Computer Information Systems Graduate Courses

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. ]

Prerequisite: Programming experience in any language. Or Instructor's consent. Explore the object-oriented approach to software design and development using Python. You will engage in a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceed to more advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course, you will be able to apply software engineering principles to design and implement Python applications that can be used with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Creativity/Innovation, Critical Thinking, Quantitative Reasoning 2.  [ 4 cr. ]

Prerequisites: MET LB 110 lab and either MET CS520 or MET CS521, or consent of instructor. Learn fundamental components of programs using various data structures to solve computational problems, and implement data structures using a high-level programming language. Algorithms will be created, decomposed, and expressed as pseudocode, and you will analyze their running time and computational complexity.  [ 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. Restrictions: This course may not be taken in conjunction with MET CS 550.  [ 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. Restrictions: This course may not be taken in conjunction with MET CS 544.  [ 4 cr. ]

Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. You will learn the foundations of statistical machine learning, regression, and classification, and explore the key components of statistical models, including how to construct, interpret, and evaluate them. Topics include data description and visualization, statistical inference, one- and two-sample tests for means and proportions, simple and multiple linear regression, multinomial and logistic regression, analysis of variance (ANOVA), and regression diagnostics. For each topic, you will examine the methodology, underlying assumptions, interpretation of results, and model assessment. The course includes a programming component using R or Python, providing hands-on experience that reinforces theoretical concepts. Methods are presented through real-world examples to help you understand when and how to apply different statistical techniques effectively.  [ 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. ]

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 472 or MET CS 572 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. ]

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. ]

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

Prerequisite: METCS535 or METCS625 or instructor's consent. In this course, you will be introduced to fundamental concepts and principles of cybersecurity and how to apply them in developing security mechanisms and policies. Explore topics such as basic risk assessment and management, legal and ethical issues, cyber attack defense methods and tools, security principles, models, and components. You will also learn about different crypto protocols, techniques, and tools, including symmetric and asymmetric encryption algorithms, hashing, public key infrastructure, and how they can be used. You will learn to identify security threats and techniques to defend hardware, operating systems, networks, and applications in modern computing environments. Hands-on labs using current tools will also be completed as part of the required coursework.  [ 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. ]

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. ]

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. Explore network design and management principles as you work through specific design areas within a Content Delivery Network (CDN). You will start with an in-depth understanding of customer needs and requirements gathering as it relates to today’s implementation of Voice, Video, and Data services delivered via a CDN. This design will encompass transmission and modulation techniques such as payload structure for Optical and multi-GigaBit Ethernet circuits. The FCAPS Network Management model will also be implemented in a real world architecture.  [ 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. ]

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. ]

Prerequisites: At least two programming-intensive or software development courses or consent of instructor. You should be proficient in at least one high-level programming language. Completion of MET CS 673 is preferred. - An overview of techniques and tools to develop secure software. You will focus on the application of security with topics including secure software development processes, DevSecOps, threat modeling, secure requirements and architectures, vulnerability and malware analysis using static code analysis and dynamic analysis tools, and vulnerabilities in C/C ++ and Java programs, Crypto and secure APIs, vulnerabilities in web applications and mobile applications, and security testing will also be covered. You will complete the required hands-on lab and programming exercises using current tools.  [ 4 cr. ]

Prerequisites: MET CS 521 and at least one of MET CS 577, MET CS 622, MET CS 673 or MET CS 682; 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. ]

Prerequisites: (MET CS 521 & MET CS 544 & MET CS 555) or MET CS 577 or consent of instructor. An overview of the principles and practice of large-scale data analytics. You will examine methods for extracting meaningful insights from large, complex, and distributed datasets, learning about core technologies for storing and processing high-volume data. This course emphasizes distributed computing frameworks based on the MapReduce paradigm, including Hadoop MapReduce and Apache Spark, along with programming models, parallel data processing, and performance considerations in cluster-based environments. Through hands-on assignments and projects, you will implement data processing algorithms and deploy them on cloud platforms such as Amazon Web Services (AWS) and Google Cloud, developing the practical skills required for data engineering and large-scale analytics in real-world environments. Educational cloud accounts and credits are provided.  [ 4 cr. ]

Prerequisites: METCS579 or METCS669 or consent of the instructor - Investigate 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. 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, are covered. You will learn about unstructured "big data" architectures and databases, gain hands-on experience with Spark and MongoDB, and complete a term project exploring an advanced database technology of your choice.  [ 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. ]

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 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. ]

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. ]

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. ]