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Training and Learning Paths

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TDAI Data Literacy

As part of its Data Literacy program, TDAI periodically holds training sessions aimed to introduce data and data analytics skills to the community. Past events have included an R Crash Course and a series of introductory R training sessions 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.

TDAI Training List - 2021

For Autumn 2021 this training event was held in person November 15th and 16th from 8:30 - 12:30 pm in Pomerene Hall.

Huge thanks to our instructors Drew Barkley (Getting Started in R), Lee-Arng Chang (Data Visualization in R), and Ariel Garsow (Getting Started and Statistics in R). Thanks also for helpers Cameron Erdman and Nelson Le.

For Summer 2021 this training event was held in person for the first time on July 26th and 27th from 1 - 5pm in Pomerene Hall!

Our instructors Lee-Arng Chang, Ariel Garsow, and Rachael Giglio and our workshop helper Drew Barkley led participants in sessions on how to get started in R, use R in statistical analysis, and create visualizations in R.

Thanks to all of our participants and instructors for another great event! This training was held virtually on Fridays from 9:30 - 11:30 starting 3/19/2021 through 4/9/2021.

The Spring R training session was held virtually and was led by instructors Lee-Arng Chang (Data Visualization in R), Rachael Giglio (Getting Started in R), and Emma Wenckowski (Statistical Analysis in R).

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

Data Training Calendar

See the calendar link below for TDAI training 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 that meets your needs? Look at the independent learning paths below for online tutorials and courses.

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.

Learning Paths

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

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

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

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

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.