The Myth of Entry-level Data Science Posted on : Nov 15 - 2017

There might not be any topic a data scientist is asked more about than “how can I get into data science.” I get it. It’s a great career and every week in the last few years there’s a new article about the unmet demand for “the best job in America.” Working on some of the most exciting new technologies like self-driving cars and AI powered chatbots is understandably appealing. And yet, it seems hard to find these jobs. If you’re baffled by this paradox, you’re not alone.

What is data science?

To understand how to become a data scientist, it’s best to get on the same page on what data science is. And if this is your career path, get accustomed to always defining your domain before you begin.

Data science, fundamentally, is about using the scientific method to solve practical problems in a business setting. Things like: “what steps can we take to measurably reduce customer churn?” or “how much of our inventory losses are due to fraud, and how can we reduce that?” The tools involved will evolve and snazzy but vague buzzwords can be confusing and misleading. But data science isn’t defined by deep learning networks, using Bayesian statistics, or however we define ‘AI’ this week. Data science is a practice, not a particular skill set.

To be successful, you’ll need to bring a variety of skills and experience to bear. For any given question, it may be necessary to write code to collect and clean data, run traditional statistical analysis to verify that your data can answer a given question, build predictive machine learning models, visualize the data in creative and expressive ways, and explain the results to whomever needs to know what you’ve discovered. Beyond the technical pieces, you’ll also need a deep understanding of the business and the topic at hand.

And this is why there are so few job postings for beginner data scientists. Doing this kind of research day in, day out requires diverse knowledge and experience.

Break in through practicality

So how can anyone get started? The straight answer? There aren’t many opportunities for entry-level data scientists. An advanced degree in a mathematical discipline, substantial training in statistics, or experience in rigorous experimental practices aren’t a must but you will have to be strong with these fundamentals.

Every year, PhD graduates in statistics, econometrics, hard sciences, and computer science—many focusing specifically on machine learning—discover they have zero interest in academia and enter the workforce. You’ll have to stand out from that cohort when going for your first data science gig. View More