Hunter Glanz | The Five Principles of Data Science Education | RStudio (2020)
In this talk, I will outline a unified philosophy of data science education, and provide tips and tools for implementing these principles in the classroom using R and RStudio. Although data science as a professional discipline is well-established, its pedagogy is still in a period of growth. Even within a single university, multiple data science courses may be offered across different departments leading to inevitable redundancy of efforts amidst rich domain-specific innovations. My experience as an instructor in many such courses has lead me to five principles that transcend domain, context, and choice of language: reproducibility, communication, version control, practical application, and data ethics. For each of these full-stack themes, I will share examples of how to leverage tools in R and RStudio to enhance learning. A 5-minute presentation in our Lightning Talks series
rstudio
Rstudio::conf(2020)
Hunter Glanz
RStudio
Data Science
Machine Learning
Python
Stats
Tidyverse
Data Visualization
Data Viz
Ggplot
Technology
Coding
Connect
Server Pro
Shiny
Rmarkdown
Package Manager
CRAN
Interoperability
Serious Data Science
Dplyr
Forcats
Ggplot2
Tibble
Readr
Stringr
Tidyr
Purrr
Github
Data Wrangling
Tidy Data
Odbc
Rayshader
Plumber
Blogdown
Gt
Lazy Evaluation
Tidymodels
Statistics
Debugging
Programming Education
Rstats
Open Source
Oss
Reticulate
