8 Fatal (Yet Non-obvious) Pitfalls in Data Science

...and how to identify and handle them.

I have had the opportunity to work on several real-world data science (DS) and machine learning (ML) projects.

This includes projects from my full-time job as well as part-time gigs, wherein, companies outsourced their projects to me.

Building end-to-end projects has taught me MANY invaluable lessons and cautionary measures, which I hardly found anyone talking about explicitly.

So, in the most recent article, I described eight such instances you will likely experience in your projects: 8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science.

It also includes some cautionary measures you can take to avoid/navigate them.

Frankly speaking, I wish someone had shared these lessons with me as I was progressing in my career.

But it would be best if you didn’t feel that way.

In my experience, these pitfalls are almost always present, but they are not obvious to observe, which ruins many projects.

I am confident you will leave with some core takeaways after reading the above article.

Read it here: 8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science.

I will plan a part II if this article resonates with most readers.

Have a good day!

Avi