Thread by @mdancho84

People are shocked when they learn that I picked #R over #Python.

It’s a true story. 🧵

#datascience #rstats

I began learning data science with Python.

This decision actually cost me 3 months and caused me to quit data science for a little bit.

Why?

Python was too much like coding. And I came from a business background.

My main tool was Excel.

Python was unnatural.

A friend of mine recommended me to try R.

I was instantly surprised at how much more intuitive it was for me given my Excel background:

👉R has functions just like Excel.

I could quickly summarize my data using mean(), sd(), sum(), and friends.

These functions were very similar to AVERAGE(), STDEV(), and SUM() from Excel.

👉R has analytics built-in.

I could do correlation and make trendlines with linear regression very easily.

👉R had the tidyverse.

The tidyverse blew my mind. 🤯

This toolkit includes data wrangling and visualization libraries that effortlessly worked together.

👉 R had the pipe!

If you’ve never tried it, the pipe %>% is this amazing operation that allows you to flow your data transformations from one operation to another.

👉R has reporting!

I was able to make a simple PDF report in minutes versus struggling with Jupyter.

So I ended up picking R and to this day I’m so happy I did.

If you’re like me, coming from a business background, using:

1. Excel,
2. Tableau, or
3. PowerBI daily

Then R will feel like those tools on STEROIDS! (seriously)

Want more information on how to become a data scientist with R?

I’ve consolidated my learnings into 10 secrets that I wished I knew going into data science.

These are the 10 most valuable tools I’ve learned over my journey.

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