PhD and MS in Computing: Data Management and Analysis

Track Faculty

Tom Fletcher
Mike Kirby
Feifei Li (Track Director)
Miriah Meyer
Valerio Pascucci
Jeff Phillips
Suresh Venkatasubramanian

MS in Computing: Data Management and Analysis

A student may pursue an M.S. with a (1) thesis option, or (2) a project option, or (3) a course-only option. The minimum number of credits for either option is 30 graduate level classes (this includes 5000 and 6000 level courses as designated by departments). A maximum of 6 project hours or 9 thesis hours is allowed to be included in the program of study for students in the project or the thesis option. A minimum of 6 hours of thesis research is required for the thesis option.

COURSE REQUIREMENTS: Must take 4 required courses.
CS 6150    Algorithms
CS 6350    Machine Learning / CS 6955 Data Mining / CS 6960 Non-Parametric Statistics
CS 6530    Database Systems
CS 6630    Scientific Visualization

A minimum of a B or greater is required for any of the required courses.


ELECTIVES: Three courses from the following list are required.
CS 5610    Interactive Computer Graphics
CS 6210    Advanced Scientific Computing I
CS 6220    Advanced Scientific Computing II
CS 6230    High Performance Parallel Computing
CS 6300    Artificial Intelligence
CS 6340    Natural Language Processing
CS 6610    Advanced Computer Graphics I
CS 6640    Image Processing
CS 6963    Parallel Programming for GPUs
CS 6964    Applications in NLP

In addition to the electives list, students may take any graduate-level courses taught by any track committee faculty members to fulfill the elective requirements. With approval of the supervisory committee, a student may take two elective courses at the graduate level or higher from other departments, excluding independent study, seminars and research credit. Students may place out of the above requirements by substituting or transferring courses from other institutions at the discretion of the TCF Chair.

In all three options, neither directed independent study (DIS) nor seminar hours can be included to fulfill the 30 graduate level credits requirement. However, once a student enters the project or the thesis option, his/her prior DIS hours can be converted into project or thesis hours if the student's advisor deems these DIS hours relevant to the project or the thesis the student will be working on.


PhD in Computing: Data Management and Analysis

Course work listed on the approved Program of Study form must comprise at least 50 semester hours of graduate course work and dissertation research, exclusive of independent study. At least 14 semester hours of dissertation research (CS 7970) and 24 semester hours of graduate course work must be included. Up to 12 hours of graduate level course work already applied to other degrees may be used in the program of study as approved by the TCF Chair.

COURSE REQUIREMENTS: Must take 4 required courses.
CS 6150    Algorithms
CS 6350    Machine Learning / CS 6955 Data Mining / CS 6960 Non-Parametric Statistics
CS 6530    Database Systems
CS 6630    Scientific Visualization

A minimum of a B or greater is required for any of the required courses.

A student must take five elective courses (fifteen hours) which involve the areas related to information, or are directly applicable to the student's dissertation research. Up to three courses (nine hours) may be taken from other departments at the University of Utah. All elective courses on the Program of Study must be taught at the graduate level. For those classes taken within the School of Computing, it is advised that students take 6000 level courses and above when available/appropriate. All courses taken by a track student to fulfill the elective requirements must be approved by the student's committee and the TCF Chair.


ELECTIVES: Three courses from the following list are required.
CS 5610    Interactive Computer Graphics
CS 6210    Advanced Scientific Computing I
CS 6220    Advanced Scientific Computing II
CS 6230    High Performance Parallel Computing
CS 6300    Artificial Intelligence
CS 6340    Natural Language Processing
CS 6610    Advanced Computer Graphics I
CS 6640    Image Processing
CS 6963    Parallel Programming for GPUs
CS 6964    Applications in NLP

Additional Electives

  • Math 5010 Introduction to Probability
  • Math 5080 Statistical Inference I
  • Math 5090 Statistical Inference II
  • Math 5250 Matrix Analysis
  • Math 6010 Linear Models
  • Math 6020 Multilinear Models
  • Math 7870 Methods of Optimization
  • ECE 5510 Random Processes
  • ECE 6540 Estimation Theory
  • ECE 6520 Information Theory and Coding
  • ECE 6551 Survey of Optimization Techniques
  • IS 6481 Data Warehousing
  • IS 6482 Data Mining
  • BMI 6010 Foundations of Medical Informatics
  • BMI 6020 Foundations of Bioinformatics and Genetic Epidemiology
  • BMI 6105 Statistics for Biomedical Informatics
  • BMI 6300 Medical Decision-Making