Learn how to use Python and/or R programming languages for data analysis (via Zoom)
Learn programming skills for computational research 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: Instruction online via Zoom with in-person help at satellite locations. Connection and room 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 22, 10am-12pm
R Programming: R Basics
Register: https://go.wisc.edu/jzc27y
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, September 29, 10am-12pm
R Programming: R Basics (repeat)
Register: https://go.wisc.edu/95i8rt
This workshop is an exact repeat of the September 22nd “R Programming: R Basics” workshop (see above).
Friday, October 6, 10am-12pm
R Programming: Data Wrangling
Register: https://go.wisc.edu/9qme74
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 RStudio (functions, operators, data types) would be helpful for you to get the most out of this session.
Friday, October 13, 10am-12pm
R Programming: Data Visualization
Register: https://go.wisc.edu/2t6347
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, RStudio, and dplyr would be helpful for you to get the most out of this session.
Friday, October 20, 10am-12pm
R Programming: Reports
Register: https://go.wisc.edu/s99p53
One way to automate your reports is to create files with human readable text and 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 document into a final report.
A working knowledge of R and RStudio would be helpful for you to get the most out of this session.
Friday, October 27, 10am-11:30am
R Programming: README Files with R Markdown
Register: https://go.wisc.edu/ysu69x
Documenting your analysis in a way that is understandable to a colleague (or yourself 3 months later) can be challenging. README files are text documents that record your computational environment, methodologies, and more. After this session, you will be able to use R markdown to create a README file template.
A working knowledge of R and RStudio, and some experience with R markdown would be helpful for you to get the most out of this session.
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
Tuesday, September 19, 10am-12pm
Python Programming: Introduction
Register: https://go.wisc.edu/9tj1e8
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.
Tuesday, September 26, 10am-12pm
Python Programming: Introduction (repeat)
Register: https://go.wisc.edu/4n39ap
This workshop is an exact repeat of the September 19th “Python: Introduction” workshop (see above).
Tuesday, October 3, 10am-12pm
Python Programming: Loops, Lists, and Functions
Register: https://go.wisc.edu/65dq29
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)
Tuesday, October 10, 10am-12pm
Python Programming: Spreadsheets and Data Wrangling with Pandas
Register: https://go.wisc.edu/i5hz61
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)
Tuesday, October 17, 10am-12pm
Python Programming: Data Visualization with Seaborn
Register: https://go.wisc.edu/e9eld7
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)
Tuesday, October 24, 10am-12pm
Python Programming: Batch Processing and Working with Image Collections
Register: https://go.wisc.edu/rk0i6s
This workshop will focus on using Python to make bulk changes to files and file directories. It will also introduce Pillow, a free and beginner-friendly Python library for processing image files.
Prerequisite: Understanding of basic Python concepts (i.e. functions, operators, data types) is helpful.
Workshop Instructors
Trisha Adamus
Trisha Adamus is a Health Sciences Librarian at University of Wisconsin-Madison (Ebling Library) specializing data services.
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.
Questions? jcraig@chazen.wisc.edu
Chris Endemann
Chris Endemann is a Data Science Facilitator at the Data Science Hub, University of Wisconsin-Madison.
Sarah Graves
Sarah Graves is the Environmental Observation and Informatics Program Coordinator for the Nelson Institute for Environmental Studies, University of Wisconsin-Madison.
Chris Henson
Chris Henson is an Education Data Reporting Specialist on the School of Medicine and Public Health Information Technology team, University of Wisconsin-Madison.
Erwin Lares
Erwin Lares is a Research Cyberinfrastructure Consultant in the Division of Information Technology (DoIT), University of Wisconsin-Madison.
Casey Schacher
Casey Schacher is a Research Cyberinfrastructure Consultant in the Division of Information Technology (DoIT), University of Wisconsin-Madison.
Dylan Schoemaker
Dylan Schoemaker is a PhD student in 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
Sarah Stevens
Sarah Stevens is the Director of the Data Science Hub, University of Wisconsin-Madison.
Helpers:
Salma Abouelhassan, Integrated Program in Biochemistry, University of Wisconsin-Madison
Imraan Alas, School of Pharmacy, University of Wisconsin-Madison
Grace Cagle, Soil Science, University of Wisconsin-Madison
Nina Desianti, Environmental Toxicologist – Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison
Corey Halpin, Software Engineer – Internet Scout, University of Wisconsin-Madison.
Paxton LaJoie, GIS Specialist in Education and Practice – MIT Libraries
Jackie Lemaire, Genetics, University of Wisconsin-Madison
Nicole Mitideri Rivera, Department of Botany, University of Wisconsin-Madison
Mary Murphy, Research Cyberinfrastructure Consultant – Division of Information Technology (DoIT), University of Wisconsin-Madison
Hannah Olson-Williams, Population Health Institute, University of Wisconsin-Madison
Sara Ronnkvist, Department of Sociology, University of Wisconsin-Madison
Deanna Schneider, Application Developer, Systems Integrator, and Data Analyst, University of Wisconsin-Extension
John Shadle, Research Data Analyst – Center for Health Disparities Research, UW-Madison School of Medicine and Public Health
Angel Tang, Data, Science & Engineering Diversity Resident – UW-Madison Libraries
Ece Turnator, Humanities and Digital Scholarship Librarian – MIT Libraries
A`ha Vuong, Student Status Specialist – UW-Madison School of Education
Rene Welch, Scientist – Biostatistics and Medical Informatics, UW-Madison School of Medicine and Public Health
Qiuyu Yang, Biostatistician – Department of Surgery, UW-Madison School of Medicine and Public Health