Date: November 1st
Time: 11:00 a.m. EDT
Description: How can we effectively and efficiently teach statistical thinking and computation to students with little to no background in either? How can we equip them with the skills and tools for reasoning with various types of data and leave them wanting to learn more? 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.
Logistics: Only 1,000 live attendees are allowed in the Webinar on a first come first serve basis. It is typical for many people who register to not attend (which is why registration does not guarantee access.) If for any reason you cannot make the webinar or cannot get in we will provide links to the recording as well as all materials within 48 hours.
General Data Science Overview Webinar Registration:
Mine Çetinkaya-Rundel , Data Scientist and Professional Educator - Mine is Professional Educator at RStudio and Assistant 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
Webinar Recordings: We try to record every webinar we host and post all materials on our website.
http://www.rstudio.com/resources/webinars/
Slides & Code:
We've started a Github repository with all webinar materials. Speakers for this webinar and all future webinars will add their materials to the repository.
https://github.com/rstudio/webinars