Data Science: MS
1. Develop innovative software solutions that improve the efficiency and scope of data science tools
2. Apply existing programming tools, languages, and algorithms to build, clean, and process large datasets as efficiently as possible
3. Identify and construct computational solutions to solve problems from a diversity of domains
4. Expand the functionality of state-of-the-art, high-performance data science software
With a master's degree in Data Science, students may pursue a career as a data scientist, data analyst, data engineer, machine learning scientist or machine learning engineer.
MS in Data Science admission requirements include:
1. A bachelors degree in Computer Science or closely related field.
2. Coursework in basic Statistics (equivalent to STAT 3000).
3. These are in addition to graduate schools requirements that can be found at: http://rgs.usu.edu/graduateschool/admissions
Curtis Dyreson, PhD, University of Arizona
Associate Professor, Graduate Advisor
Area: Software systems, temporal databases, native XML databases, data cubes, and providing support for proscriptive metadata
Office: MAIN 402 A
Phone: (435) 797-0742
Douglas Galarus, PhD, Montana State University
Area: Data science, big data, machine learning, systems and software engineering, mobile/web/embedded development, computer science education, spatio-temporal and geo-computing
Office: Main 401D
John Edwards, PhD, The University of Texas
Area: Geometric Modeling, Simulation, Scientific Visualization
Office: MAIN 401D
Phone: (435) 797-0246
Xiaojun Qi, PhD, Louisiana State University
Area: Content-based image/video retrieval, digital watermarking, digital steganography/steganalysis, image processing, pattern recognition, computer vision, image forensics
Office: MAIN 401 C
Phone: (435) 797-8155
Vladimir Kulyukin, PhD, University of Chicago
Area: Assistive technology, mobile and ubiquitous computing, human-robot interaction, information retrieval
Office: MAIN 401 E
Phone: (435) 797-8163
Shuhan Yuan, Ph.D in computer engineering, Thongji University, Shanghai, China
Area: Machine Learning, Deep Learning, Artificial Intelligence, Data Mining, Natural Language Processing.