Workshops and Learning Paths

Body
Banner - learners at a workshop

TDAI Data Literacy

As part of its Data Literacy program, TDAI periodically holds workshops aimed to introduce data and data analytics skills to the community.  These past workshops have included an R Crash Course and a series of introductory R workshops which include learning and using R and R Studio, statistical analysis in R, and visualizing data in R.

Furthering our Data Literacy mission, TDAI is pleased to be in partnership with the OSU Libraries to bring the Carpentries to the University.

Advanced

Accordion Header
TDAI Workshop List - 2021

Text

The Translational Data Analytics Institute, in partnership with the University Libraries Research Commons, regularly presents a multi-part workshop for learning R.  R is a free and open-source programming language used by many for statistical computing.  Join our instructors Lee-Arng Chang, Ariel Garsow, and Rachael Giglio and our workshop helper Drew Barkley to learn how to get started in R, use R in statistical analysis, and create visualizations in R.

Participants should have a computer (PC or Mac) with R / R Studio installed or be able to sign up for a free account at either the Ohio Supercomputer Center (OSC) or rstudio.cloud. 

For Summer 2021 this workshop will be held in person!

Getting Started in R

7/26/2021; 1 - 5:00 pm

Learn how to get started in R and Rstudio.  This workshop covers basic R functionality:  objects, functions, vectors, and various data operations. Sign up here.

Statistical Analysis in R and Data Visualization in R

7/27/2021; 1 - 5:00 pm

This workshop builds upon the Getting Started in R workshop and gives participants a foundation for performing statistical analysis using R. The second part of this workshop covers understanding of what makes good visualizations and how to create them in R. Sign up here.

Text

This workshop was held virtually on Fridays from 9:30 - 11:30 starting 3/19/2021 through 4/9/2021.

Thanks to all of our participants and instructors for another great workshop!

This workshop workshop was led by instructors Lee-Arng Chang (Data Visualiztion in R), Rachael Giglio (Getting Started in R), and Emma Wenckowski (Statistical Analysis in R).

Text

Interested in learning R, but can't make it to one of our workshops?  Check out the R Learning Resources guide.  There are links to tutorials, cheat sheets, and videos to get you started and keep learning R.

Text

Workshop Calendar

See the calendar link below for TDAI workshop events, as well as Data related events hosted by TDAI partners at the OSU Libraries, the Ohio Supercomputer Center (OSC), and the Big Data and Analytics Association (BDAA).

Can’t find an event or workshop that meets your needs?  Look at the independent learning paths below for online tutorials and courses.

Text

Text

Suggested Independent Learning Paths

Below are some suggested learning pathways designed to introduce you to data analytics or how to effectively use data.  Pick and choose from several pathways to customize your own learning experience to suit your needs and interests using a combination of both on-campus workshops and resources available on-line from LinkedIn Learning or Lynda.com.

For more information on obtaining accounts on either LinkedIn Learning through Ohio State, please see the criteria for access at the IT Academy’s eligibility page.  Public libraries (such as the Columbus Public Library) also provide access to these resources.

Accordion Header
Learning Paths

Text

This learning path may be useful for researchers and students that are starting to use the Ohio Supercomputer Center (OSC) resources for their research.  These events may be found in the calendar to the right, or on the OSC events page.

  1. Intro to Supercomputing at OSC
  2. Getting started at OSC
  3. Big Data at OSC Workshop
Text

This learning track uses material from lynda.com as an introduction to Data Analytics.  It may be supplemented with material available in workshops on campus as noted.

  1. Learning Data Analytics
  2. Data Science Foundations: Fundamentals
  3. Learning Data Science: Tell Stories with Data
  4. Data Science Foundations: Data Mining
Text

This learning path goes in depth on using Data to create an effective narrative.  It uses material from lynda.com as a baseline, and is supplemented with resources and workshops available from Research Commons.

  1. Data Visualization: Storytelling (1h 37m)
  2. Learning Data Visualization (3h 50m)
  3. Data Visualization Tips & Tricks (2h 14m)
  4. Research Commons Data Visualization tools series (pick one)

General Visualization

  1. Intro to Tableau for Data Visualization
  2. Intro to Excel for Data Visualization
  3. Open and Affordable tools for analyzing and visualizing data

GIS (mapping)

  1. Web mapping basics with ArcGIS online
Text
Text

This learning track may be of interest to those wanting to use Amazon Web Services for their Data Analytics projects.  If the Big Data Technology Fundamentals is omitted it also makes a good introduction to AWS and cloud computing.

  1. AWS Cloud Practitioner Examples
  2. Introduction to Amazon EC2
  3. Amazon Simple Storage Service (S3) primer
  4. Big Data Technology Fundamentals
Text

Microsoft Developer has a series of videos on YouTube called “Python for Beginners”.  Each video is just a few minutes long and the series aims to help the viewer learn Python starting with very little programming background.