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School of Computing Graduate Handbook - 2013-2014

MS IN COMPUTING: 

DATA MANAGEMENT & ANALYSIS

A student may pursue an MS with a (1) thesis option, or (2) a project option, or (3) a course-only option. The minimum num-

ber of credits for either option is 30 graduate level classes. 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.

TRACK FACULTY

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

Venkatasubramanian

ELECTIVES

Three courses from the following list are required: 

 

 

 

CS 6230 

 

High-Performance Computing and Parallelization

CS 6964 

 

Applications of NLP

CS 6610 

 

Interactive Computer Graphics

CS 6235 

 

Parallel Programming for GPUs/Many Cores/Multi-Cores

CS 6640 

 

Image Processing

CS 6340 

 

Natural Language Processing 

CS 6300 

 

Artificial Intelligence

CS 6220 

 

Advanced Scientific Computing II

CS 6210 

 

Advanced Scientific Computing I  

COURSE REQUIREMENTS

Required courses:  must take 4 required courses.

CS 6350    Machine Learning   /   CS 6955   Data Mining   /   CS 6960  Non-Parametric Statistics

CS 6530 

 

Database Systems 

CS 6150 

 

Advanced Algorithms 

CS 6630 

 

Scientific Visualization

 

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

In addition to the electives list, students may take any graduate-level courses taught by any track committee faculty mem-

bers 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 track director.

In all three options, seminar hours cannot be included to fulfill the 30 graduate level credits requirement. Independent 

study credit hours can only be used on the Program of Study for students who pursue the project based degree. However, 

once a student enters the project or the thesis option, his/her prior indeptent study or thesis research hours can be convert-

ed into project or thesis hours whichever is applicable, if the student’s advisor deems these hours relevant to the project or 

the thesis the student will be working on.

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School of Computing Graduate Handbook - 2013-2014

PHD IN COMPUTING: 

DATA MANAGEMENT & 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 track director.

TRACK FACULTY

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

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 SoC, the students needs to take 6000 level courses and above when available/appropriate. In addition to the 

following electives, other 6000 level and above classes taught by track faculty are also allowed as electives. All courses taken 

by a track student to fulfill the elective requirements must be approved by the student’s committee and the track director. 

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ADDITIONAL ELECTIVES

• IS 6481 Data Warehousing

• IS 6482 Data Mining

• 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

COURSE REQUIREMENTS

Required courses:  must take 4 required courses.

CS 6350    Machine Learning   /   CS 6955   Data Mining   /   CS 6960  Non-Parametric Statistics

CS 6530 

 

Database Systems 

CS 6150 

 

Advanced  Algorithms 

CS 6630 

 

Scientific Visualization

 

ELECTIVES

Three courses from the following list are required: 

 

 

 

CS 6230 

 

High-Performance Computing and Parallelization

CS 6964 

 

Applications of NLP

CS 6235 

 

Parallel Programming for GPUs/Many Cores/Multi-cores

CS 6640 

 

Image Processing

CS 6340 

 

Natural Language Processing 

CS 6300 

 

Artificial Intelligence

CS 6220 

 

Advanced Scientific Computing II

CS 6210 

 

Advanced Scientific Computing I  

CS 6610 

                Interactive Computer Graphics 

CS 5610 

                 Interactive Computer Graphics  

• 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

• ECE 5510 Random Processes

• ECE 6520 Information Theory and Coding

• ECE 6551 Survey of Optimization Techniques

• ECE 6540 Estimation Theory