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The Department of Statistics and Data Sciences at The University of Texas at Austin is hosting the 13th annual UT Summer Statistics Institute (SSI) May 26–29, 2020.  


  • SSI attracts participants from academia, health professions and marketing firms.
  • Participants acquire new statistical knowledge and skills through hands-on data analysis.
  • SSI provides an exciting venue to build statistical knowledge alongside a diverse and dynamic audience.

UT's Summer Statistics Institute (SSI) offers intensive four-day workshops on diverse topics from introductory data sciences to advanced statistics. Whether you are new to data analysis or a seasoned statistician, SSI provides a unique hands-on opportunity to acquire valuable skills directly from experts in the field. The UT Summer Statistics Institute (SSI) is open to 700 participants.

Courses span introductory statistics, statistical software, statistical methods and statistics applications. Each course will meet for four half-days, either mornings or afternoons, for a total of twelve hours. Instructors will post lectures, datasets, exercises, and course information on a website accessible to enrolled participants. There will be no examinations, and participants will receive certificates upon completion. Academic credit will not be issued. Please carefully check the specified prerequisite knowledge before enrolling in a course.

The Department of Statistics and Data Sciences now offers credit for educational professional development opportunities during our 2019 Summer Statistics Institute! Teachers currently working in PK–12 settings can earn 12 hours of Continuing Professional Education (CPE) by attending one of our 24 exciting SSI courses held May 26-29, 2020. 

Courses will be held on the UT Campus in Patton Hall (RLP), Flawn Academic Center (FAC) and Robert A. Welch Hall (WEL).

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(9:00 AM - 12: 00 Noon)


(1:30 PM - 4:30 PM)


Data Analysis using SPSS

Intermediate Excel for Personal and Professional Use

Introduction to Data Analysis and Graphics Using R

Data Analysis using SAS 

Data Analysis and Graphics Using R

Introduction to GIS

Introduction to SQL and Relational Database Design


BIg Data Analytics: Theory and Methods

Informed Decision Making from Data: Regression Analysis

Introduction to Bayesian Statistics 

Introduction to Statistics

Structural Equation Modeling

Statistical Methods for Categorical Data - Logistic Regression and Beyond

Causal Inference with Observational Data

Geospatial Data Analysis in R

Introduction to Statistics

Scalable Machine Learning: Methods and Tools

Time Series Analytics  


Applied Hierarchical Linear Modeling

Common Mistakes in using Statistics: Spotting Them and Avoiding Them 

Statistics for the Dissertation

Non-Parametric Statistical Methods

The Power and Pleasure of Probability