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