MCS-Data Science Track Requirements

The Professional MCS track in Data Science is a non-thesis (no research) degree that requires 32 credit hours of graduate coursework. This program is completed online. Students can complete the eight courses required for the MCS-DS at their own pace, within a five-year window.

Degree Requirements

MCS Data Science Track Table.

Breadth Requirement: 12-16 credit hours.
Must complete four different courses, each from a different area, from the following areas with a grade of B- or higher:

  • Artificial Intelligence: Applied Machine Learning
  • Database, Information Systems, Bioinformatics: Text Information Systems, Introduction to Data Mining
  • Graphics/HCI: Data Visualization
  • Systems and Networking (includes real-time systems and security): Cloud Computing Concepts, Cloud Computing Applications, Cloud Networking

Advanced Coursework: 12 credit hours with a grade of C or higher.
Pick from among: Practical Statistical Learning, Multivariate Analysis, Foundations of Data Curation, Theory Practice of Data Cleaning, Data Mining Capstone, Cloud Computing Capstone.

Additional Requirements

  • At least 24 credit hours must be taken in computer science offered by the University of Illinois at Urbana-Champaign.
  • Any 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 may be transferred and applied to the Professional MCS degree requirements. In addition, 12 credit hours of non-degree graduate courses completed within the Department of Computer Science at the University of Illinois Urbana-Champaign may be transferred and applied to the MCS degree requirements.
  • Online MCS-DS students have up to 5 years in which to complete the degree.

Financial Assistance

The Department of Computer Science does not offer research or teaching assistantships to students enrolled in our online programs, including the MCS Data Science track.

MCS Data Science Track Table

 

Hours

Total Required Credit Hours for MCS-DS (from among the following courses): 32
  Data Mining (pick at least one course):
   
Text Information Systems
4
   
Introduction to Data Mining
4
  Data Visualization (pick at least one course):
   
Data Visualization
4
  Machine Learning (must take, at least, "Applied Machine Learning"):
   
Applied Machine Learning
4
   
Practical Statistical Learning
4
  Cloud Computing (pick at least one course):
   
Cloud Computing Concepts
4
   
Cloud Computing Applications
4
   
Cloud Networking
4
  Statistical Analysis
   
Statistics and Probability
4
   
Methods of Applied Statistics
4
   
Multivariate Analysis
4
  Information Science
   
Foundations of Data Curation
4
   
Theory & Practice of Data Cleaning
4
  Capstone
    Data Mining Capstone 4
    Cloud Computing Capstone 4
 
Other Requirements and Conditions (may overlap):
  Required Hours in Advanced Courses (pick at least three 500-level courses): 12
    Practical Statistical Learning, Multivariate Analysis, Foundations of Data Curation, Theory Practice of Data Cleaning, Data Mining Capstone, Cloud Computing Capstone
  Breadth Requirement (must complete four different CS courses, each from a different area): 12 - 16
    Must take “Applied Machine Learning,” plus at least one course from each of the following areas (above): Data Mining, Data Visualization, and Cloud Computing. A grade of B- or higher is required for Breadth coursework.
  Elective Course Requirement 4 - 8
  Minimum Program GPA 3.0
  A minimum of 24 CS credit hours must be taken from the University of Illinois at Urbana-Champaign.
  At most, 12 semester credit hours of previous graduate coursework may be transferred and applied to the MCS degree requirements
  At most, 12 credit hours of non-degree graduate coursework completed in the Department of Computer Science at the University of Illinois at Urbana-Champaign may be transferred and applied to the MCS degree requirements.
  Online MCS-DS students have up to 5 years in which to complete the degree.