ONLINE MASTER’S IN COMPUTER SCIENCE (MSCS)

In software problem areas that require exploratory development efforts, those with complex requirements and high levels of change, agile software development practices are highly effective when deployed in a collaborative, people-centered organizational culture. This course examines agile methods, including Extreme Programming (XP), Scrum, Lean, Crystal, Dynamic Systems Development Method and Feature-Driven Development to understand how rapid realization of software occurs most effectively. The ability of agile development teams to rapidly develop high quality, customer-valued software is examined and contrasted with teams following more traditional methodologies that emphasize planning and documentation. Students will learn agile development principles and techniques covering the entire software development process from problem conception through development, testing and deployment, and will be able to effectively participate in and manage agile software developments as a result of their successfully completing this course. Case studies and software development projects are used throughout.

This is an introduction to Human Computer Interaction (HCI). It covers basic concepts, principles, and frameworks in HCI; models of interaction; and design guidelines and methodologies. The course includes extensive readings and reports, as well as work on projects involving interface design and development.

This course will give students a rigorous introduction to the foundations of machine learning, including but not limited to frequently used tools in linear algebra, calculus, probability and widely applied methods such as linear regression and support vector machines. In addition, this course provides hands-on training on implementing these algorithms via Python from scratch. Students will be trained to use popular Python libraries such as Numpy, Scipy and Matplotlib.

In this course, we will talk about the foundational principles that drive machine learning applications and practice implementing machine learning algorithms. Specific topics include supervised learning, unsupervised learning, neural networks and graphical models. The main goal of the course is to equip you with the tools to tackle new ML problems you might encounter in life.

Deep learning (DL) is a family of the most powerful and popular machine learning (ML) methods and has wide real-world applications such as face recognition, machine translation, self-driving cars, recommender systems, and playing the Go game, etc. This course is designed for students either with or without an ML background. The course will cover fundamental ML, computer vision, and natural language problems and DL tools for solving the problems. The students will be able to use DL methods for solving real-world ML problems. The homework is mostly implementation and programming using the Python language and popular DL frameworks such as TensorFlow and Keras. Knowledge and skills in Python programming and linear algebra are strictly required. Probability theory, statistics, and numerical analysis are recommended but not required. Knowledge in machine learning and artificial intelligence is helpful but unnecessary.

Natural language processing (NLP) is one of the most important technologies in the era of information. Comprehending human language is also a crucial and challenging part of artificial intelligence. People communicate almost everything in language: conferences, emails, customer service, language translation, web searches, reports and so on. There is a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have achieved high performance in many different NLP tasks. Instead of traditional and task-specific feature engineering, deep learning can solve tasks with single end-to-end models. The course provides an introduction to machine learning research applied to NLP. We will cover topics including word vector representations, neural networks, recurrent neural networks, convolutional neural networks, semi-supervised models and reinforcement learning for NLP, as well as some attention-based models.

Many managerial decisions — regardless of their functional orientation — are increasingly based on analysis using quantitative models from the discipline of management science. Management science tools, techniques and concepts (e.g., data, models and software programs) have dramatically changed the way businesses operate in manufacturing, service operations, marketing, transportation and finance. Business Analytics explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation, and optimization models.

Applied Analytics is a capstone course for the analytic-focused MBA program. It is intended to integrate all previously taken courses in the program by presenting a set of increasingly complex business problems. These problems can be solved through analytic skills taught in this and previous courses. In particular, the course is intended to reinforce the understanding of analysis as a way to build models that can focus attention on parts of the system that can be improved through intervention. The early part of the course uses synthetic data and empirical data readily available for analysis. The second part of the course encourages students to state and solve their own problem, gathering their own data as a part of the analytic process

This course explores the area of cognitive computing and its implications for today’s world of big data analytics and evidence-based decision-making. Topics covered as part of this seminar include cognitive computing design principles, natural language processing, knowledge representation, advanced analytics, as well as IBM’s Watson DeepQA and Google’s TensorFlow deep learning architectures. Students will have an opportunity to build cognitive applications as well as explore how knowledge-based artificial intelligence and deep learning are impacting the field of data science.

The field of Big Data is emerging as one of the transformative business processes of recent times. It utilizes classic techniques from business intelligence and analysis (BI&A) along with new tools and processes to deal with the volume, velocity, and variety associated with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data as technologists, managers, or users. This course will build on their understanding of the basic concepts of BI&A to provide them with the background to succeed in the evolving data-centric world, not only from the point of view of the technologies required but also in terms of management, governance, and organization. Students taking the course will be expected to have some background in areas such as multivariate statistics, data mining, data management, and programming.

In this course, students will learn through hands-on experience how to extract data from the web and analyze web-scale data using distributed computing. Students will learn different analysis methods that are widely used across the range of internet companies, from start-ups to online giants like Amazon or Google. At the end of the course, students will apply these methods to answer a real scientific question or to create a useful web application.

