Table of Contents:

This page covers frequently asked questions (FAQs) about the R and Python workshops provided by Information Technology Services - Research Services (ITS-RS) at the University of Iowa. Table of Contents:

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What tools do I need to participate in the workshops?

Depending on the workshop, JupyterLab or RStudio will be available in the Interactive Data Analytics Service (IDAS). Workshop materials will be available in IDAS. After registering for the workshop, participants will be given access to the workshop instance in IDAS, which lasts for the duration of the workshop.

A link to log in to IDAS will be shared at the beginning of the workshop. Participants do not have to request an IDAS account. Participants will need to log in with their HawkID and authenticate with Two-Step Login (Duo).

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The good news is that Python and R are open-source programming languages, which means there are a lot of free resources to study on your own if you have time! Here are a few free resources:

1. Python resources:

• Learn Python - Full Course for Beginners: https://www.youtube.com/watch?v=rfscVS0vtbw  

• The official Python tutorial: https://docs.python.org/3/tutorial/

• Python tutorials: https://www.learnpython.org/en/Welcome

• Pandas tutorials: https://pandas.pydata.org/docs/getting_started/index.html#getting-started

• Python Data Science Handbook: https://jakevdp.github.io/PythonDataScienceHandbook/

• Scikit-learn user guide: https://scikit-learn.org/stable/user_guide.html

• "Probabilistic Machine Learning: An Introduction" and "Probabilistic Machine Learning: Advanced Topics" by Kevin Murphy: free pdf drafts of books: https://probml.github.io/pml-book/

2. R resources:

• Book: “Cookbook for R”: good to start with: http://www.cookbook-r.com/

• Learn R at the Console (interactively): the swirl package: once you have installed R and can install packages, the "swirl" package is an interactive way to learn R: https://swirlstats.com/

• Book: “R for Data Science”: learn to use R for data science: https://r4ds.had.co.nz/

• Article: “The Layered Grammar of Graphics: good to learn more about graphing in R: http://vita.had.co.nz/papers/layered-grammar.pdf

• RStudio Education resources for beginner, intermediate and expert levels: https://education.rstudio.com/learn/

• plotly examples: https://plotly.com/r/

3. Jupyter resources

• Try Jupyter for free in your browser without installing anything. Click on “Jupyter Notebook” under the “Applications” section to try Jupyter Notebook and see tutorials for Jupyter Notebook: https://jupyter.org/try

4. Other data analytics resources, free for UI members:

• LinkedIn Learning: on-demand online training library: https://its.uiowa.edu/linkedin-learning

• The Iowa Social Science Research Center’s workshops: https://ppc.uiowa.edu/research-support/workshops

Will the workshops be recorded?

The workshops will not be recorded. Future workshops will be posted on the UI calendar as they are scheduled, typically at the beginning of each Fall and Spring semester. You are welcome to subscribe to our email list to receive an email when we announce our workshop schedule. Please see above for a list of free resources to learn Python and R.

I can’t attend all sessions of the workshop. Can I attend only one of the sessions?

Each workshop is split into multiple days to reduce Zoom fatigue and encourage reviewing of the materials between sessions. Since later sessions build on previous one(s), participants might have difficulty following along if previous materials are missed. Participants are encouraged to attend all sessions to learn the complete contents of the workshop. However, it is up to the participants to decide how many and which days of the workshop they want to attend. Each day’s agenda is posted in the workshop description to help participants understand what topics will be covered; please see the workshop description on the UI calendar for more information.

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Thank you for your interest in our workshops. ITS – Research Services offers workshops in R, Python, and High Performance Computing (Argon), and Linux. We typically offer the following workshops every year:

• R workshops: (recommended to be taken in this order)

  • Introduction to Data Analysis Using R

  • Interactive Data Visualization Using Plotly in R

• Python workshops: (recommended to be taken in this order)

  • Python for Data Analysis: Python Fundamentals

  • Python for Data Analysis: Introduction to Pandas (being evaluated for combining with the Python Fundamentals workshop)

  • Python for Data Analysis: Machine Learning with Python

  Introduction to Linux Using Argon (Schedule TBD)

• Introduction to High Performance Computing Using Argon (

  Schedule TBD

  taught by Dr. Doosoo Yoon)

Workshops will be posted on the UI calendar as they are scheduled, typically at the beginning of each Fall and Spring semester. You are welcome to subscribe to our email list to receive an email when we announce our workshop schedule. We will continue to improve our workshop offerings to meet the needs of the research community as we are able.

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