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

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

Advanced Coursework: 12 credit hours with a grade of C or higher.
Pick from among: Practical Statistical Learning, Advanced Bayesian Modelling, 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.

MCS Data Science Track Requirements Table

(Click on course title for a brief course description.)

 

Hours

Total Required Credit Hours for MCS-DS (from among the following courses): 32
  Data Mining (pick at least one course):
   
CS 410 Text Information Systems (Syllabus)
4
   
CS 412 Introduction to Data Mining
4
  Data Visualization (pick at least one course):
   
CS 498 Data Visualization
4
  Machine Learning (must take, at least, "Applied Machine Learning"):
   
CS 498 Applied Machine Learning
4
   
STAT 542/CS 598 Practical Statistical Learning
4
  Cloud Computing (pick at least one course):
   
CS 425 Distributed Systems (Cloud Computing Concepts I & II) (Syllabus)
4
   
CS 498 Cloud Computing Applications
4
  Statistical Analysis
   
STAT 420 Methods of Applied Statistics
4
   
STAT 578/CS 598 Advanced Bayesian Modelling (Syllabus)
4
  Information Science
   
CS 598/LIS 531 Foundations of Data Curation (Syllabus)
4
   
CS 598/LIS 590 Theory & Practice of Data Cleaning
4
  Capstone
    CS 598 Data Mining Capstone 4
    CS 598 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, Advanced Bayesian Modelling, 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.

Course Availability

Fall Semester:
CS 410 Text Information Systems (first offered in Fall 2016)
CS 425 Distributed Systems (Cloud Computing Concepts) (first offered in Fall 2016)
CS 598 Foundations of Data Curation (starting Fall 2017)
STAT 578/CS 598 Advanced Bayesian Modelling (starting Fall 2017)

Spring Semester:
CS 412 Intro to Data Mining (first offered Spring 2017)
CS 498 Cloud Computing Applications (first offered in Spring 2017)
CS 498 Applied Machine Learning (starting Spring 2018)
CS 598 Theory & Practice of Data Cleaning (first offered in Summer 2017 but regularly scheduled for Spring beginning in 2018)

CS 598 Cloud Computing Capstone (starting Spring 2018)
CS 598 Data Mining Capstone (starting Spring 2018)

Summer Semester:
CS 498 Data Visualization (first offered in Summer 2017)
STAT 420 Methods of Applied Statistics (first offered in Summer 2017)
STAT 542 Statistical Learning (starting Summer 2018)