Online M.S.

in Computer Science

Expand your career opportunities with an online master’s degree in computer science.

Admissions

Important Dates
Who Should Apply
Application Materials & Instructions
Tuition & Fees

Important Dates

June 1

Priority Fall Application Deadline

July 15

Final Fall Application Deadline

October 1

Priority Spring Application Deadline

November 15

Final Spring Application Deadline

Who Should Apply

This program is intended for students with a solid background in computer science and mathematics. An undergraduate degree in computer science equivalent to the one offered by UMass Amherst is ideal preparation, but by no means required. At a minimum, students should have taken courses equivalent to those listed below or have relevant work experience that developed the same skills. Individual graduate courses may have greater expectations. We recommend that students check descriptions of courses they are interested in for their prerequisites. Applicants are encouraged to detail all relevant work and educational experiences in their personal statements.

CICS 110: Foundations of Programming

An introduction to computer programming and problem solving using computers. This course teaches you how real-world problems can be solved computationally using programming constructs and data abstractions of a modern programming language. Concepts and techniques covered include variables, expressions, data types, objects, branching, iteration, functions, classes, and methods. We will also cover how to translate problems into a sequence of instructions, investigate the fundamental operation of a computational system and trace program execution and memory, and learn how to test and debug programs.

CICS 160: Object Oriented Programming

This course will expose students to programming practices beyond the introductory level, concentrating on Object Oriented Programming techniques and an introduction to Data Structures. Students will also study and analyze the complexity of both the algorithms presented in class and of the algorithms they develop. Before taking this course, students are expected to have been exposed to the following concepts through a college-level course or equivalent in some high level computer programming language: input and output operations, conditional statements, loops, arrays, recursion, and functions/methods. The course places an emphasis on the careful design and testing of programs, as well as in the analysis of said programs.

CICS 210: Data Structures

An introduction to the design, analysis, and implementation of data structures. This course teaches you how to build, test, debug, document, and evaluate objects that encapsulate data and their associated operations using programming constructs and data abstractions of a modern programming language. Concepts and techniques covered include linear and non-linear structures, recursive structures and algorithms, traversal algorithms, binary search trees, balanced trees, priority queues, union-find, hash tables, Bloom filters, and graphs. We will also informally compare and contrast the run time efficiency of algorithms and their performance characteristics including the concept of worst-case running time analysis and the classification of algorithms in terms of constant, logarithmic, linear, log linear, quadratic, and exponential time using Big-O notation.

COMPSCI 220 Programming Methodology

Development of individual skills necessary for designing, implementing, testing and modifying larger programs, including: use of integrated design environments, design strategies and patterns, testing, working with large code bases and libraries, code refactoring, and use of debuggers and tools for version control.

COMPSCI 230 Computer Systems Principles

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results. On the other hand, many computer systems don't perform nearly as well as Google - hence the now-cliche "the system is down." In this class, we study the scientific principles behind the construction of high-performance, scalable systems. The course begins with a discussion of C language, and moves up the stack from there to the features of modern architectures, assembly languages, and operating system services such as I/O and synchronization.

COMPSCI 250 Introduction to Computation

Basic concepts of discrete mathematics useful to computer science: set theory, strings and formal languages, propositional and predicate calculus, relations and functions, basic number theory. Induction and recursion: interplay of inductive definition, inductive proof, and recursive algorithms. Graphs, trees, and search. Finite-state machines, regular languages, nondeterministic finite automata, Kleene's Theorem.

MATH 131 Calculus I

This course covers continuity, limits, and the derivative for algebraic, trigonometric, logarithmic, exponential, and inverse functions. Those concepts have applications to physics, chemistry, and engineering.

MATH 132 Calculus II

Math 132 continues the study of single-variable calculus which covers the definite integral, techniques of integration, and applications to physics, chemistry, and engineering. Sequences, series, and power series.

MATH 235 Introduction to Linear Algebra

Basic concepts of linear algebra. Matrices, determinants, systems of linear equations, vector spaces, linear transformations, and eigenvalues.

MATH 233 Multivariate Calculus or STATS 515 Statistics I

Math 233 is the third semester of the calculus sequence. It develops the extension of single-variable calculus to functions of several variables. In particular, it covers vectors in two- and three-dimensional space, partial derivatives, double and triple integrals, line integrals, and surface integrals. The culmination of the course is the generalization of the fundamental theorem of calculus: Green’s theorem, Stokes’s theorem, and the Divergence Theorem.

STAT 515 provides a calculus based introduction to probability (an emphasis on probabilistic concepts used in statistical modeling). Coverage includes basic axioms of probability, sample spaces, counting rules, conditional probability, independence, random variables (and various associated discrete and continuous distributions), expectation, variance, covariance and correlation, probability inequalities, the central limit theorem, the Poisson approximation and sampling distributions.

Application Materials & Instructions

Applications are handled through the UMass Amherst Graduate School. All materials should be sent to the Graduate Admissions Office only. For questions on the graduate admissions process, see the Graduate Admissions website.

To apply, you must submit:

Online application
Personal statement
Transcripts (unofficial)
Two letters of recommendation
English exam scores for international students

Submission of GRE scores is optional for the MS program. Choosing not to submit GRE scores will not harm an applicant's chances of admission.

For more detailed information, visit the Graduate Application Instructions page.

Tuition & Fees 2022-2023

$825 / credit
30 credits
Total cost = $25,557
UMass Amherst alumni receive a 10% discount.

Some employers offer tuition reimbursement. Check with your employer to see if they have a tuition reimbursement program.