Apply for Summer 2021 by February 15, 2021.
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.
Also, if you do not have graded and transcripted prerequisite CS coursework in the areas of data structures, algorithms, and object-oriented programming, check out our new Accelerated Computer Science Fundamentals Specialization that is designed to help you prepare for the Data Structures Proficiency Exam, which can strengthen your application for admission.
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:
Build expertise and career skills in the most important computer science topics. Courses and projects cover subjects like:
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.
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.
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).
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.
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.
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.
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.
|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)
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.
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 ($7,875 Tuition Due)||CS 598 / IS 531 Foundations of Data Curation, CS 598 / STAT 578 Advanced Bayesian Modeling ($5,250 Tuition Due)|
|Spring||CS 411 Database Systems, CS 498 Applied Machine Learning ($5,250 Tuition Due)||CS 411 Database Systems ($2,625 Tuition Due)|
|Summer||CS 498 Data Visualization, CS 513 Theory and Practice of Data Cleaning, Stat 420 Methods of Applied Statistics ($7,875 Tuition Due)||CS 498 Data Visualization, CS 513 Theory and Practice of Data Cleaning ($5,250 Tuition Due)|
|Fall||CS 425 Cloud Computing Concepts ($2,625 Tuition Due)|
|Spring||CS 498 Applied Machine Learning ($2,625 Tuition Due)|
|Summer||Stat 420 Methods of Applied Statistics ($2,625 Tuition Due)|
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.
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:
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.