Online Master of Computer Science in Data Science
Complete a Master’s degree online in the hottest area of the new millennium! Learn about new statistical and computational methods that are transforming business and society from the Illinois faculty who are pioneering them. Illinois Computer Science offers a specialized track of courses that satisfy the MCS degree requirements, but with coursework that focuses on data science — the art of extracting new knowledge and finding meaningful information in a massive sea of data.
The Master of Computer Science in Data Science (MCS-DS) track is a non-thesis (coursework-only) program of study that leads to the MCS degree using courses that focus on data science. The MCS-DS track requires 32 credit hours of graduate coursework, completed through eight graduate-level courses each at the four credit hour level. The MCS-DS includes required coursework in machine learning, data mining, data visualization and cloud computing.
Students receive lectures through Coursera's massive open online course (MOOC) platform, but are advised and assessed by Illinois faculty and teaching assistants on the more rigorous set of assignments, projects, and exams required for university degree credit.
Students who would like to take a more general set of courses should consider the University of Illinois Online MCS.
Who may apply? Applicants should hold a 4-year bachelor's degree (or equivalent). Students with a bachelor’s degree in a field other than CS are encouraged to apply, but to succeed in graduate-level CS courses, they must have prerequisite coursework or commensurate experience in object-oriented programming, data structures, algorithms, linear algebra, and statistics/probability. The recommended undergraduate GPA for applicants applying to the Professional Master's program is a 3.2/4.0 or higher. The Department of Computer Science does not require GRE scores for any of its graduate programs. Applications for the MCS do not require letters of recommendation, but will be considered if included, especially if used to justify experience in lieu of required coursework, or other irregularities.
Note: Each MCS-DS course is four credit hours.
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.
|Machine Learning:||CS 498 Applied Machine Learning (Spring)|
|Data Mining:||CS 410 Text Information Systems (Fall)
CS 411 Database Systems (Spring)
CS 412 Introduction to Data Mining (Spring)
|Data Visualization:||CS 498 Data Visualization (Summer)|
|Cloud Computing:||CS 425 Cloud Computing Concepts (Fall)
CS 498 Cloud Computing Applications (Spring)
CS 498 Cloud Networking (Fall)
Must complete three courses (12 credit hours).
- CS 513 Theory and Practice of Data Cleaning (Summer)
- CS 598 / IS 531 Foundations of Data Curation (Fall)
- CS 598 / STAT 542 Practical Statistical Learning* (Fall)
- CS 598 / STAT 578 Advanced Bayesian Modeling (Spring)
- CS 598 Cloud Computing Capstone* (Fall & Summer)
- CS 598 Data Mining Capstone* (Spring & Summer)
* Prerequisites apply. Please see printable MCS-DS Degree Requirements document.
Not required, but available to use toward the eight courses required for the degree.
- CS 421 Programming Languages and Compilers (Spring)
- CS 427 Software Engineering I (Fall)
- CS 450 Numerical Analysis
- CS 484 Parallel Computing (Spring)
- STAT 420 Methods of Applied Statistics (Summer)
- 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.
CS 410 Text Information Systems
CS 425 Distributed Systems (Cloud Computing Concepts)
CS 498 Cloud Networking
CS 598 Foundations of Data Curation
CS 598 / STAT 578 Advanced Bayesian Modelling
CS 598 / STAT 542 Practical Statistical Learning
CS 598 Cloud Computing Capstone
CS 411 Database Systems
CS 412 Intro to Data Mining
CS 498 Cloud Computing Applications
CS 498 Applied Machine Learning
CS 598 Data Mining Capstone
CS 498 Data Visualization
STAT 420 Methods of Applied Statistics
CS 513 Theory & Practice of Data Cleaning
CS 598 Cloud Computing Capstone
CS 598 Data Mining Capstone
- Tuition: $600 per credit hour, for a total of $19,200 for the complete 32 credit hour degree. As of Fall 2019, tuition is expected to be $670 per credit hour, for a total of $21,440 for the complete 32 credit hour degree.
- Coursera fees: $79 per each Coursera MOOC course that is applied toward the Online MCS. (Each credit-bearing course of enrollment at the University of Illinois has two associated MOOC courses.) Note: If a required Coursera course has been completed for a certificate before the official Online MCS course started, students do not need to re-pay. More information on Coursera payments and Coursera financial aid can be found here. As of Fall 2019, Coursera fees will no longer be assessed.
- ProctorU fees: Most Online MCS courses require one or more exams. Exams are proctored online through the ProctorU service, which will be billed directly to the student when an exam is scheduled, at $8.75 (30-min. exam), $14.75 (1-hr. exam), $21.50 (90-min. and 2-hour exam), or $30.25 (3-hour exam).
- Other fees: Some courses can require additional fees, such as the Amazon Web Services cloud programming platform used for the cloud computing courses
The Department of Computer Science does not offer research or teaching assistantships to students enrolled in our online programs, including the Online MCS. The Online MCS program is accredited by the Higher Learning Commission and enrolled students are eligible for financial assistance. See the Office of Student Financial Aid for further information.
If you are interested in receiving updates about the Online MCS, please complete Coursera's Interest Form.