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

The courses focuses on data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, perspective.

Webinars

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

November 1, 2017

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.

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About the speaker

Mine Çetinkaya-Rundel
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Professional Educator and Data Scientist

Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera.