allissadillman/nih-data-science-bootcamp-2020

Course Overview

Hello and welcome to The National Institutes of Health (NIH) Data Science for Science Teachers Boot Camp. This is course is a broad and well-rounded training course designed specifically for you to learn data science techniques and to facilitate integration of this highly desired, cutting-edge skill set in your students’ coursework.

This workshop will provide hands-on training for data science tools commonly used by the biomedical research community and informational videos on data science topics.

This bootcamp will enable you to:

• Perform analysis with real data using data science techniques
• Network with and learn from leaders in the data science field
• Discuss strategies and success stories and form a network of educators across the country
• Learn about NIH programs that support educational partnerships and STEM programs

*Note: All times are in EDT to see your time zone use this calculator: https://www.timeanddate.com/worldclock/converter.html

Monday 6th

• 12:00-12:50 EDT- Introduction to bootcamp and goals

• 1:00-1:50 EDT – NIH and data science by Dr. Susan Gregurick

• 2:00-2:30 EDT – Introduction to technologies used in this bootcamp

• 2:30-3:30 EDT - Icebreakers breakout

Videos to watch before Wednesday's Data Science and data sources breakout Q & A

• Biology to Data Science:A Voyage of Discovery by Dr. Miranda Darby

• Shazam for Earthquakes: How geoscientists are leveraging data science to improve earthquake monitoring capabilities by Dr. Karianne Bergen

• Data, data everywhere! Exploring publicly available data sources by Dr. Lisa Federer

Tuesday 7th

• 12:00-12:50 EDT - Open Software for Biology live presentation by Dr. Anton Nekrutenko

• 1:00-4:00 EDT - Galaxy sequencing analysis hands-on (Dr. Allissa Dillman, TA: Dr. Amy Stonelake)

Videos to watch before Thursday's Stats, ML/AI, ethics panel

• Why you should care about statistics by Dr. Jeff Leek

• Applications of Artificial Intelligenece in Medicine by Dr. Sara Ranjbar

• Ethics by Design:Toward the Ethics of Machine Learning by E Jardas

Wednesday 8th

• 12:00-12:50 EDT - Data Science breakout and data sources Q & A

  • With: Dr. Karianne Bergen, Dr. Miranda Darby, Dr. Lisa Federer

• 1:00-4:00 EDT – RStudio Hands-on (Dr. Allissa Dillman, TA: Dr. Wynn Meyer)

  • Slides for this hands-on activity

  • Rscript

  • Dataset 1, Dataset 2

Videos to watch before Friday's SEPA and Citizen Science panel

• Biomedical Open Innovation: Citizen Science and Crowdsourcing by Dr. Jennifer Couch

  • (Watch first as it will give an overview of the SEPA program)
  • Also check out this Citizen Science Resource

• Presentations from the SEPA program project leaders

  • Teaching the Genome Generation by Dr. Charlie Wray

  • Informatics and Data Science Projects at HudsonAlpha by Dr. Neil Lamb and team

  • Data to action: a secondary school-based citizen science project to address arsenic contamination of well water by Dr. Jane Disney

Thursday 9th

• 12:00-12:50 EDT – Stats, ML/AI, Bioethics panel
  • With: Dr. Jeff Leek, Dr. Sara Ranjbar, E Jardas
• 1:00-4:00 EDT – RStudio Hands-on (Dr. Allissa Dillman, TA: Dr. Wynn Meyer)

Optional

• Other work problems in R
• Novel learning methods videos:
  • Science education games example
  • Solving programming puzzles through an animated series
  • Bodystorming: combining dance and science

Friday 10th

• 12:00-12:50 EDT – SEPA and Citizen Science panel

  • With: Dr. Jennifer Couch, Dr. Charlie Wray, Dr. Neil Lamb, Dr. Jane Disney

• 1:00-1:50 EDT - NIH training programs, scholarships and internships

  • High School Scientific Training and Enrichment Program (HISTEP) by Dr. Kristen Zukosky
  • Undergraduate Scholarship Program (UGSP) by Dr. Darryl Murray
  • Summer Internship Program in Biomedical Research (SIP) by Dr. Yewon Cheon

• 2:00-2:50 EDT – Bootcamp: Group Discussion, what did we learn?

• 3:00-4:00 EDT – Meeting outcomes:

  • Future opportunities
  • Collaborations
  • Slack channel management
  • Resource sharing on github

If you would like to continue learning data science skills here is a growing list of Data Science Training Resources: https://github.com/allissadillman/DS-Training-Resources