Resources

This page collects useful resources to support your learning in Data and Code Management: From Collection to Application.
All resources are freely available online.

Getting started with R and RStudio

• The CRAN website — official source for R and its documentation.
• An Introduction to R — official tutorial covering basics of R programming and statistical analysis.
• RStudio IDE — the most widely used IDE for R.
• RStudio cheat sheets — concise guides for R packages and workflows.
• The tidyverse — collection of R packages for data manipulation, visualization, and analysis.

Getting started with Python

• Python official documentation — language reference and tutorials.
• Python Data Science Handbook by Jake VanderPlas — free online guide to NumPy, pandas, matplotlib, scikit-learn.
• Real Python — tutorials and best practices for Python programming.
• Pandas documentation — data wrangling in Python.
• Matplotlib gallery — quick examples for Python plotting.

SQL and Databases

• Mode SQL Tutorial — interactive beginner-to-advanced SQL lessons.
• SQLBolt — hands-on SQL exercises.
• PostgreSQL documentation — reference for one of the most popular open-source databases.
• SQLite documentation — lightweight relational database, easy to set up.

Reproducibility and Collaboration

• Happy Git and GitHub for the useR by Jenny Bryan — beginner-friendly guide to version control.
• Pro Git Book — complete reference on Git.
• RMarkdown and Jupyter Notebooks — tools for literate programming.
• Quarto — next-generation publishing system for R, Python, and Julia.

Business Intelligence & Visualization

• Power BI Documentation — tutorials and user guides from Microsoft.
• Mastering Shiny by Hadley Wickham — developing interactive web apps in R.
• Streamlit documentation — build web apps in Python for data science.
• ggplot2 book by Hadley Wickham — R visualization.
• Seaborn documentation — Python data visualization.

Large Language Models (LLMs) and AI Tools

• Prompt Engineering Guide — strategies for effective LLM use.
• LangChain documentation — framework for building applications powered by LLMs in Python.
• OpenAI API documentation — reference for integrating LLMs into data workflows.

Recommended Books

• Advanced R by Hadley Wickham.
• R for Data Science by Grolemund & Wickham.
• Fluent Python (2nd Edition) by Luciano Ramalho.
• Effective Python by Brett Slatkin.
• SQL for Data Scientists by Renee M. P. Teate.

Miscellaneous

• Shiny — open-source R package for interactive apps.
• Streamlit — Python alternative for quick dashboards.
• Rcpp for Seamless R and C++ Integration — extend R with C++.
• Engineering Production-Grade Shiny Apps — best practices for robust Shiny development.