Online Master of Science in Computer Science Program
Online Computer Science Master’s Program Overview
No GRE Required
20-36 Months to Complete*
*Varies based on class availability and the number of credit hours taken per semester
$1,930 Per Credit Hour**
**Based on 2023-2024 tuition rate. Tuition and fees subject to change each year as approved by Tulane University
How long does it take to complete the online Master of Science in Computer Science?
The time it takes to earn your MSCS online from Tulane depends on your program pace and individual priorities. You have the option to complete the 10-course, 30-credit hour MSCS at a full- or part-time pace. A full-time pace requires taking two to three courses per semester for five semesters. In this case, you can complete your degree in an average of 20-28 months.
Alternatively, a part-time pace is a manageable workload if you plan to continue working while earning your degree. A recommended part-time pace is one to two courses per semester, which would allow you to complete your degree in approximately 28-36 months. For full- and part-time learners, your weekly time commitment consists of live classes, independent coursework, and study time.
Is this program the right fit for you?
At Tulane, our online master’s in computer science program offers you the option to customize your coursework and areas of emphasis. So whether you’re a seasoned computer scientist or an aspiring artificial intelligence specialist, our degree has something for you.
Prepare for your professional pivot
For technical professionals interested in changing industries, the online master’s in computer science can equip you with the tools to pivot. If you have a STEM bachelor’s degree and are curious about computer science, our online MSCS program will prepare you with the depth of technical know-how and industry connections you need to succeed in your next role.
Advance in your industry
Today, a master’s degree is a typical requirement for computer research scientist roles.* If you’re already in the field and eager to advance, our online computer science master’s can help you reach your goals. Develop next-level computing skills, with the option to focus your studies in natural language processing, machine learning, algorithms, and more.
*U.S. Bureau of Labor Statistics, Occupational Outlook Handbook, 2022
Find your specialty
Sights set on a career in research or academia? The computer science master’s degree faculty will help you prepare for your dream job. Our graduate degree also offers you the option to dive deeper into areas like computational geometry, cloud computing, data science, and beyond so that you can become a best-in-class specialist upon graduation.
Online Program Experience
Tulane’s online Master of Science in Computer Science program redefines flexibility. Here, it means you can complete your online master’s in computer science from wherever you are, at your own pace, all while choosing electives connected to your interest areas.
You are at the center of our online MS in Computer Science program. As an online student, you will participate in virtual classes with professors and peers while you complete self-paced, project-based assignments on your own schedule. Our small class sizes mean you’ll work closely with your faculty and classmates throughout the program. You can ask questions, share insights, and receive feedback, just like you would taking classes on campus. There are also live office hours to further your connection to faculty and support your mastery of program concepts.
The typical weekly time commitment for the online MSCS varies based on the number of credit hours taken each semester. On average, you can expect to spend two to three hours per week, per credit hour, on homework and assignments. This includes one 75-minute live session per three-credit-hour course each week.
For more information about the online structure and program experience, schedule time with one of our enrollment advisors today.
What will you learn in a computer science master’s program?
Tulane’s online MS in Computer Science curriculum challenges you to think beyond basic programming. With the expansion of remote work and uptick in demand for virtual operational systems, computer science professionals need advanced automation, data science, machine learning, and cyber security skills. Our 30-credit curriculum prepares you with the tools you need to make the most of the expanding digital space while focusing on areas of the field that interest you.
Over the course of your online MSCS degree program, you will complete 9 credits of core classes and 21 elective credits. Core classes include 3 credits (1 course) in each breadth area: algorithms, systems, and artificial intelligence/machine learning.
Your remaining 21 credits of elective coursework are highly customizable. You can personalize your course of study to a specialization within computer science or one of six focus areas, including computational geometry, computational biology and bioinformatics, algorithms and theory, systems, data science, artificial intelligence and machine learning.
*Courses are subject to change.
This class focuses on several core topics in the design, analysis, and implementation of computational tools that are drawn from the fields of data structures, software engineering, and programming languages. Other topics include object-oriented programming, test-driven development, data structures and abstract data types, imperative programming and memory management, and functional programming.
By solving practical, real-life problems in different programming languages and in different ways, students learn to select a language and approach most appropriate for the situation, and prepare to learn new languages independently. The high-level goal of this course is to train students to be able to draw from a versatile set of skills, which in turn will provide a strong foundation for further study in computer science.
This course covers fundamental algorithm design principles and data structures, basic notions of complexity theory, as well as an advanced introduction to parallel algorithms, randomized algorithms, and approximation algorithms. Topics include: divide-and-conquer, dynamic programming, amortized analysis, graph algorithms, network flow, map reduce, and more advanced topics in approximation algorithms and randomized algorithms. Satisfies the algorithm breadth area requirement.
The objective of the course is to introduce students to the core concepts and analytic techniques in the design and analysis of computer networks and network protocols. We will explain both how computer networks work using the Internet as the paradigm and why they work from an optimization and control perspective. Satisfies the systems breadth area requirement.
The aim of this course is to provide the student with an introduction to the main concepts and techniques playing a key role in artificial intelligence. In addition to covering the primary components of AI, particular attention will be devoted to its applications in several fields. Among the topics covered are: intelligent artificial agents,problem solving using search and constraint satisfaction, uncertainty, Bayesian networks and probabilistic inference, supervised learning, planning, sequential decision problems, as well as several additional topics. Satisfies the artificial intelligence/machine learning breadth area requirement.
