How to Navigate This Guide

This guide uses a Q&A format to help you build skills progressively.

Each topic includes:

  • Question – A clear and practical problem statement
  • Explanation – A walkthrough of the key ideas
  • Python Code – A solution using Python
  • R Code – A solution using R

This side-by-side approach helps reinforce concepts and develop fluency in both languages.

Best Practices for Using Python & R Side by Side

To get the most from this guide, follow these practices:

✅ Run Each Language Independently

Use: - Jupyter Notebook or VSCode for Python
- RStudio or VSCode (with R extension) for R

✅ Modify and Experiment

Change values, functions, and inputs to explore variations and deepen understanding.

✅ Compare Results

Notice how outputs and plots differ between Python and R — especially in data visualization and statistical analysis.

✅ Use the Same Dataset

Use consistent datasets (e.g., data/iris.csv) in both languages for a true side-by-side comparison.

What’s Next?

Now that your environment is ready and you understand how to navigate the guide, you’re ready to dive into the first practical tasks:

  • Setting up a working project directory
  • Loading your dataset
  • Installing essential Python and R libraries

Let’s begin your data journey with the Exploratory Data Analysis (EDA) section.