Learn how to analyze data using Python and/or R programming languages (via Zoom)

August 11, 2022

Learn essential programming skills during the R workshop series and the Python workshop series. Attend any or all of the sessions. Brought to you as a part of the UW Libraries Graduate Support workshop series. Open to all UW-Madison students, faculty, and staff.

Location:  Online via Zoom; connection information will be sent in advance.

The R Series

*Registration required.  Registration is by workshop, not for the entire series. See links below to register for individual workshops.  Sessions are filling up fast!

To find out more about this series, see: https://researchguides.library.wisc.edu/R

Friday, September 23, 11am-1pm
Programación básica en R
Regístrate: https://go.wisc.edu/kvzxeq

This workshop will be taught in Spanish. See September 30 and October 7 workshops for English version of R Basics workshop.

Aprender a programar puede ser intimidante, pero te ahorrará tiempo a largo plazo. Erwin Lares de la oficina de Research Cyberinfrastructure será el facilitador de este taller introductorio al lenguaje de programación R. Al final de la sesión, te habrás familiarizado con el ambiente de programación de RStudio y serás capaz de cargar, inspeccionar y manipular conjuntos de datos utilizando el paquete dplyr.

Este taller está diseñado para personas con ninguna o poca experiencia programando.

Friday, September 30, 10am-12pm
R Programming:  R Basics
Register: https://go.wisc.edu/e9123d

Learning how to code can be intimidating, but will save you time and effort in the long run. This workshop will cover the basics of R programming. By the end of this session, you will be able to create variables, use pre-defined functions, understand data types, and load and inspect a dataset using RStudio.

This workshop is geared toward programming novices, so no previous experience is required.

Friday, October 7, 10am-12pm
R Programming:  R Basics (repeat)
Register: https://go.wisc.edu/135b9d

This workshop is an exact repeat of the September 30th “R Programming: R Basics” workshop (see above).

Friday, October 14, 10am-12pm
R Programming:  Data Wrangling
Register:  https://go.wisc.edu/930o7p

Data is rarely perfect out of the box. This workshop will cover how to manipulate datasets using an R package called dplyr. After this session, you will be able to select rows and columns, add new columns, remove missing data and create summary tables of your data. A basic working knowledge of R and R studio (functions, operators, data types) would be helpful for you to get the most out of this session.

Friday, October 21, 10am-12pm
R Programming:  Visualization
Register:  https://go.wisc.edu/oa9l8d

So you’re familiar with R, but want to do more with your plots than the base graphics package. This workshop will show you how to use the ggplot2 package in R. After this session, you will be able to create a variety of plot types, alter their aesthetics, and create custom themes. A working knowledge of R, R studio, and dplyr would be helpful for you to get the most out of this session.

Friday, October 28, 10am-12pm
R Programming:  Reports
Register:  https://go.wisc.edu/6×5766

Documenting your analysis in a way that is understandable to a colleague (or yourself 3 months later) can be challenging. One way to make reports more readable, even by people who don’t code, is to alternate human readable text with machine readable code. This workshop will cover creating reproducible reports of this type using knitr. After this session, you will be able to create R markdown documents, add formatted text and executable code blocks, and render the R markdown document into a final report.

The Python Series

*Registration required.  Registration is by workshop, not for the entire series. See links below to register for individual workshops.  Sessions are filling up fast!

To find out more about this series, see: https://researchguides.library.wisc.edu/python

Thursday, September 29, 10am-12pm
Python Programming:  Introduction
Register: https://go.wisc.edu/mau089

This workshop is for the absolute beginner wanting to slowly walk through the process of getting started with Python, a programming language commonly used for data analysis.  We’ll work through installation and setup of some helpful software and introduce basic concepts and terminology used in Python.  Finally, we’ll work together to create your first simple but useful program!

This workshop is geared toward programming novices, so no previous experience is required.

Thursday, October 6, 10am-12pm
Python Programming:  Introduction (repeat)
Register: https://go.wisc.edu/1fb8vr

This workshop is an exact repeat of the September 29th “Python: Introduction” workshop (see above).

Thursday, October 13, 10am-12pm 2pm
Python Programming:  Loops, lists, and functions
Register: https://go.wisc.edu/l1gei7

This workshop will take a deeper dive into Python, covering essential topics such as automating tasks using loops, lists, and functions.

