Resources

A useful collection of resources I have compiled

📚 Books

These are open-source books, all made available for free by the authors.

• Advanced R
• Beyond Multiple Linear Regression
• Data Science at the Command Line
• Efficient R Programming
• Engineering Production-Grade Shiny Apps
• Forecasting: Principles and Practice
• Fundamentals of Data Visualization
• Geocomputation with R
• Happy Git and GitHub for the useR
• Introduction to Probability
• Introduction to Probability for Data Science
• Introduction to R Markdown
• Introduction to Statistical Learning
• Mastering Shiny
• Modern Data Science with R
• Outstanding User Interfaces with Shiny
• R for Data Science
• R Graphics Cookbook
• R Packages
• Statistical Inference via Data Science
• Text Mining with R
• Tidy Modeling with R

📈 Datasets

• Kaggle: Huge collection of datasets for machine learning, data science, and competitions.
• UCI Machine Learning Repository: Classic archive of datasets widely used in research and teaching.
• Google Dataset Search: Search engine for discovering datasets across the web.
• Data.gov: US government’s open data portal (economics, health, education, climate, and more).
• Awesome Public Datasets (GitHub): Massive curated list of freely available public datasets.
• FiveThirtyEight Data: Datasets used in journalism stories (sports, politics, culture, economics).
• OpenML: Platform for sharing datasets and experiments in machine learning.
• World Bank Open Data: Economic and global development datasets.
• UNICEF Data: Global humanitarian and children’s development datasets.
• AWS Open Data: Open datasets hosted in the cloud (satellite imagery, genomics, COVID-19, etc.).

💻 Integrated Devlopment Environments (IDEs)

Tools used for coding, data science, and software development.

• Xcode: Apple’s official IDE for macOS and iOS development.
• JetBrains IDEs: Professional IDEs like PyCharm (Python), IntelliJ IDEA (Java/Kotlin), and more.
• Visual Studio Code: Lightweight, extensible code editor supporting many languages (Python, C++, web development, and more).
• RStudio: Integrated development environment for R (and Python) with strong support for data science.
• Positron IDE: Modern, lightweight IDE from Posit, designed especially for data science (likely aimed to replace RStudio).
• Visual Studio: Full-featured Microsoft IDE for .NET, C++, Python, web, and more.
• Eclipse: Open-source IDE, popular for Java and general-purpose development.

️🧑‍💻 Learn-to-Code Platforms

• Codecademy: Structured interactive courses in many languages (Python, JavaScript, SQL, etc.).
• DataCamp: Learn data science, Python, R, SQL, and machine learning interactively.
• Hacking with Swift: Excellent platform for learning Swift and SwiftUI for iOS/macOS/etc. development.
• LeetCode: Coding challenges to improve algorithm and problem-solving skills (especially for interviews).