Data Science

Developed through a collaborative effort between the Departments of StatisticsMathematicsPhysicsEconomicsGeography, and Political Science, the Data Science program offers the master of science in data science and graduate certificate in data science. The program teaches students to understand data and contribute important insights with the goal of changing the way in which people live, work, and communicate. Through a structured curriculum that provides foundational knowledge as well as application skills, students learn how to confront the most complex problems facing government and private industry

Explanation of Course Numbers

  • Courses in the 1000s are primarily introductory undergraduate courses
  • Those in the 2000–4000s are upper-division undergraduate courses that can also be taken for graduate credit with permission and additional work
  • Those in the 6000s and 8000s are for master’s, doctoral, and professional-level students
  • The 6000s are open to advanced undergraduate students with approval of the instructor and the dean or advising office

DATS 6101. Introduction to Data Science. 3 Credits.

Basic techniques of data science; algorithms for data mining; and basics of statistical modeling. Concepts, abstractions, and practical techniques. Prerequisites: STAT 2118 or permission of the instructor. Recommended background: An undergraduate degree with a strong background in science, mathematics, or statistics. (Same as STAT 6289).

DATS 6102. Data Warehousing. 3 Credits.

Fundamentals and practical applications of data warehousing, including planning requirements, infrastructure, design, and maintenance. Prerequisites: STAT 2118 or permission of the instructor. Recommended background: An undergraduate degree with a strong background in science, mathematics, or statistics.

DATS 6103. Introduction to Data Mining. 3 Credits.

Concepts, principles, and techniques related to data mining; strengths and limitations of various data mining techniques, including classification, association analysis, and cluster analysis. Restricted to candidates for the MS or graduate certificate in data science; permission of the instructor may be substituted. Prerequisites: DATS 6101 or permission of the instructor.

DATS 6201. Numerical Linear Algebra and Optimization. 3 Credits.

This course is a study of linear and quadratic programming, nonlinear equations, global and unconstrained optimization, and general linearly and nonlinearly constrained optimization as used in data science. Restricted to Designed primarily for students in the Data Science program, however other students with appropriate backgrounds can register for the course with permission of the instructor. Prerequisites: MATH 2184 or MATH 2185. Recommended background: An undergraduate degree with a strong background in science, mathematics, or statistics.

DATS 6202. Machine Learning I: Algorithm Analysis. 3 Credits.

This course is a practical approach to fundamentals of algorithm design associated with machine learning. Topics include techniques of statistical and probability theory, combinatorial optimization, and factor graph and graph ensemble as used in machine learning. Restricted to Designed primarily for students in the Data Science program, however other students with appropriate backgrounds can register for the course with permission of the instructor. Recommended background: An undergraduate degree with a strong background in science, mathematics, or statistics. (Same as PHYS 6620).

DATS 6203. Machine Learning II: Data Analysis. 3 Credits.

This course is a practical approach to fundamentals of machine learning with an emphasis on data analysis; i.e., how to extract useful information from different datasets Topics include linear models, error and noise, training and testing methods, and generalization as used in machine learning. Restricted to Designed primarily for students in the Data Science program, however other students with appropriate backgrounds can register for the course with permission of the instructor. Prerequisites: DATS 6101 - Introduction to Data Science. Recommended background: An undergraduate degree with a strong background in science, mathematics, or statistics. (Same as PHYS 6720).

DATS 6401. Visualization of Complex Data. 3 Credits.

This course is a practical approach to fundamentals of data visualization specifically for data science professional. It covers all significant topics, including graphics, discrete and continuous variables, clustering and classification. Restricted to candidates for the MS or graduate certificate in data science; permission of the instructor may be substituted. Prerequisites: DATS 6101, DATS 6102, and DATS 6103.

DATS 6402. High Performance Computing and Parallel Computing. 3 Credits.

This course is a practical approach to high performance computing specifically for the data science professional. It covers topics such as parallel architectures and software systems, and parallel programming. Restricted to candidates for the MS or graduate certificate in data science; permission of the instructor may be substituted. Prerequisites: DATS 6101, DATS 6102, and DATS 6103.

DATS 6450. Topics in Data Science. 3 Credits.

The purpose of DATS 6450 being a topics course is to respond to new ideas and issues in the rapidly evolving fields of Data Science and Big Data. Possible topics may include new application areas in Big Data, emerging new languages and development systems, and policy issues arising from impacts of Big Data on individuals and society. Restricted to Intended primarily for students in the Data Science Master's and Certificate programs. Prerequisites: DATS 6101 Introduction to Data Science or permission of instructor. Recommended background: Enrollment in a Data Science graduate program.

DATS 6499. Data Science Applied Research. 3 Credits.

Students conduct research projects under the supervision of the instructor. Project topics build on the knowledge and skills acquired during the data science program. Permission of instructor required.

DATS 6501. Data Science Capstone. 3 Credits.

The course is a final practical application of the knowledge and skills acquired during the data science curriculum. The topics of the capstone team projects will be chosen in consultation with the Capstone Course instructor and the members of the teams. The course is designed to help students transition into the data science profession. Restricted to Designed for students in their last semester of the Data Science program as their final required core course. Prerequisites: Eight courses in the Data Science program, including the core courses 6101, 6102, and 6103 plus five approved courses from the categories Intermediate Analytics, Advanced Analytics, and Electives. Recommended background: Completion of the required courses in the Data Science Master's program.