Data Analysis Workshops Fall 2020

Summary

Check the Calendar listing for data analysis workshops links and registration from:


Multiple Data analysis workshops are offered on campus during this Covid-19 Fall 2020 by remote video teaching. The workshops listed here are either free or have a very small fee for UW-Madison affiliates.

The offerings cover the popular and powerful R and Python programming languages with additional RStudio interface training. from both Steenbock Library and the Social Science Computing Cooperative (SSCC.) The latter also offers workshops on Stata (see SSCC Training Classes.)

Steenbock Library R & Python series

Learn how to analyze data using Python and/or R programming languages. Learn essential programming skills during the R workshop series and the Python workshop series. Attend any or all of the sessions.

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

Note: Registration required. Registration is by workshop, not for the entire series.

R Programming Fridays – 10am – 12pm Registration link
R Basics 9/18 https://go.wisc.edu/x14j74
R Basics (alternate date) 9/25 https://go.wisc.edu/4rx9vk
R Data Wrangling 10/2 https://go.wisc.edu/7udxxw
R Data Visualization 10/9 https://go.wisc.edu/bp290e
R Reports 10/23 https://go.wisc.edu/946o94

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

Python Programming Thrusdays (10am – 12pm) Registration link
Introduction 9/10 https://go.wisc.edu/45nx90
Loops, lists, and functions 9/17 https://go.wisc.edu/s08fbf
Spreadsheets and data manipulation 9/24 https://go.wisc.edu/au3564
Data Visualization 10/1 https://go.wisc.edu/ds56ir

Registration required. Registration is by workshop, not for the entire series.

See Steenbock announcement link for workshops content details and instructors list.

Social Science Computing Cooperative (SSCC)

SSCC courses are (again) free but regular attendance is requested and expected for longer courses. The most relevant series at SSCC are:

Title Date Time Instructor
Introduction to R with RStudio 9/14, 9/15 8:30 – 9:30 Hemken
Data Wrangling in R  9/16, 9/17, 9/18,
9/21, 9/22, 9/23,
9/24, 9/25, 9/28,
9/29, 9/30, 10/1,
10/2
8:30 – 9:30 Hemken

For a complete listing see SSCC Training Classes.

Software Carpentry

Software Carpentry aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.

See details at https://uw-madison-datascience.github.io/2020-09-16-uwmadison-mini/

Schedule

Before Pre-workshop survey
Sept. 16 Automating Tasks with the Unix Shell
Sept. 30 Data Management with SQL
Oct. 14 Version Control with Git/GitHub (PREREQ: For this workshop, you need to be familiar with all of the commands taught in this unix shell lesson.)
Nov. 11 Jekyll Pages (same workshop as on November 25)
Nov. 25 Jekyll Pages (same workshop as on November 11)
Dec. 9 Introduction to Docker (PREREQ: For this workshop, you need to be familiar with all of the commands taught in this unix shell lesson.)

 


Data Science Hub

Are you…

  • unsure which campus data and computing resources you need for your research?
  • interested in making connections and starting new collaborations with data scientists and other researchers on campus?
  • looking for training in data and computing skills?

The Data Science Hub can help! Send an email to the Data Science facilitators (facilitator@datascience.wisc.edu), or join us on Slack for Coding Meetup (Tuesday, 2:30-4:30 p.m.) and office hours (Thursday, 2:30-4:30 p.m.). Office hours will resume on September 3.

Data Science Hub newsletter: sign up at this link


BCRF Tutorials

In case you missed it I also created a complete R and RStudio tutorioal for tabular data exploring both
“classic R” as well as the more modern “Tidyverse” methods as announced in this post:

Tabular data analysis with R and Tidyverse
Does data analysis feel like the impossible job of “catching the moon?

See more tutorials in the “Tutorials” menu above.