University of Illinois
Sit in on a Degree Course
You don’t have to wait to get started towards your MCS degree — you can try out an open course online and begin learning today. If you later apply and are admitted to the degree program, your assignments completed in open courses can count toward completion of degree courses.
Take one of these open courses or Specializations to learn from Illinois MCS degree instructors and complete assignments that give you a head start on degree courses.
- Cloud Computing Specialization
- Cloud Computing Concepts, Part 1
- Data Mining Specialization
- Data Visualization
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.
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. 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.
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:
Coursework focusing on tool-oriented and problem-directed approaches to machine learning with applications in computer vision, natural language processing, geopositioning, and voice & music.
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.
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.
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
|Master of Computer Science (MCS)||Master of Computer Science in Data Science (MCS-DS)|
|Must complete four courses (16 credit hours) each from a different area, from the following core areas with a grade of B- or higher.||Must complete one course each (with a grade of B- or higher) from the four different areas of machine learning, data mining, data visualization and cloud computing.|
|Artificial Intelligence: CS 498 Applied Machine Learning, CS 445 Computational Photography||Machine Learning: CS 498 Applied Machine Learning, CS 445 Computational Photography|
|Database and Information Systems: CS 410 Text Information Systems, CS 411 Database Systems, CS 412 Introduction to Data Mining||Data Mining: CS 410 Text Information Systems, CS 411 Database Systems, CS 412 Introduction to Data Mining|
|Graphics/HCI: CS 418 Interactive Computer Graphics, CS 498 Data Visualization||Data Visualization: CS 498 Data Visualization|
|Parallel Computing: CS 484 Parallel Computing||Cloud Computing: CS 425 Cloud Computing Concepts, CS 498 Cloud Computing Applications, CS 498 Cloud Networking|
|Programming Languages & Software Engineering: CS 421 Programming Languages and Compilers, CS 427 Software Engineering I||–|
|Scientific Computing: CS 450 Numerical Analysis||–|
|Systems and Networking: CS 425 Cloud Computing Concepts, CS 498 Cloud Computing Applications, CS 498 Cloud Networking||–|
For MCS & MCS-DS: Must complete three courses (12 credit hours)
- CS 513 Theory and Practice of Data Cleaning
- CS 598 Foundations of Data Curation
- CS 598 Practical Statistical Learning*
- CS 598 Advanced Bayesian Modeling
- CS 598 Cloud Computing Capstone*
- CS 598 Data Mining Capstone*
- Prerequisites apply.
For MCS: Not required. Counts toward the eight courses required to earn the degree.
For MCS-DS: Not required. 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 Computing
- CS 498 Internet of Things
- STAT 420 Methods of Applied Statistics
- All coursework must be taken through the Coursera platform
- Breadth coursework must be completed with a letter grade of B- or higher
- All other courses taken for a letter grade must be completed with 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 towards MCS degree requirements
The availability of specific courses is subject to change. For the latest information, see the Course Availability section of the Illinois Online MCS or Illinois Online Master of Computer Science in Data Science page.
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|
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.