background image

School of Computing Graduate Handbook – 2015-2016

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 number of 

credits for any of the three options is 30 from 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, Chris Johnson, Sneha Kumar Kasera, Mike Kirby, Feifei Li, Miriah Meyer, Valerio Pascucci, Jeff Phillips 

(Track Director), Vivek Srikumar, Hari Sundar, Suresh Venkatasubramanian

COURSE CLASSES

Required courses:  must take 4 required courses.

CS 6150 

 

Advanced Algorithms 

CS 6530 

 

Database Systems 

CS 6140  

 

Data Mining   /or/   CS 6350  Machine Learning

CS 6630 

 

Visualization

 

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

Students may substitute other SoC graduate-level courses for elective requirements with approval of the Track Director (especially 

those taught by track faculty). With approval of the supervisory committee, a student may take two elective courses (6 credit hours) 

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 independent study or thesis research hours can be converted into project or thesis 

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

13

ELECTIVES

 : 

Three courses from the following list are required: ( /or/ CS 6140/CS 6350 if not counted above) 

 

CS 7960  

 

Models of Computation for Massive Data   

CS 6210  

 

Advanced Scientific Computing

CS 6230  

 

High-Performance Computing and Parallelization

CS 6300 

 

Artificial Intelligence

CS 6390  

 

Probabilistic Modeling

CS 6640 

 

Image Processing

CS 6340 

 

Natural Language Processing 

CS 6170  

 

Computational Topology

CS 6160  

 

Computational Geometry

CS 6235  

 

Parallel Programming for GPUs/Many Course/Multi-Cores

CS 6480  

 

Advanced Computer Networks

ALGORITHMICS

ANALYTICS

MANAGEMENT

CS 6490  

 

Network Security

background image

School of Computing Graduate Handbook – 2015-2016

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 ap-

plied to other degrees may be used in the program of study as approved by the track director. Students may place out of the 

following requirements by substituting or transferring courses from other institutions at the discretion of the track director.

TRACK FACULTY

Tom Fletcher, Chris Johnson, Sneha Kumar Kasera, Mike Kirby, Feifei Li, Miriah Meyer, Valerio Pascucci, Jeff 

Phillips (Track Director), Vivek Srikumar, Hari Sundar, Suresh Venkatasubramanian

30

COURSE CLASSES

Required courses:  must take 4 required courses. 

CS 6150 

 

Advanced Algorithms

CS 6530 

 

Database Systems 

CS 6140  

 

Data Mining    /or/     CS 6350     Machine Learning

CS 6630 

 

Visualization

 

A student must take four elective courses (twelve hours) which involve the areas related to data, or are directly applicable 

to the student’s dissertation research. Up to three courses (nine hours) may be taken from other departments at the Univer-

sity 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, 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 typically 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. 

ELECTIVES

Three courses from the following list are required: (or CS 6140/CS 6350 if not counted above, or appropriate classes by 

track 

faculty) 

   

CS 7960  

 

Models of Computation for Massive Data   

CS 6210  

 

Advanced Scientific Computing

CS 6230  

 

High-Performance Computing and Parallelization

CS 6300 

 

Artificial Intelligence

CS 6390  

 

Probabilistic Modeling

CS 6640 

 

Image Processing

CS 6340 

 

Natural Language Processing 

CS 6170  

 

Computational Topology

CS 6160  

 

Computational Geometry

CS 6235  

 

Parallel Programming for GPUs/Many Course/Multi-Cores

CS 6480  

 

Advanced Computer Networks

CS 6490  

 

Network Security

ALGORITHMICS

MANAGEMENT

ANALYTICS

background image

School of Computing Graduate Handbook – 2015-2016

MS IN COMPUTING:

 

DATA MANAGEMENT & ANALYSIS

POTENTIAL OUT-OF-DEPARTMENT ELECTIVES

• MATH 5080 Statistical Inference I

• MATH 5090 Statistical Inference II

• MATH 5250 Matrix Analysis

• MATH 6010 Linear Models

• MATH 6020 Multilinear Models

• MATH 6070 Mathematical Statistics

• MATH 6210 Real Analysis

• MATH 7870 Methods of Optimization

• ECE 5510 Random Processes

• ECE 6540 Estimation Theory

• ECE 6520 Information Theory and Coding

• BMI 6020 Foundations of Bioinformatics

• BMI 6105 Statistics for Biomedical Informatics

• BMI 6470 Biomedical Infomation Retrieval