensive courses or instructor's consent. Students should be familiar with object-oriented design concepts and proficient in at least one high level programming language before taking this course. 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. Features a term-long group project where students 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. 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.
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Data Science with Python
MET CS 677
Prereq: (MET CS 521) or equivalent or instructor's consent. Major Python tools and techniques for data analysis. Weekly assignments and mini projects 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. Students choose a topic for a final project and present it on the last day of class. 4 cr.
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Information Systems Analysis and Design
MET CS 682
Undergraduate Prerequisites: Basic programming knowledge or instructor's consent. - Prereq: 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, implementation, and management; project control; and systems-level testing. 4 cr.
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Enterprise Cybersecurity Management
MET CS 684
Enables IT professional leaders to identify emerging security risks and implement security policies to support organizational goals. Discusses methodologies for identifying, quantifying, mitigating and controlling security risks. Students learn to write IT risk management plans, standards, and procedures that identify alternate sites for processing mission-critical applications, and techniques to recover infrastructure, systems, networks, data, and user access. Also discusses disaster recovery; handling information security; protection of property, personnel and facilities; protection of sensitive and classified information; privacy issues; and hostile activities. 4 cr.
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Web Mining and Graph Analysis
MET CS 688
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.
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Mobile Forensics
MET CS 694
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.
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Cybersecurity
MET CS 695
Undergraduate Prerequisites: (METCS625) or instructor's consent - Prereq: (MET CS 535 or MET CS 625) or instructor's consent. Provides an in-depth presentation of security issues in computer systems, networks, and applications. Formal security models are presented and illustrated on operating system security aspects, more specifically memory protection, access control and authentication, file system security, backup and recovery management, and intrusion and virus protection mechanisms. Application level security focuses on language level security and various security policies including conventional and public keys encryption, authentication, message digest, and digital signatures. Internet and intranet topics include security in IP, routers, proxy servers, firewalls, application-level gateways, web servers, and file and mail servers. Discusses remote access issues, such as dial-up servers, modems, and VPN gateways and clients. 4 cr.
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Data Mining
MET CS 699
Undergraduate Prerequisites: MET CS 521, and MET CS 546 and either MET CS 579 or MET CS 669 or instructor's consent. - This course aims to study basic concepts and techniques of data mining. The 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.
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Advanced Machine Learning and Neural Networks
MET CS 767
Graduate Prerequisites: (METCS566) Recommended: students enroll only after taking the MS in computer scie nce core - Prereq: (MET CS 521) and (MET CS 622 or MET CS 673 or MET CS 682) or instructor's consent; (MET CS 677 highly recommended). Formerly titled Machine Learning. Theories and methods for learning from data. Covers a variety of approaches, including Supervised and Unsupervised Learning, Neural Nets and Deep Learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. Each student focuses on two of these approaches and creates a term project. Laboratory course. 4 cr.
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Big Data Analytics
MET CS 777
Prereq: (MET CS 521 & MET CS 544 & MET CS 555) or (MET CS 677) or instructor's consent. 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 course focuses both on the cluster computing software tools and programming techniques used by data scientists, as well as on the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, students study 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 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, students study the main standard supervised and unsupervised models and improvement techniques on the model side. 4 cr.
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ADV DTA BSE MGN
MET CS 779
Graduate Prerequisites: (METCS579 OR METCS669) or consent of the instructor - ADV D-B MGMT 4 cr.
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IT Strategy and Management
MET CS 782
Undergraduate Prerequisites: Restrictions: Only for MS CIS students. - Prereq: (MET CS 682) or instructor's consent. 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 IT to business needs. MET CS 782 is at the advanced Master's (700-) level and assumes that students understand IT systems at the level of MET CS 682 Systems Analysis and Design. Students who have not completed MET CS 682 should contact their academic advisor or the instructor to determine if they are adequately prepared. 4 cr.
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Generative AI
MET CS 788
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.
Questrom School of Business
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Information Systems and Emerging Technologies in Business
QST IS 223
Undergraduate Prerequisite: QSTSM 131 - Provides students with an understanding of the important role that information and information technology play in supporting the effective operation and management of business. The course highlights issues in managing information systems for competitive enterprises and the nature of competition in digital markets. Further, the course introduces modern business technologies, including generative artificial intelligence and supports the application of these tools to real-world business projects. 4 cr.