README.Rmd

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title: Teaching Data Science
output: github_document
---

### Latin R 2019

`r emo::ji("information_source")` Centro de Extensión UC  
`r emo::ji("spiral_calendar")`    Wednesday, 2019-09-25  
`r emo::ji("clock8")`             09:00 - 12:00  
`r emo::ji("white_check_mark")`   [Register](https://latin-r.com/)

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## Overview

Success in data science and statistics is dependent on the development of both analytical and computational skills. As statistics educators we are more familiar and comfortable with teaching the former, but the latter is becoming increasingly important. The goal of this workshop is to equip educators with concrete information on content and infrastructure for painlessly introducing modern computation into a data science and/or statistics curriculum. In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into choosing infrastructure and developing curriculum. Workshop attendees will work through several exercises from existing courses and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, and collaboration, version control, and automated feedback with Git/GitHub. We will also discuss best practices for configuring and deploying classroom infrastructures to support these tools. This workshop is aimed at participants who are interested in the role of computing in either a Statistics or Data Science curriculum, including faculty designing new courses/programs and those interested in adding or improving a computational component to an existing course. A basic knowledge of R is assumed and familiarity with Git is preferred.

## Learning objectives

1. Understand the differences between various computing infrastructures for teaching data science and set up a classroom infrastructure using RStudio Cloud. 
2. Adopt or design an introductory data science curriculum using R as the programming language and tidyverse packages as the grammar of choice. 
3. Implement reproducible data analysis assignments and assessments that leverage R Markdown and Git as well as course management via GitHub. 
4. Gain skills and confidence for teaching data science and computational statistics with a modern toolkit.

## Schedule

| Time          | Activity                                |
|:--------------|:----------------------------------------|
| 09:00 - 09:05 | [Welcome](/00-welcome/00-welcome.pdf)   |
| 09:05 - 09:40 | [Curriculum design](/01-curriculum-design/01-curriculum-design.pdf)  |
| 09:40 - 10:30 | [Computing infrastructure](/02-computing-infrastructure/02-computing-infrastructure.pdf)  |
| 10:30 - 11:00 | `r emo::ji("coffee")` *Coffee break*    |
| 11:00 - 12:30 | [Reproducible workflows](/03-reproducible-workflows/03-reproducible-workflows.pdf)                  |

## Instructor

[Mine Çetinkaya-Rundel](http://mine-cr.com) is Senior Lecturer in the School of Mathematics at University of Edinburgh and Data Scientist and Professional Educator at RStudio. She is on leave from her Associate Professor of the Practice position at the 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, an annual two-day competition in which teams of undergraduate students work to reveal insights into a rich and complex data set. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. As part of this project she co-authored three open-source introductory statistics textbooks. She is also the creator and maintainer of datasciencbox.org and she teaches the popular Statistics with R MOOC on Coursera. 

Mine is the Chair of the ASA's Section on Statistics and Data Science Education. In 2018 Mine received the David Pickard Teaching Award and in 2016 the ASA Waller Education Award. She is also the recipient of the 2015 JSM Best Paper Award in the Section on Teaching Statistics in the Health Sciences and the 2014 Duke University David and Janet Vaughan Brooks Award for Teaching Excellence.

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