Master of Computer Science
University of Illinois
Sit in on a Degree Course
If you would like to get an idea of how the courses are structured in the degree program, or, if you want to get a head start on learning the material for when you start the degree, you can take a specialization or an open content course. Although they do not convey course credit, you will be able to familiarize yourself with the course material before starting the class.
Specializations
- Cloud Computing Specialization
- Data Mining Specialization
- Deep Learning for Healthcare Specialization
- Hands-on Internet of Things Specialization
Courses
Also, if you do not have graded and transcripted prerequisite CS coursework in the areas of data structures, algorithms, and object-oriented programming, but have at least a 3.2 GPA and 4-5 years of relevant CS experience, check out our Accelerated Computer Science Fundamentals Specialization. This series of courses is designed to help you prepare for the Data Structures Proficiency Exam, which can strengthen your application for admission. Admission preference is still given to those who have prerequisite coursework on their transcript.
Curriculum
In this degree program, you can pursue a Master of Computer Science or specialize in data science through the Master of Computer Science in Data Science track. Interested in how assignments are designed for the online MCS? Find out how.
Curriculum for both tracks are outlined below:
Master of Computer Science
Build expertise and career skills in the most important computer science topics. Courses and projects cover subjects like:
Architecture, Compilers, and Parallel Computing
Learn parallel programming and how to achieve peak performance from multi-core CPU and many-core GPU architectures. Master languages, compilers, and libraries that are suited for various parallel applications and platforms.
Artificial Intelligence and Machine Learning
Build your knowledge of the fundamental statistical models and numerical optimizations of machine learning, including deep learning, with applications in computer vision, natural language processing and intelligent user interaction.
Database and Information Systems
Learn the basics of database systems as well as data mining methods for extracting insight from structured datasets (e.g. for a sales recommendation system) as well as unstructured data (e.g. from natural language text).
Formal Methods, Programming Languages, and Software Engineering
Discover the fundamentals of software engineering, including function-based and object-oriented methods for analysis and design. Learn to manage a large software project from specification through implementation, testing, and maintenance. You‘ll also learn to manage large enterprise-level codebases.
Graphics, Visualization, and Interactive Computing
Learn the fundamentals of interactive computing that promote synergy between the computer and its user. The Data Visualization course, for example, shows how to present and manipulate data to communicate understanding and insight to the public.
Systems and Networking
Learn how to network computers into distributed systems and build a cloud computing platform or an Internet of Things. Understand how to create applications that utilize cloud resources with programming projects that utilize Amazon Web Services and Microsoft Azure.
Scientific Computing
Discover the fundamentals of numerical analysis, and how it’s applied to scientific and engineering simulations, with applications ranging from creating video game worlds to virtual medicine.
When you graduate, you’ll be able to:
- Apply mathematical foundations, algorithmic principles, and computer science theory to real-word problems
- Analyze a problem and identify the computing requirements appropriate to its solution
- Design, implement, and evaluate a computer-based system, process, component, or program
- Apply design and development principles to construct software systems of varying complexity
Master of Computer Science in Data Science Track
Earn your Master’s, learn from pioneering Illinois faculty, and gain the data science skills that are transforming business and society. Illinois Computer Science offers a specialized track that includes both MCS degree requirements and data science-focused coursework. This degree is right for anyone who not only wants to learn to extract knowledge and insights from massive data sets, but also wants full command of the computational infrastructure to do so.
The Master of Computer Science in Data Science (MCS-DS) leads the MCS degree through a focus on core competencies in machine learning, data mining, data visualization, and cloud computing, It also includes interdisciplinary data science courses, offered in cooperation with the Department of Statistics and the School of Information Science.
If you select the Data Science track, your courses and projects will focus on:
Machine Learning
Coursework focusing on tool-oriented and problem-directed approaches to machine learning with applications in computer vision, natural language processing, geopositioning, and voice & music.
Data Visualization
Coursework designed to show you how to create effective and understandable data presentations. Learn database visualization tools like Tableau. Use D3.js to develop reactive web pages for narrative data storytelling.
Data Mining
This course shows you how to discover patterns in structured data. You’ll also learn to retrieve information from unstructured data sources, such as natural language text.
Cloud Computing
Coursework on the cloud computing technology, infrastructure and application development that is essential for supporting the discovery and extraction of knowledge from big data.
When you graduate, you’ll be able to:
- Utilize cloud computing to scale up analysis and processing of big data
- Visually and computationally analyze available data to inform critical decisions
- Study data scientifically, and use it to form, prove and defend hypotheses
- Program effectively, using the right tools for the job
Curriculum and Graduation Requirements
Breadth Coursework
Advanced Coursework
For MCS and MCS-DS: Must complete three courses (12 credit hours).
- CS 513 Theory and Practice of Data Cleaning
- CS 519 Scientific Visualization
- CS 598 Foundations of Data Curation
- CS 598 Practical Statistical Learning*
- CS 598 Advanced Bayesian Modeling
- CS 598 Deep Learning for Healthcare
- CS 598 Cloud Computing Capstone*
- CS 598 Data Mining Capstone*
*Prerequisites apply.
Electives
For MCS: Must complete one course (4 credit hours). Can take an additional Breadth, Advanced course, or the following. Counts toward the eight courses required to earn the degree.
For MCS-DS: Must complete one course (4 credit hours). Can take an additional Breadth, Advanced course, or the following. Counts toward the eight courses required to earn the degree.
- CS 418 Interactive Computer Graphics
- CS 421 Programming Languages and Compilers
- CS 427 Software Engineering I
- CS 450 Numerical Analysis
- CS 484 Parallel Programming
- CS 498 Internet of Things
- STAT 420 Methods of Applied Statistics
Additional Requirements
- All coursework must be taken through the Coursera MOOC platform.
- Breadth coursework must have a letter grade of B- or higher. Any other course taken for letter grade must have a grade of C or higher.
- Up to 12 credit hours of previous graduate coursework that is approved by the Department of Computer Science (including non-degree graduate courses completed within the Department of Computer Science) may be transferred and applied to the Professional MCS degree requirements.
Program Length
The program is designed so students can complete it at their own pace as they balance their personal and professional commitments. Most students complete the degree in less than three years, though it can be completed in as little as one year or as many as five years.
The MCS requires 32 credit hours of graduate coursework, completed through eight graduate-level courses at the four-credit-hour level. Each course requires approximately 10 - 12 hours of work per week.
| Sample Schedule | 1 year (at least 20-30 hours per week) | 2 years (at least 10-20 hours per week) |
|---|---|---|
| Fall | CS 598 / IS 531 Foundations of Data Curation, CS 598 / STAT 578 Advanced Bayesian Modeling, CS 425 Cloud Computing Concepts | CS 598 / IS 531 Foundations of Data Curation, CS 598 / STAT 578 Advanced Bayesian Modeling |
| Spring | CS 411 Database Systems, CS 498 Applied Machine Learning | CS 411 Database Systems |
| Summer | CS 498 Data Visualization, CS 513 Theory and Practice of Data Cleaning, Stat 420 Methods of Applied Statistics | CS 498 Data Visualization, CS 513 Theory and Practice of Data Cleaning |
| Fall | CS 425 Cloud Computing Concepts | |
| Spring | CS 498 Applied Machine Learning | |
| Summer | Stat 420 Methods of Applied Statistics |
Flexibility
Earn your degree on your schedule with 100% online courses and pay as you go. Lectures and quizzes are available on demand, and your professors and teaching assistants are accessible through multiple office hours sessions as well as course discussion boards. Access classes from your mobile device and download lectures to study without using your data plan.
Coursera on Mobile
Students can access all of their course materials wherever they are with the mobile app, which is used by over 80 percent of degree students on Coursera. The app is available on iOS and Android.
Using the mobile app, learners can:
- Save a week’s worth of content for offline access with one click
- Save and submit quizzes offline
- View text transcripts of lecture videos
- Take notes directly in the app
- Set reminder alerts that help you make progress
Download Coursera's mobile app
Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution.