Prerequisite: Understanding of basic Python concepts (i.e. variables, data types)

Thursday, October 20, 10am-12pm
Python programming:  Spreadsheets and data wrangling with pandas
Register: https://go.wisc.edu/u8x21y

Real-world data can be messy.  This workshop will cover a range of topics related to organizing and manipulating spreadsheet data for more effective analysis. We’ll use pandas, a popular and free data analysis library written for Python.

Prerequisite: Understanding of basic Python concepts (i.e. functions, operators, data types)

Thursday, October 27, 10am-12pm
Python Programming:  Data Visualization with seaborn
Register: https://go.wisc.edu/2a411r

In this workshop, we will explore different methods and tools for visualizing data using Python. We’ll use seaborn, a popular and free data visualization library written for Python.

Prerequisite: Understanding of basic Python concepts (i.e. functions, operators, data types)

Thursday, November 3, 10am-12pm
Python Programming: Processing image collections with Pillow
Register: https://go.wisc.edu/vc5710

This workshop will introduce Pillow, a free and beginner-friendly Python library for processing image files. We will walk through a range of Pillow’s image manipulation features, as well as how to automate processing workflows for large image collections.

Prerequisite: Understanding of basic Python concepts (i.e. functions, operators, data types)

Workshop Instructors

Trisha Adamus

Trisha Adamus is a Health Sciences Librarian at University of Wisconsin-Madison (Ebling Library) specializing R programming and data management.

Questions? adamus@wisc.edu

Dave Bloom

Dave Bloom is a Science and Engineering Librarian at University of Wisconsin-Madison specializing research data management.

Questions? david.bloom@wisc.edu

Jordan Craig

Jordan Craig is the Digital Asset Coordinator at Chazen Museum of Art, University of Wisconsin-Madison.

Chris Endemann

Chris Endemann is a Data Science Facilitator at Data Science Hub, University of Wisconsin-Madison.

Corey Halpin

Corey Halpin is the Software Engineer for Internet Scout, University of Wisconsin-Madison.

Sarah Graves

Sarah Graves is a Environmental Observation and Informatics Program Coordinator for The Nelson Institute for Environmental Studies, University of Wisconsin-Madison.

Erwin Lares

Erwin Lares is a PA Research Cyberinfrastructure at University of Wisconsin-Madison.

Casey Schacher

Casey Schacher is a Science and Engineering Librarian at University of Wisconsin-Madison specializing Python programming and data management.

Questions? casey.schacher@wisc.edu

Dylan Schoemaker

Dylan Schoemaker is a PhD student – Plant Breeding and Plant Genetics, University of Wisconsin-Madison

Heather Shimon

Heather Shimon is a Science and Engineering Librarian at University of Wisconsin-Madison specializing research data management..

Questions? heather.shimon@wisc.edu 

Qiuyu Yang

Qiuyu Yang is a Biostatistician for the Department of Surgery at University of Wisconsin School of Medicine and Public Health.

Helpers: 

Elissa Chasen, Scientist – Gratton Lab, University of Wisconsin-Madison

Katie Dunn, Electronic Resources Librarian – Libraries, University of Wisconsin-Madison

Summera Fahmi Khan, University of Wisconsin-Madison

Mike Howe, Gratton Lab, University of Wisconsin-Madison

Ishita Kemeny, Department of Physics, University of Wisconsin-Madison

Elizabeth Manriquez, Scholarly Communication Librarian – Libraries, University of Wisconsin-Madison

Deanna Schneider, Application Developer, Systems Integrator, and Data Analyst, University of Wisconsin-Extension

Immad A Shah, University of Wisconsin-Madison

Mamta Shah, University of Wisconsin-Madison

Sarah Stevens, Data Science Hub Facilitator, University of Wisconsin-Madison

Hridyesh Tewani, University of Wisconsin-Madison

Cassie Varrige, University of Wisconsin-Madison

Aaron Ward, Data Engineer-CHDR,University of Wisconsin-Madison

Maria Widmer, Teaching, Learning, & Technology Manager – MERIT, University of Wisconsin-Madison