A Short Defense of the Citizen Data Scientist

You hear a lot about a huge shortage of data scientists, and those data science unicorns with such hard to find skillsets. For some companies it might be true that they really need data scientists and engineers on par with Apple and Google. I've also heard a lot of criticisms lately about folks with only a bachelors degree having data scientist titles. I admit, graduate training made a big difference for me in terms of having a deeper understanding of statistics and experimental design as well as some of the mechanics of machine learning algorithms. To take advantage of that training I heavily leveraged my undergraduate quantitative and analytical courses in economics, statistics, and mathematics. There are plenty of folks out there with heavy quant skills brandishing bachelor's degrees that combined with programming skills and subject matter expertise are able to create value. Think of engineers, actuaries, market researchers, or quants working for hedge funds to name a few.

Enter the citizen data scientist.....

Regardless of degree, what skills are essential to solve business problems? As Kirill Eremenko and Greg Pope discuss in a Super Data Science podcast, this level of acumen does not require an all knowing data science unicorn:

Kirill: "I think there’s a level of acumen that people should have, especially going into data science role. ..You might not need that much detail…If you’re doing the algorithms, that acumen might be enough. You don’t need to know the nitty-gritty mathematical academic formulas to everything about support vector machines or Kernels and stuff like that to apply it properly and get results. ...I think the space of data science is so broad you can’t just learn everything in huge depths. It’s better to learn everything to an acceptable level of acumen and then deepen your knowledge in the spaces that you need."

Greg: "if you don’t want to get into that detail, I totally get it. You can be totally fine without it. I have never once in my career had somebody ask me what are the formulas behind the algorithm….there’s a lot of jobs out there for people that don’t know them."

Scott Nicholson makes a similar point bout the most important skillsets to focus on:

GP: What advice you have for aspiring data scientists?

SN: Focus less on algorithms and fancy technology & more on identifying questions, and extracting/cleaning/verifying data. People often ask me how to get started, and I usually recommend that they start with a question and follow through with the end-to-end process before they think about implementing state-of-the-art technology or algorithms. Grab some data, clean it, visualize it, and run a regression or some k-means before you do anything else. That basic set of skills surprisingly is something that a lot of people are just not good at but it is crucial.