Welcome to the first post!
This is the start of a blog series that I hope will demonstrate how Python (the programming language) can unleash your data analysis skills, even at a beginner level.
In my line of work, I am constantly trying to relay the insights and stories revealed by various data analyses. For a major portion of my budding professional career, I was an Excel power user. Sure, I knew it had its limitations (cue in flashback: so many rows, oh so many crashes!), but Excel was my weapon of choice through and through.
Then I discovered true power:
Python and the Pandas Library.
Python is a programming language that increases your prowess as a data analyst. Pandas is an open-sourced library that can only be described as Excel on hardcore drugs.
If you are constantly wrangling, slicing and dicing data and needing to do so quickly, I highly recommend you browse my posts to see the capabilities of Python (and its libraries!) for data science. If you are a data analyst who wears many hats or would like to take your data analyses to the next level -- such as professional-quality data visualizations and robust data applications, then this blog series is for you.
As I began my journey to learn Python for data science, I completed a body of work. I recently revisited this work and immediately remembered a lot of my mistakes. And then materialized a running list of 'things I wish I had known'. So, follow along as I communicate with my novice self and blog about key topics and tips that I believe will help boost the path from novice to expert, one post at a time. Ready to join me on this voyage? Awesome! But first, let's get you set up. Shouldn't take more than an hour.
Go to my 'Quick Set-up' blog post.