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Spring Data Services Workshop Series

January 17, 2017
data services

Learn to make the most of your research data through this series of workshops hosted by data librarian Ryan Womack.

If you are generating or using data in your research, a well thought-out approach to data management will save you time and frustration while maximizing the impact of your work. This semester, join data librarian Ryan Womack for the Data Services Workshop Series and learn how to make the most of your research data.

Introduction to SPSS, Stata, and SAS

Introduction to SPSS, Stata, and SAS provides overviews of these three popular commercial statistical software programs, covering the basics of navigation, loading data, graphics, and elementary descriptive statistics and regression using a sample dataset. If you are already using these packages with some degree of success, you may find these sessions too basic for you.

Introduction to R

This workshop provides a three-part orientation to the R programming environment. R is freely available, open source statistical software that has been widely adopted in the research community. Due to its open nature, thousands of additional packages have been created by contributors to implement the latest statistical techniques, making R a very powerful tool. No prior knowledge is assumed. The three parts cover statistical techniques, graphics, and data manipulation.

Data Visualization in R

Data Visualization in R discusses principles for effective data visualization, and demonstrates techniques for implementing these using R. Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background. The three parts are principles and use in lattice and ggplot2; miscellany of methods; and 3-D, interactive, and big data.

Reproducible Research

Reproducible research describes the growing movement to make the products of research accessible and usable by others in order to verify, replicate, and extend research findings. This workshop reviews how to plan research, to create publications, code, and data in open, reusable formats, and maximize the impact of shared research findings. Examples in LaTeX and Rmarkdown are discussed, along with platforms for reusability such as the Open Science Foundation.