Data Science Concentration Requirements

MS students wishing to add the Data Science Concentration to their MS degree are asked to submit the pre-application and are required to:

• Complete 30-course credits meeting the Data Science Course Requirements (courses taken to satisfy core/elective/statistic requirements are included)
• Satisfy all MS in CS core/course requirements (courses taken to satisfy Data Science requirements are included)
• Satisfy 4 Data Science Core Requirements
• Satisfy 2 Data Science Elective Requirements
• Satisfy 1 Data Science Statistics Requirement

Data Science Course Requirements

1. Core requirements. You must have satisfied four Data Science core requirements (one from each of three areas, plus one additional requirement from any of the three areas). This requirement is usually satisfied by taking courses and getting a B or better in them.
2. Elective Requirements. You must have satisfied two Data Science elective requirements 
3. Statistics Requirement. You must have satisfied one Data Science statistic requirement
4. Credits. You must take a total of 30 credits with the following restrictions:
   • No more than 18 of the course credits may come from courses at the 500 level. 500-level classes taken to satisfy core requirements fall into this group.
   • At least 12 of those credits must come from courses at the 600-900 level that are not independent studies. 600-level classes taken to satisfy core requirements fall into this group.
   • No more than 12 credits may come from independent studies 
   • No more than 9 credits may come from courses outside of the Computer Science Department. (Credit for graduate courses from other departments must be approved by the GPD.)
   • No more than 6 credits may be taken pass/fail
   • Classes with a grade below a C may not be counted toward the MS degree.
   • Only a limited number of credits may be transferred from other programs or institutions.
5. GPA. Your overall grade point average for those 30 credits must be 3.0 or higher.

Data Science Core Requirements

All DataSci core courses can be used toward the CompSci MS core requirements. 

Data Science Theory Courses

The following course can be used to complete the Theory for DS core requirement:

• Algorithms for Data Science (COMPSCI 514)
• Advanced Algorithms (COMSPCI 611)
• Optimization for Computer Science (COMPSCI 651)

Data Systems Cores 

The following course can be used to complete the Systems for DS core requirement:

• Systems for Data Science (COMPSCI 532)
• Database Design and Implementation (COMSPCI 645)
• Distributed and Operating Systems (COMPSCI 677)

Data Science AI Cores

The following courses can be used to complete the Data Analysis core requirement:

• Natural Language Processing (COMPSCI 585)
• Machine Learning (COMPSCI 589)
• Data Visualization and Exploration (COMPSCI 590V)
• Neural Networks: A Modern Introduction (COMPSCI 682)
• Artificial Intelligence (COMPSCI 683)
• Reinforcement Learning (COMPSCI 687)
• Machine learning: pattern classification (COMPSCI 689)
• Advanced Natural Language Processing (COMPSCI 685 or 690N)
• Visual Analytics/Computer Vison (COMPSCI 690V)

Data Science Elective Requirements

Students must complete two of the following courses with a grade of B or better. Courses that are crossed-listed as core and elective may only satisfy one area requirement. Outside courses on this list are preapproved and can count toward the CompSci MS core/course requirements.

Data Science Statistics Requirements

Students must complete one of the following courses with a grade of B or better. Outside courses on this list are preapproved and can count toward the CompSci MS core/course requirements