Personal computing is now mobile and cloud-based. Disconnected mobile computing challenges many of the assumptions underlying much of today’s distributed systems. Cloud computing provides a powerful background computing facility for mobile devices but also raises important issues of trust and privacy. Many of these issues arise in critical yet sensitive domains such as electronic healthcare delivery. Mobile computing applications are location-aware or context-aware; the privacy implications of these applications are profound. Mobile and, increasingly, location-aware gaming systems are now one of the largest sectors of the world entertainment industry. The purpose of this course is to review the fundamentals of mobile systems and applications and how they relate to services in the cloud. The course will review material from wireless communication, distributed systems, and security and privacy as they pertain to the systems being studied. The course will involve programming mobile apps using a popular mobile computing platform, such as Android or iPhone, to get hands-on experience with the concepts being discussed in the class. Programming experience with Java or C# is required.

This course will provide students with an introduction to internet programming. It will cover the basic knowledge of how the internet works and how to create advanced websites using script languages after learning the basics of HTML. The course will teach students how to build a complex global site through the creation of individual working modules, helping them develop the skills required in any business, such as proper teamwork and coordination between groups.

This course focuses on teaching students the newest technologies available in web programming. Topics include advanced client-side programming, responsive design, NoSQL databases, JQuery, AJAX, website security, and the latest frameworks. Students will be given the opportunity to suggest topics they would like to explore at the end of the semester. The course is very hands-on, where everything taught will be practiced through in-class exercises.

Theory of object-oriented design, classes, interfaces, inheritance hierarchy, and correctness; abstract data types, encapsulation, formal specification with preconditions, postconditions and invariants, and proofs of correctness; object-oriented software, objects and classes, genericity, inheritance, polymorphism, and overloading; single and multiple inheritance, programming by contract, subclassing as subcontract, specification, and verification; programming language examples include C+ +, Java, Smalltalk, and Eiffel.

This course covers the computing background for large-scale enterprise computing, including the outsourcing of computing to the cloud. The course includes developing and deploying web and microservice applications in the cloud for both client-facing and B2B applications. The course also considers cloud support for enterprise integration and Internet of Things, and NoSQL data stores such as CosmosDB. Finally, the course considers virtualization and its role in the cloud, including security in virtualization. Cloud computing: SaaS and PaaS (e.g., Azure App Service). Web applications in the cloud: ASP.NET MVC. Enterprise Web services: gRPC and Web API. Serverless applications and microservices. Gathering and processing data using NoSQL data stores, e.g., CosmosDB. Enterprise blockchain: Azure Confidential Ledger and Quorum Blockchain Service. Virtualization as the basis for scalable enterprise and cloud computing: Xen, KVM, z/VM. Secure virtualization, e.g., Security Enhanced Linux (SELinux). Programming experience with Java or C# is required.

This course covers the issues in designing and engineering large enterprise and cloud-based software systems. Such systems are distributed and require increasingly complex inter-enterprise as well as intra-enterprise coordination. Technologies such as Web Services and cloud computing provide platforms for building such systems, and architectures such as microservices and cloud-native applications, event-driven architecture (EDA), domain-driven design (DDD), representational state transfer (REST), command query responsibility segregation (CQRS), serverless and blockchain are idioms for structuring such systems. Data modeling includes E-R designs, XML and JSON Schemas, NoSQL data models, semantic data modeling (OWL), and object-relational mapping (ORM). Process modeling includes BPMN, Workflow and Petri nets. The course includes hands-on application of the concepts with tools such as Jakarta EE and Eclipse MicroProfile, Docker, Kubernetes and Kafka, and Hyperledger Fabric. Knowledge of Java or C# is required.

In software problem areas that require exploratory development efforts, those with complex requirements and high levels of change, agile software development practices are highly effective when deployed in a collaborative, people-centered organizational culture. This course examines agile methods, including Extreme Programming (XP), Scrum, Lean, Crystal, Dynamic Systems Development Method, and Feature-Driven Development to understand how rapid realization of software occurs most effectively. The ability of agile development teams to rapidly develop high-quality, customer-valued software is examined and contrasted with teams following more traditional methodologies that emphasize planning and documentation. Students will learn agile development principles and techniques covering the entire software development process from problem conception through development, testing and deployment, and will be able to effectively participate in and manage agile software developments as a result of their successfully completing this course. Case studies and software development projects are used throughout.

Introduction to the design and querying of relational databases. Topics include relational schemas, keys and foreign key references, relational algebra (as an introduction to SQL), SQL in depth, Entity-Relationship (ER) database design, translating from ER models to relational schemas and from relational schemas to ER models, functional dependencies and normalization.