Online Master of Science in Computer Science

New students take the Mastery Skill Profiler to determine their current level of programming and discrete mathematics skills. Students may skip this 0-credit foundational course if their results demonstrate proficiency in foundational mathematical and processing skills.

This course will introduce students to basic mathematical and processing topics. These topics include numbering systems, summations, progressions, combinatorics, logarithms, matrices, probabilities and how to use basic processing tools to compute those. The topics are intended to set a solid discrete mathematical foundation to develop basic programming skills in a simple script language and to allow the student to support discrete mathematics operations with basic script language commands. Credits: 0

This course is an introduction to programming concepts. Emphasis will be placed on algorithms, test-driven design, development, and structured programming in the Python language. Topics include program development, modularity, variables and data types as numbers, strings, arrays and lists, plus the basic programming concepts as conditionals and Boolean algebra, loops, I/O operations, classes, and objects, abstract data types, sorting algorithms, and recursion. Credits: 4

This foundational course is an introduction to algorithmic thinking and the mathematics of computer science. Topics include abstract data types such as lists, stacks, queues, hash maps, trees and graphs, but also basics of asymptotic analysis, recursion and various algorithmic strategies including brute force, decrease-and-conquer and divide-and-conquer. Programming exercises will help create proficiency in Python. Emphasis will be placed on understanding underlying mathematics, such as discrete probability, statistics, graph theory and set theory. Credits: 4

This course is an extension of the process of algorithmic thinking and the mathematics of computer science. Topics include asymptotic analysis, and various algorithmic strategies including transform-and-conquer, dynamic, greedy, amortized analysis, linear and integer programming, randomized, and approximation algorithms. Emphasis will be placed on understanding underlying mathematics, such as discrete probability, statistics, graphs, and set theory. Credits: 4

This course introduces students to basic concepts of computational theory from a practical point of view. The course also introduces students to the C++ programming language, assuming a fluency in Python. Students are expected to understand the definition of a language using finite automata and regular expressions. The concepts of pushdown automata and Turing machines are demonstrated as a basic model of computation, equivalent to all existent programming languages. Students are introduced to the concept of decidability, which is the determination of whether a language can be Turing-decidable or not, allowing them to investigate the power of algorithms to solve problems. Credits: 4

Great products start out from great designs authored by effective teams. This course introduces the student to the software development lifecycle at the graduate level. Focus will be placed on design and documentation methodologies used by practitioners. Students will learn to author clear and effective software documentation for a host of different design methodologies. Software design methodologies discussed will include waterfall, spiral, scrum, and agile. Other topics include version control, issue tracking, software project management, debugging, and profiling. Credits: 4

An introduction to databases at the graduate level. In this course, students will learn to effectively design, implement, and deploy both relational and non-relational databases. Topics include relational databases, normal forms, consistency, basic SQL, stored procedures, query optimization, non-relational and no-SQL databases. Examples will be drawn from industry. Students will also obtain hands-on experience with several database engines. Credits: 4

This course introduces students to key programming language families and concepts, and key system programming concepts. Topics include procedural, object-oriented, and functional programming language principles, the role of type systems and type safety, multi-threaded programming and associated design techniques including parallelization, deadlock and deadlock avoidance, and basic scheduling algorithms. Examples will be drawn from contemporary systems and languages. Credits: 4

This course will introduce students to advanced concepts in programming. These topics will include the development and use of large-scale application programmer interfaces (APIs), effective documentation of APIs, authoring clean and useful APIs, sockets, generics, regular expressions, client-server model applications, and design patterns such as factories, decorators, and MVC. Credits: 4

Great products begin with great designs authored by effective teams. This course introduces students to the software development lifecycle at the graduate level. Emphasis is placed on design and documentation methodologies used by practitioners. Students learn to author clear and effective software documentation for a host of design methodologies,including waterfall, spiral, scrum and agile. Other topics include version control, issue tracking, software project management, debugging and profiling. Credits: 4

This course provides an introduction to databases at the graduate level. Students learn to effectively design, implement and deploy relational and non-relational databases. Topics include relational databases, normal forms, consistency, basic SQL, stored procedures, query optimization, non-relational and no-SQL databases. Examples are drawn from industry. Students also obtain hands-on experience with database engines. Credits: 4

This course explores the concept of the thinking machine, capable of its own reasoning and extending itself beyond the limits of its programming. Core topics include extending a machine’s ability to search for its own solutions through the exploration of problem spaces and the use of reasoning through propositional and first-order logic. Advanced topics may include data mining, deep learning, artificial life and natural language understanding. Credits: 4

In this course, students learn about the basics of deep neural networks and their application to AI tasks. The course explores various neural network structures, such as feedforward networks, recurrent networks, convolutional networks and transform networks. Students learn to utilize deep learning techniques in their software design and development. Additional topics include linear algebra, simulation, modeling, tensors and others as time allows. Credits: 4

This course explores the ethical considerations of the use of artificial intelligence. Students review case studies and applications and learn to identify issues of fairness, justice, bias and truth. Students also learn to understand dataset and algorithmic biases, as well as methods to mitigate them. Students gain the skills to incorporate these methods in their own software development.

Students with programming experience may be eligible to waive Foundations of Programming (CSC 6003) and complete the capstone experience.

In this course, students demonstrate mastery of the material related to their area of study through a faculty-guided capstone project. The project must be centered on the development of a software product and include the motivation, goals, requirements, plan, algorithms, software modules and documentation. Students must also write a final report describing all aspects of the produced software product, as well as an account of their challenges and achievements. Credits: 4