This story touches on a mix of topics like the divide between data science and data engineering, model complexity in machine learning, the transition from academia to data science, and hybrid AI-human solutions.
In a recent interview for the Data for Future podcast, we chatted about a range of topics related to all things data science. So I decided to write this post to cover the main takeaway messages of our conversation.
Should your company hire data scientists or data engineers?
Data scientists can be even more valuable if they have data engineers by their side.
Companies often expect data scientists to do magic with their data. But the company may not have good data architectures and robust ETL pipelines in place, resulting in overall poor data quality, or little practical ability to iterate machine learning models. In such circumstances, data scientist…
In academia, you work to expand the limits of human wisdom. In industry, you work just to make a company richer. Or don’t you?
In your life as an academic, you are Jodie Foster looking for radio signals from other worlds. You are Prof. Indiana Jones teaching archeology in between adventures. You are Alan Grant, Ellie Sattler and Ian Malcolm talking about chaos theory while exploring Jurassic Park.
Then you move to industry and start working for The Wolf of Wall Street. Or Patrick Bateman. Or those greedy men who want to make a profit from Jurassic Park and end up getting a lot of people killed.
Yes, these are the stereotypes we are used to, but how do they hold up in reality…
Are you looking for your first data science job after leaving academia? It may not be as easy as you think.
When I left astrophysics I thought that finding a data science job in industry would be easy. After all, I was already quite good with both data and science. But the truth is that going over to the dark si… I mean finding my first industry job was actually much harder than I expected.
The job application processes in industry and academia differ significantly. And more importantly, there are a few fundamental differences in what makes a candidate successful. …
Are you considering leaving academia, but you’re afraid of not being able to go back if you change your mind?
Believe me, I understand your fear. I don’t have any tattoos, I backup my files regularly, and I appreciate having an “undo” button above these lines as I write them. In other words: I always try to avoid irreversible changes. So, when I started pondering leaving academia to become a data scientist, that same fear was holding me back. But is leaving academia really an irreversible career change?
The short answer
Of course, a definitive answer to this generic question would require gathering data and thoroughly analysing it. But I’m in a lazy Sunday afternoon mood (plus I’m not an academic anymore!). Instead…
Are you leaving academia to start working on so-called “real world” problems? If so, you’re probably all too familiar with this turn of phrase. Well, I was in the same boat 4 years ago, and in this post, I’d like to share with you my views on what we should call real world problems, and what they actually are in industry.
What is the “real world”?
To start with, let me find my astrophysics hat, dust it off a bit and put it on for the first time in a while. I need to make a little digression about the real world… ahem.
Our planet…
Pablo Rosado
Data scientist in renewable energy, PhD in Astrophysics, technical mentor at Data Science for Social Good, effective altruist: https://pablorosado.com/
