How I Began My Data Science Journey With R In The Last Month

Over the past month, I decided to dive seriously into data science, with one clear mission: learn how to analyze real data using R like a professional.

To challenge myself, I worked on a complete e-commerce analytics project. It ended up being demanding, sometimes frustrating, but incredibly rewarding.

Here is what I learned, how I progressed, and why this one-month experience became a turning point in my journey.

At first, R looked unusual and a bit intimidating. But once I started using the right libraries, everything became more natural:

Writing pipelines with %>% even became enjoyable. It felt like guiding the computer step-by-step through a clear thought process.

A major lesson from this project: good organization matters.

I created separate scripts for each step of the analysis:

This approach taught me how data analysts build reproducible workflows, just like in professional environments.

I finally understood why people say: “80% of data science is data cleaning.”

Fixing these issues helped me develop a deeper sense of how real datasets behave — and how to make them usable.

Source: Dev.to