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How I Optimized My Data Analysis Practices — Hacks & Libraries You Should Start Using

A compilation of useful hacks & libraries to speed up your analysis

Harshit Tyagi
Towards Data Science
9 min readJun 20, 2020

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A shot of my monitor while I was working on this article

So, I have been working on a lot of Machine Learning and Data Science projects lately. And one thing that I can share from my experience is that every time you pick up a project, you have to cross this inevitable bog of data cleaning and wrangling before you reach the sexiest part(for me at least) of the project which is Model Engineering.

Now to make my life easier, I started looking for hacks, libraries, configurations, etc. to be more efficient at performing exploratory analysis.

In this blog post, I’m going to share a few of my findings/configurations that might be useful and interesting to you.

So, I intend to cover this in 2 sections :

  • Jupyter hacks and
  • Useful Libraries for data analysis

Jupyter Hacks

Since spend long hours with your jupyter notebooks, let’s make your workspace look a bit cooler so that you stay focused and enjoy working with those notebooks. Later, we’ll see a few tricks that would help you speed up your analysis. Trust me you’re going to start enjoying spending time with your…

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Harshit Tyagi
Harshit Tyagi

Written by Harshit Tyagi

Director Data Science & ML at Scaler | LinkedIn's Bestselling Instructor | YouTuber

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