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Engineering

Applied Data Science Graduate Certificate at University of Kansas

University of Kansas, United States

Programme overview

This 15-credit-hour certificate teaches students to extract, refine, analyze and deliver data using statistical and computational methods for large datasets, so they can apply common statistical methods in their jobs.

Faculty: School of Medicine (KU Medical Center)  |  Department: Department of Biostatistics & Data Science  |  Duration: Typical full-time plan completes in one year (fall, spring, summer); starts in fall, spring or summer  |  Location: Lawrence, KS, United States

What you will study

STAT 820 (SAS) or STAT 823 (R), STAT 840 Linear Regression, STAT 850 Multivariate Statistics, DATA 824 Data Visualization and Acquisition, and DATA 880 Data Mining and Analytics or DATA 881 Statistical Learning I.

Official programme page

Application information

Entry requirements

The application process for the Graduate Certificate in Applied Data Science is an online process. Detailed instructions on how to apply are posted on the Department of Biostatistics & Data Science website. Students may be admitted to begin the fall, spring or summer semester. Admission requirements: A bachelor's degree from a regionally accredited institution documented by submission of an official transcript indicating the degree has been conferred before entering the program. Students with degrees from outside the U.S. may be subject to transcript evaluation indicating the degree is equivalent to a U.S. degree and meets the minimum cumulative GPA requirements.

Tuition is charged per credit, so the total depends on the modules you register for each year. Current fees are listed in the official fee schedule.

Entry terms follow the KU academic calendar; the application for first-year and transfer students is submitted through the KU application portal (go2.ku.edu). The fall freshman scholarship deadline is Dec. 1, the FAFSA priority deadline is Feb. 1 and the fall transfer scholarship deadline is Aug. 1.

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