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

August 10, 2021

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!

Friday, September 24, 10am-12pm
R Programming:  R Basics
Register:  https://go.wisc.edu/824m5n

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 1, 10am-12pm
R Programming:  R Basics (repeat)
Register:  https://go.wisc.edu/yzur5b

This workshop is a repeat of the September 24 “R Programming: R Basics” workshop (see above).

 

Friday, October 8, 10am-12pm
R Programming:  Data Wrangling
Register:  https://go.wisc.edu/o57557

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 15, 10am-12pm
R Programming:  Visualization
Register:  https://go.wisc.edu/c0i736

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 and R studio and dplyr would be helpful for you to get the most out of this session.

 

Friday, October 22, 10am-12pm
R Programming:  Reports
Register:  https://go.wisc.edu/08ucyl

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.

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

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!

Thursday, September 23, 10am-12pm
Python Programming:  Introduction
Register:  https://go.wisc.edu/poklr3

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!

 

Thursday, September 30, 10am-12pm
Python Programming:  Introduction (repeat)
Register:  https://go.wisc.edu/fx716s

This workshop is a repeat of the September 23 “Python: Introduction” workshop (see above).

Thursday, October 7, 10am-12pm
Python Programming:  Loops, lists, and functions
Register:  https://go.wisc.edu/n88bpy

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

 

Thursday, October 14,  10am-12pm
Python programming:  Spreadsheets and data wrangling with pandas
Register:  https://go.wisc.edu/8i090k

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.

 

Thursday, October 21, 10am-12pm
Python Programming:  Data Visualization with seaborn
Register:  https://go.wisc.edu/35yb7j

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.

Workshop Instructors


Trisha Adamus

Trisha Adamus is a Health Sciences Librarian at UW-Madison specializing R programming and data management.

Questions? adamus@wisc.edu

 


Casey Schacher

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

Questions? casey.schacher@wisc.edu

 


Heather Shimon

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

Questions? heather.shimon@wisc.edu 

 

Dave Bloom

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

 

 

 


Sarah Graves

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

 

 

Corey Halpin

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

 

 

 

 


Sarah Stevens

Sarah Stevens is the Data Science Hub Facilitator for the Data Science Institute, University of Wisconsin-Madison.

 

 

Helpers: 

Chris Endemann, Data Science Hub Facilitator, University of Wisconsin-Madison

Eriwin Lares, PA Research Cyberinfrastructure, University of Wisconsin-Madison

Zekai Otles, Systems Administrator, Clinical & Health Informatics Institute, University of Wisconsin-Madison

Jennifer Patiño, Data and Digital Scholarship Diversity Resident Librarian, University of Wisconsin-Madiso

Clare Michaud, Data Science Hub Facilitator, University of Wisconsin-Madison

Mary Murphy, Wrap Database Administrator, Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison

Steven Warren, WISCIENCE Public Service in STEM Fellow, University of Wisconsin-Madison

Scott Wildman, Policy and Planning Analyst – Academic Planning and Institutional Research, University of Wisconsin-Madison