General data science overview - data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication.

November 1, 2017 Mine Çetinkaya-Rundel

Download Materials

Abstract
In this talk we describe an introductory data science course that is our (working) answer to these questions. The courses focuses on data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, instead of an inference-based, perspective. A heavy emphasis is placed on a consitent syntax (with tools from the tidyverse), reproducibility (with R Markdown) and version control and collaboration (with git/GitHub). We help ease the learning curve by avoiding local installation and supplementing out-of-class learning with interactive learnr modules. By the end of the semester teams of students work on fully reproducible data analysis projects on data they acquired, answering questions they care about.
This talk will discuss in detail course structure, logistics, and pedagogical considerations as well as give examples from the case studies used in the course. We will also share student feedback and assessment of the success of the course in recruiting students to the statistical science major.

About the Author

Mine Çetinkaya-Rundel

Mine is Professional Educator at RStudio and Associate Professor of the Practice at Duke University. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and teaches the popular Statistics with R MOOC on Coursera.

Follow on Twitter Visit Website More Content by Mine Çetinkaya-Rundel
Previous Video
Data science case study an analysis in R, using a variety of packages for web scraping and processing non-tidy data into tidy data frames
Data science case study an analysis in R, using a variety of packages for web scraping and processing non-tidy data into tidy data frames

An analysis in R, using a variety of packages for web scraping and processing non-tidy data into tidy data ...

Next Video
Testing Shiny applications with Shinytest - Shiny developers now have tools for automated testing of complete applications.
Testing Shiny applications with Shinytest - Shiny developers now have tools for automated testing of complete applications.

Testing Shiny applications with Shinytest - Shiny developers now have tools for automated testing of comple...

×

Please register to receive regular updates on our webinars.

!
Thank you!
Error - something went wrong!