Duke University :: Department of Statistical Science Computing Bootcamp 2019

This is a five hour computing bootcamp for incoming Ph.D. and M.S. students to the Department of Statistical Science at Duke University. These materials are adapted from the 2018 bootcamp by Mine Çetinkaya-Rundel and Colin Rundel.

The workshop will cover the following topics:

Introduction to the DSS and Duke computing eco-systems

• Account activation and access to departmental servers
• Discussion of how to responsibly use distributed computing resources
• Docker containers and Duke VM
  • RStudio
  • Jupyter Notebook

Introduction to reproducible research

• Recognize the problems that reproducible research helps address, featuring a brief discussion of case studies gone wrong and how reproducible research could have possibly helped
• Identify pain points in getting your analysis to be reproducible
• The role of documentation, sharing, automation, and organization in making your research more reproducible
• Introduce some tools to solve these problems, specifically R / RStudio / R Markdown

Organizing your project to facilitate reproducible research

• Organize projects and folders to enable reproducibility and reusability
• Understand the structure of data files and the importance of documenting all changes made
• Create a reproducible project workflow using R / RStudio / R Markdown

Version control

• Introduce git and GitHub.
• Initiate a project directory, understand the git workflow, and create a pull request to a remote repository
• Discuss the role of version control in reproducibility
• Discuss version control best practices

R / RStudio and R Markdown

• Navigate R Markdown and RStudio
• Analyze data and create graphics with package tidyverse
• Discuss workflow

Python and Jupyter notebook

• Navigate Jupyter notebooks
• Introduce Python basics, control flow, and functions
• Discuss popular Python packages including: NumPy, SciPy, pandas, matplotlib, seaborn, scikit-learn, and TensorFlow
• Discuss similarities and differences between Python and R
• Discuss how to leverage the best of R and Python

Acknowledgments

Git / GitHub

• Pro Git
• Version Control Best Practices

Other

• Department of Statistical Science Computing Bootcamp 2018 by Mine Çetinkaya-Rundel and Colin Rundel
• Retraction Watch

Python

• A Byte of Python by Swaroop C H
• Jupyter
• Python
• Python Crash Course by Eric Matthes

R

• R for Data Science by Hadley Wickham and Garret Grolemund