In Data Science

By Dimitri Antinou

Over the last few blog posts, we’ve answered a lot of questions around Data Science: What is it? What’s the difference from data analytics? Which type of program is right for me? If you’re interested in becoming a data scientist, you might be wondering how other people got into the field. Given how new the profession is, most of today’s practitioners probably didn’t study data science formally as undergraduate or graduate students. So today we’re asking: where do data scientists come from?

Let’s start broadly by defining the possible pathways into this career. If you’re a Data Scientist, you probably followed one or more of these paths:

  • Learning on the job: You ‘did it live’ and hacked your way into a data science skillset.
  • Universities: You studied Data Science, Analytics, Statistics, Programming, or Business formally in a university setting.
  • MOOCs (Massive Open Online Courses): You learned through an online resource like Udemy or Codecademy.
  • On-site or corporate training: You were trained by a learning & development department, internal academy, or contracted provider.
  • Immersive programs/bootcamps: You went to coding bootcamp  and learned Data Science through an immersive, hands-on career accelerator (like Codeup, perhaps?)

Each of these pathways has unique advantages and disadvantages across variables like cost, formal credential, length, and pace. A free online program is free and accessible, but takes a lot of dedication to follow through and is harder to change careers with. A bootcamp specializes in quick and efficient job outcomes, but is a big investment. A university offers a formal degree and dives deeper, but is more expensive and takes longer.

Each of these pathways also leaves gaps against the complete picture of a data scientist. From your training you might be missing components like: working with real data sets, understanding industry and company demands, using up-to-date technologies, or even just knowing what you don’t know! What’s important to understand here is that different pathways yield strengths and gaps. Your job is to find, acknowledge, and improve your gap areas!

Now that we have a framework for understanding potential pathways, let’s look at some data. In preparing to launch Codeup’s immersive Data Science program, we researched over 250 data scientist profiles on LinkedIn and analyzed their educational and career histories. Here’s what we found!

Education

95% have a Bachelor’s Degree, 70% have a Master’s Degree, and 27% have a PhD. Of those degrees, the most represented areas of study are: math, stats, business, engineering, and CS.

Degree's by area of study

Career History

Data Scientists come from an incredibly diverse range of professional backgrounds: psychology research, software development, business analyst, mechanical engineering, and more! We saw a few prominent patterns in our data:

Data Scientist Career History

Archetypes

A third component of our research was to interview practicing Data Scientists. We asked questions like: What was your path to the field? What did you study? Is there a need for programs like Codeup? What are the most important skills to learn? After conducting these interviews, we had three valuable lenses to understand the paths into Data Science: educational histories, career histories, and first-hand qualitative research. From these three, we compiled 5 archetypal Data Scientist personas!

 

Codeup Data Science Personas

If you see yourself in any of this research, you might be on the path to becoming a Data Scientist! Still have questions? Reach out to us at (210) 802-7289 or DataScience@codeup.com! Wondering which of Codeup’s programs is right for you? We’ve got you covered. And of course, if data science gets you excited, get started with us today at codeup.com/apply!