We offer Data Services Workshops covering topics such as reproducible research, data visualization, and statistical software skills (including R, SAS, Stata, and SPSS). Workshops are open to all who wish to learn and improve their data skills.

Workshops are announced via the Libraries Events Calendar, Ryan Womack's RyanData blog, @ryandata on Twitter, and on the Data Services Libguide.

Data Analytics and Visualization with Python

Miranda So, Instructor

Python Basics and Data Exploration

  • Friday, March 23, 2:00-3:30 pm (Room 413)
  • Wednesday, March 28, 1:00-2:30 pm (Room 413)

Data Manipulation and Analysis with Python**

  • Friday, March 30, 1:00-2:30 pm (Room 413)
  • Wednesday, April 4, 1:00-2:30 pm (Room 413)
  • Thursday, April 12, 2:00-3:30 (Room 413)

Data Visualization and Machine Learning with Python**

  • Thursday, April 5, 1:00-2:30 pm (Room 415)
  • Wednesday, April 11, 1:00-2:30 pm (Room 413)
  • Thursday, April 19, 1:00-2:30 pm (Room 413)
  • Friday, April 20, 2:00-3:30 pm (Room 413)

**Python workshops on Data Manipulation and Data Visualization assume familiarity with basic Python syntax and programming concepts (looping, conditional structures, data types, etc.). Consider attending Python Basics first if you are not familiar with these.

Statistical Software and Data Workshops, Spring 2018

New Brunswick Libraries Data Workshop Series

Spring 2018

This Spring, Ryan Womack, Data Librarian, will repeat the series of workshops on statistical software and data visualization as part of New Brunswick Libraries Data Management Services. A detailed calendar and descriptions of each workshop are below. The workshop on reproducible research is moving online to YouTube – stay tuned for an upcoming blog post and announcement on its availability.

This semester each workshop topic will be repeated twice, once at the Library of Science and Medicine on Busch Campus, and once at Alexander Library on College Ave. These sessions will be identical except for location. Sessions will run approximately 3 hours. Workshops in parts will divide the time in thirds. For example, the first SPSS, Stata, and SAS workshop (running from 12-3 pm) would start with SPSS at 12 pm, Stata at 1 pm, and SAS at 2 pm. You are free to come only to those segments that interest you. There is no need to register, just come!


Location: The Library of Science and Medicine (LSM on Busch) workshops will be held in the Conference Room on the 1st floor of LSM on Mondays from 12 to 3 pm. The Alexander Library (College Ave) workshops will be held in room 413 of the Scholarly Communication Center (4th floor of Alexander Library) from on Tuesdays from 1:10 to 4:10 pm.

For both locations, you are encouraged to bring your own laptop to work in your native environment. Alternatively, at Alexander Library, you can use a library desktop computer instead of your own laptop. At LSM, we will have laptops available to borrow for the session if you don’t bring your own. Room capacity is 25 in both locations, first come, first served.

If you can't make the workshops, or would like a preview or refresher, screencast versions of many of the presentations are already available at and Additional screencasts are continually being added to this series. Note that the "special topics" [Time Series, Survival Analysis, and Big Data] are no longer offered in person, but are available via screencast.

Calendar of workshops

Wednesday (LSM)
12 noon–3 pm
  Tuesday (Alexander)
1:10 pm–4:10 pm
January 29 Introduction to SPSS, Stata, and SAS January 30
February 5 Introduction to R February 6
February 12 Data Visualization in R February 13

Description of Workshops:

§ Introduction to SPSS, Stata, and SAS (January 29 or January 30) 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.

  • SPSS is widely used statistical software with strengths in survey analysis and other social science disciplines. Copies of the workshop materials, a screencast, and additional SPSS resources can be found here: SPSS is made available by OIRT at a discounted academic rate, currently $100/academic year. Find it at SPSS is also available in campus computer labs and via the Apps server (see below).
  • Stata is flexible and allows relatively easy access to programming features. It is popular in economics among other areas. Copies of the workshop materials, a screencast, and additional Stata resources can be found here: Stata is made available by OIRT via campus license with no additional charge to install for Rutgers users. Find it at
  • SAS is a powerful and long-standing system that handles large data sets well, and is popular in the pharmaceutical industry and health sciences, among other applications. Copies of the workshop materials, a screencast, and additional SAS resources can be found here: SAS is made available by OIRT at a discounted academic rate, currently $100/academic year. Find it at SAS is also available in campus computer labs, online via the SAS University Edition cloud service, and via the Apps server (see below).

Note: Accessing software via

SPSS, SAS, Stata, and R are available for remote access on does not require any software installation, but you must activate the service first at

§ Introduction to R (February 5 or February 6) – This session 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: getting around in R, descriptive statistics, regression, significance tests, working with packages
  • Graphics: comparison of graphing techniques in base R, lattice, and ggplot2 packages
  • Data Manipulation: data import and transformation, additional methods for working with large data sets, also dplyr and other packages from the tidyverse useful for manipulation.

Additional R resources, including handouts, scripts, and screencast versions of the workshops, can be found here:

R is freely downloadable from

§ Data Visualization in R (February 12 or February 13) 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 & Use in lattice and ggplot2: discusses classic principles of data visualization (Tufte, Cleveland) and illustrates them with the use of the lattice and ggplot2 packages. Some of the material here overlaps with Intro to R, pt 2, but at a higher level.
  • Miscellany of Methods: illustrates a wide range of specific graphics for different contexts
  • 3-D, Interactive, and Big Data: presentation of 3-D data, interactive exploration data, and techniques for large datasets. Relevant packages such as shiny and tessera are explored.

Additional R resources can be found here:

R is freely downloadable from


§ Special Topics

Note that the following special topics are no longer covered by in-person workshops, but are available via screencast.