Summary
Check the Calendar listing for data analysis workshops links and registration forms. Below are workshop dates for SSCC, Madison chapter of Software Carpentry, and Steenbock Library series.
Social Science Computing Cooperative (SSCC)
The table below shows only the R and Python courses. See complete listing at https://sscc.wisc.edu/sscc_jsp/training/
Title | Date | Time | Instructor |
---|---|---|---|
Introduction to Python for Data Analysis | 8/29 | 10:00 – 3:00 | Dimond |
Introduction to R | 8/29 | 12:30 – 3:30 | Struck |
Data Wrangling in Python | 8/30, 8/31, 9/1, 9/2 | 10:00 – 3:00 | Dimond |
Data Wrangling in R | 8/30, 8/31, 9/1, 9/2 | 12:30 – 4:30 | Struck |
JupyterLab and Jupyter Notebooks: Not Just For Python Any More | 9/6 | 2:00 – 3:00 | Dimond |
Introduction to Python for Data Analysis | 9/12, 9/14 | 10:00 – 12:00 | Dimond |
Introduction to R | 9/13 | 1:00 – 4:00 | Struck |
Data Wrangling in R | 9/15, 9/20, 9/22, 9/27, 9/29 | 1:00 – 4:00 | Struck |
Data Wrangling in Python | 9/19, 9/21, 9/26, 9/28, 10/3, 10/5 | 10:00 – 12:00 | Dimond |
Regression Review with R | 9/27 | 9:00 – 12:00 | Hemken |
Data Visualization in R: ggplot2 Basics | 9/29 | 9:00 – 11:00 | Struck |
Regression Diagnostics with R | 10/4 | 1:00 – 4:00 | Struck |
SSCC courses are (again) free but regular attendance is requested and expected for longer courses.
Software Carpentry
The Data Science Hub is providing a Fall online “mini series” workshops. There is a small $5 fee for UW-Madison affiliate (and snacks during the sessions!) The table below details the Fall workshop series.
The main registration page is: Fall 2022 Mini Workshops
Notes: registration for “Unix Shell” ends August 31. Registration for later workshops will open gradually.
Schedule
Date | Workshop | PreRequisites |
---|---|---|
September 7th | Unix Shell | NA |
September 28th | Git/GitHub | For this workshop, you need to be familiar with all of the commands taught in this Unix Shell lesson |
October 26-27th | Intro to Machine Learning with Sklearn | For this workshop, you need to have a basic familiarity with python |
November 16-17th | Intro to Docker | For this workshop, you need to have a base familiarity with using a command shell and the text editor |
November 30th | Interactive Data Visualizations in Python | For this workshop, you need to have a base familiarity with python, the unix shell, and github |
December 14th | Intro to Julia | For this workshop, you need to have a base familiarity with files, directories, arrays, and indices |
The University of Wisconsin-Madison is a Platinum Member Organization of the THE CARPENTRIES. For more details on what are the “Carpentries” see my Blog entry Software-carpentry
Steenbock Library Workshops
See details on announcement page: Learn how to analyze data using Python and/or R programming languages (via Zoom) for details on each workshop. Below is a summary for dates and registration links for R and Python series. Open to all UW-Madison students, faculty, and staff.
Materials:
R series dates and registration
Date: Fridays 10am-12pm | R Programming: | Registration Link |
---|---|---|
September 23, 11am-1pm | R Basics in Spanish | https://go.wisc.edu/kvzxeq |
September 30 | R Basics | https://go.wisc.edu/e9123d |
October 7 | R Basics (repeat) | https://go.wisc.edu/135b9d |
October 14 | Data Wrangling | https://go.wisc.edu/930o7p |
October 21 | Visualization | https://go.wisc.edu/oa9l8d |
October 28 | Reports | https://go.wisc.edu/6×5766 |
(Registration is required for each workshop.)
Python series dates and registration
Date: Thursdays 10am-12pm | Python Programming: | Registration Link |
---|---|---|
September 29 | Introduction | https://go.wisc.edu/mau089 |
October 6 | Introduction (repeat) | https://go.wisc.edu/1fb8vr |
October 13 | Loops, lists, and functions | https://go.wisc.edu/l1gei7 |
October 20 | Spreadsheets and data wrangling with pandas |
https://go.wisc.edu/u8x21y |
October 27 | Data Visualization with seaborn | https://go.wisc.edu/2a411r |
(Registration is required for each workshop.)
See description for each workshop content on Learn how to analyze data using Python and/or R programming languages