How to Transition into Data Science: A Realistic Q&A

Looking to break into data science, analytics, or related roles? Here’s a direct, no-nonsense guide covering frequently asked questions for aspiring data professionals. Be prepared to put in real effort and focus on what matters.

The “semi-harsh” tone here is refreshing. Would love to see what full-harsh might look like.

Keats said:
The “semi-harsh” tone here is refreshing. Would love to see what full-harsh might look like.

“You’re going to die alone and broke because you ignored SQL and probability.” There’s your full-harsh.

@Koa
And because you didn’t get the sweet data science money.

Here’s how I went from geoscience to SWE at Google:

  1. Took a semi-technical role out of college and showed I was interested in programming.
  2. Moved to a more technical role at my next job by highlighting my self-taught skills.
  3. Gained enough experience for mid-size dev roles and eventually FAANG.

For data science: leverage your current job to learn skills like SQL, Python, and reporting, then transition to proper DS roles over time. SQL skills are incredibly useful in almost any data role.

@Nile
What geoscience jobs involve programming?

LauraCoder said:
@Nile
What geoscience jobs involve programming?

Geoscience often involves handling and analyzing spatial datasets, optimizing geomechanical models, or automating tasks in GIS systems using Python.

Should this post be required reading for anyone considering data science? Yes. I’d also add that being able to communicate effectively is key. No one wants to work with someone who can’t explain their analysis in plain language.

@Keenan
Totally agree. The ability to convey insights clearly is as important as technical skills.

Titanic, Iris, and Housing datasets are the holy trinity of entry-level projects. Useful to learn but not to showcase.

Rey said:
Titanic, Iris, and Housing datasets are the holy trinity of entry-level projects. Useful to learn but not to showcase.

They’re good for beginners to learn the basics. Just don’t make them your portfolio centerpiece.

You don’t need to be a math wizard to succeed in data science. Focus on delivering value. Sure, you need a baseline understanding of probability and linear algebra, but obsessing over advanced math won’t help you ship impactful products.

@Van
Absolutely. The math foundation is critical for understanding limitations and biases, but the primary focus should always be creating value.

This guide is blunt, but I love it. Most posts on this topic avoid the reality of the grind needed to break into data science.