…why you're here, this session will:

• Introduce you to data science
• Introduce you to R
• Discuss some state-of-the-art tools for data exploration and visualization, with the context of classroom use

Important missing piece: Where does the data come from? (e.g. Experimental Design)

• Encourage and enforce working collaboritavely (think, code, write, present)
• Underscore effective communication of findings
• Ask questions students want to answer
• Equip students with the tools to answer questions of their own choosing

This is the difference between statistics and data science, by Justin Megahan

"And there is certainly no lack of demand for data scientists. A few months ago, Glassdoor named it the top job of 2016 – with more than 1,700 job openings and an average salary of $116k."
- Justin Megahan

"There have been calls to do more in the statistics community, to expand its boundaries, to look more to data collection, management, and presentation, to focus more on predicting future outcomes and less on merely inferring relationships."
- Justin Megahan

Data science "is"

• Applied statistics (statistical modeling)
• Data skills (exploratory data analysis and visualization)
• Computational skills (scripting; reproducibility)

For educational use?

• Students measured sleep over one month, recorded factors of interest (test next day, spring break, sleep location, etc.), and analyzed against sleep quality.

Our path to better science in less time using open data science tools (Lowndes et al., 2017)

"Coding is 'as important to modern scientific research as telescopes and test tubes'\(^1\), but it is critical to 'dispel the misconception that these skills are intuitive, obvious, or in any way inherent'\(^2\)".

Reasons researchers do not publish reproducibly (Borgman, 2007):

• a lack of incentives in terms of citations or promotion
• the effort required to clean data and codes
• the creation of a competitive advantage over other fellows
• intellectual property issues

DataCamp is free for every participant during the Summer Workshop!!

Click to join group

"Our mission is to provide researchers high-quality, domain-specific training covering the full lifecycle of data-driven research."
- Data Carpentry

• FOCUSED ON EDUCATION
• Shiny web app
• Focus on data life cycle
• GUI based, like JMP…
• …but creates code from GUI actions for reproducible report
• Default to tidyverse, but allow base R
• Easy drag-and-drop creation of dashboards
• Use pipes user-interface for data life cycle
• Use modern visualization tools (htmlwidgets)
• Database + Google Sheet connections

"'Do experienced programmers use Google frequently?'
The resounding answer is YES, experienced (and good) programmers use Google… a lot. In fact, one might argue they use it more than the beginners."
- Umer Mansoor

RStudio cheatsheets

RExRepos

Quick-R

R for Data Science

My whole lab has decided to learn R this summer. We have @hadleywickham books! Any online courses or tutorials you #rstats people recommend?

— Tessa Hill (@Tessa_M_Hill) July 21, 2017

Some QUBES communities and resources:

• QUBES Community Group: Using R in the classroom
• Collections in the Using R group
• RStudio Server
• Introduction to Biostatistics (BIOL 327) at William and Mary
• The Analysis of Biological Data by Whitlock & Schluter - QUBES Group
• Serenity: Follow the development!

Remember: DataCamp is free for you this week!