I spent so much money because I came from a business background, so I knew NOTHING about data science.

If this is also the case for you and you’re feeling like you have no clue what you’re getting yourself into, this article is for you (and my past self)!

I’d also be feeling clueless if I saw AI popping up new data science subfields each day. But don’t worry! I got you.

I’m here to give you the heads-up I wish someone had given me 5 years ago when I was just a beginner.

Today, I’m sharing 5 crucial lessons that I learned from 3 years of data science training at top schools (including NYU), and 3 years of working at Spotify — 5 lessons that any data science beginner should know as early as possible!

I guarantee this article will help you better plan your own data science journey and fast-track your way to your career goals without following the same costly timely path.

You’ll come out with a much better idea of what it means to be a data scientist today.

Lesson #1. Understand the Different Career Pathways of Data Science

When I started my data science journey, the field looked completely different from what it is today.

Data science sits at the heart of AI, so naturally, the field has been impacted by the same magnitude of change.

When you plan your data science education, you must take into consideration the different shifts happening within the data science profession under the influence of AI.

It starts with understanding what career pathways are available to you and finding the one that fits you best. Depending on your choice, you’ll be managing your data science education differently.

Here’s an example — What do you think is the difference between data science researchers and data scientists working in big tech?