The aim of this course is to provide the student with an introduction to the main concepts and techniques required for collecting, processing, and deriving insight into data. Data Science is an interdisciplinary set of topics that includes everything you need to create data-driven answers and solutions to specific business, scientific, or sociological questions. Topics typically covered include an introduction to one or more data collection and management systems, e.g., SOL, web scraping, and various data repositories; exploratory and statistical data analysis, e.g., bootstrapping, measures of central tendency, hypothesis testing, and machine learning techniques including linear regression and clustering; data and information visualization, e.g., plotting and interactive charts using various technologies; and presentation and communication of the results of these analyses.
This course provides an introduction to geometric algorithms and geometric data structures. Computational Geometry is a young discipline which enjoys close relations to mathematics and to various application areas such as geometric databases, molecular biology, sensor networks, visualization, geographic information systems (GIS), VLSI, robotics, computer graphics and geometric modeling. Covered topics include fundamental geometric algorithm design and analysis paradigms, geometric data structures for planar subdivisions and range searching, algorithms to compute the convex hull, Voronoi diagrams, and Delaunay triangulation, as well as selected advanced topics.
An introduction on how graphical representations of data can be used to aid understanding. This course details the theory and practice of designing effective information or scientific visualizations. The techniques learned in this class have wide applications to all fields in engineering and science, where due to increasing sizes and complexity, data now demands effective presentation and analysis. Topics will include iso-surfacing, volume rendering, transfer functions, vector/tensor fields, topological analysis, large data visualization, and uncertainty in visualizations.
This course has two main goals. The first one is to give a broad overview of the fundamentals of multi-agent systems (MAS). MAS are playing an increasingly important role in artificial intelligence as distributed resources push for highly distributed forms of intelligence. The second aim is to provide a more in-depth discussion of selected MAS topics: game theory and voting from a computational point of view. Situated at the nexus between economics and computer science, these research areas provide a perfect example of interdisciplinary cross-fertilization and mutual enrichment and lie at the core of multi-agent systems theory. The course will provide the student with an understanding of how self-interested behavior and coordination can be formally modeled and implemented in societies of artificial agents. Course may be repeated up to unlimited credit hours.
This course investigates computational methods to work with human language, analyzing its lexical, syntactic, and semantic aspects. Examples include document classification and clustering, syntactic parsing, information extraction, speech recognition, and machine translation. Theoretical and practical aspects of the latest techniques will be covered, including probabilistic modeling, neural networks, and deep learning.
This is a project-oriented course on fundamentals of software development and software engineering. Working in teams, students apply a recognized software engineering methodology, a modern programming language, and software development tools (including an IDE, debugger, version control system, and testing framework) to design and implement a semester-long project – a software solution for a real-world problem. The high goal of the course is to train students to function efficiently in a real-world software development environment. To help reach that goal, the students do a lot of independent learning, teamwork, documentation and public presentation of their product and design process. The particular technologies employed in the course may change in synchrony with changes in the software engineering field, currently the focus is on engineering software-as-a-service using Ruby for programming language and Rails for web development framework.
This course varies from time to time, focusing on topics of interest to the faculty and students. Course may be repeated up to unlimited credit hours.
Who will you learn from?
Meet the brilliant minds behind our online MS in Computer Science at Tulane. Internationally recognized researchers and practitioners, the MSCS faculty bring a high caliber of computer science proficiency to the classroom.
Explore our faculty, their areas of expertise, and the new and noteworthy research coming out of the School of Science and Engineering.
PhD, 2008, University of Massachusetts at Amherst
Dr. Culotta conducts research in natural language processing, machine learning, and social network analysis. Application domains include public health, emergency management, and political science.
Zhengming (Allan) Ding
PhD, 2018, Northeastern University
Dr. Ding focuses on research in computer vision and machine learning with applications as traditional image analysis, autonomous driving, materials science, and medical data analysis.
PhD, 2008, University of Pennsylvania
Dr. Hamm’s main research areas are deep learning, adversarial machine learning, and private/trustworthy machine learning. Applications include AI-based medical data analysis and AI-based material design.
PhD, 2010, University of Texas, Dallas
Dr. Kurdia’s research is in computer science education with concentrations in effective teaching and active learning, peer teaching, learning at scale, accessible design, and increasing diversity in technical fields.
PhD, 2012, University of Kentucky
Dr. Mattei conducts research in artificial intelligence, data science, and machine learning applied to settings involving group decision making and preference reasoning.
PhD, 2019, University of New Orleans
PhD, 2002, University of Texas, Austin
Dr. Mettu conducts research at the intersection of algorithms and machine learning, with applications to computational biology and robotics.
Dr. Ming’s research interests span software and systems security, with a focus on binary code analysis, hardware-assisted software security analysis, mobile systems security, and language-based security.
Dr. Peng’s research area is computer systems and architecture focusing on many design issues on CPUs and GPUs, accelerators and applications for machine learning, and blockchain hardware accelerators and applications.
Dr. Shirvani’s interests lie at the intersection of artificial intelligence and game development, with a focus on simulations and interactive narratives, and applications in entertainment, education, and healthcare.
PhD, 2013, University of Utah
Dr. Summa conducts research in visualization, visual analytics, image processing, and computer graphics. Application domains include biology, medicine, geoscience, physics, and the arts.
PhD, 2002, Freie Universität Berlin
Dr. Wenk’s research interests include algorithms, in particular computational geometry and shape matching, topological data analysis, as well as applied areas such as geospatial data analysis and biomedical applications.
PhD, 2010, Ohio State University
Dr. Zheng conducts research in (deep) reinforcement learning, security, and optimization. Application domains include edge and cloud computing, autonomous driving, power systems, and healthcare.
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