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Graduate Degree
Data Science - Master of Science
Program:
Data Science - Master of Science
Plan Code:
DATASCI_MS
Program Level:
Graduate
Award Type:
Master of Science
College:
College of Natural Science
Department:
Statistics and Probability
Excerpt from the official Academic Programs Catalog:
Listed below are the approved requirements for the program from the official Academic Programs Catalog.
Students must consult their advisors to learn which specific requirements apply to their degree programs.
College of Natural Science
Department of Statistics and Probability
Graduate Study
Data Science - Master of Science
View this text within the context of the catalog.
The Master of Science degree in Data Science is designed to provide students with an interdisciplinary blend of statistics, computer science, and computational science and mathematics which provides the necessary training to assimilate, process, analyze, and interpret data from diverse sources.
Admission
To be considered for admission to the master’s degree, a student must:
Have a four-year bachelor’s degree in a relevant quantitative discipline.
Demonstrate sufficient quantitative preparation through work or other relevant experiences.
In addition to meeting the requirements of the university and of the College of Natural Science, students must meet the requirements specified below.
Requirements for the Master of Science Degree in Data Science
A total of 30 credits is required for the degree under Plan B (without thesis). The student’s program of study must be approved by the student’s guidance committee and must meet the requirements specified below.
1.
All of the following courses (18 credits):
CMSE
830
Foundations of Data Science
3
CMSE
831
Computational Optimization
3
CSE
482
Big Data Analysis
3
CSE
881
Data Mining
3
STT
810
Mathematical Statistics for Data Scientists
3
STT
811
Applied Statistical Modeling for Data Scientists
3
2.
Complete 9 credits of elective courses from the following:
CMSE
402
Data Visualization Principles and Techniques
3
CMSE
822
Parallel Computing
3
CMSE
890
Selected Topics in Computational Mathematics, Science, and Engineering
1 to 4
CSE
802
Pattern Recognition and Analysis
3
CSE
830
Design and Theory of Algorithms
3
CSE
847
Machine Learning
3
STT
802
Statistical Computation
3
STT
812
Statistical Learning and Data Analysis
3
STT
873
Statistical Learning and Data Mining
3
STT
874
Introduction to Bayesian Analysis
3
STT
875
R Programming for Data Sciences
3
CMSE 890 must be approved by the student’s guidance committee.
Other courses may be available to fulfill this requirement with advisor approval.
3.
Completion of a 3-credit capstone course involving an applied, industrial, or governmental data science project. Students may complete this requirement by enrollment in Computer Science and Engineering 890, Computational Mathematics, Science, and Engineering 890, or Statistics and Probability 890. The student’s topic area must be approved by the student’s guidance committee.
4.
Completion of a final examination or evaluation.
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