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.

**13**

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.

**30**

**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