Data Science

How to get into data science from a non-technical background

22nd Jun `17, 11:00 AM in Data Science

DJ Patil, the current Chief Data Scientist of the United States and previously the Head of Data Products…

Roger Huang
Roger Huang Contributor

DJ Patil, the current Chief Data Scientist of the United States and previously the Head of Data Products at Linkedin, is the one who first coined the term “data science”.

DJ posits that, “the dominant trait among data scientists is an intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested.” There is no mention here of a strict definition of data science, nor of a profile that must fit it.

Doug Cutting, one of the most famous data engineers in the world and the creator of the Hadoop framework that has helped propel data science into the mainstream graduated with an AB in linguistics. Tim O’Reilly, now known as the founder of O’Reilly Media, and the curator of thousands of data and programming resources, graduated in Classics.

The most important trait among data scientists aren’t technical degrees, or the amount of time spent in school. It’s the curiosity that pulls them to hard problems and pulls out solutions and new insights from old datasets.

You can get into data science from a non-technical background and do the same thing. Springboard has recently built a Data Science Career Track to help you do just that. Here’s some actionable advice from them.

  1. Skill up with a curated curriculum
  2. Do real-life projects
  3. Join data science communities
  4. Attend data science events
  5. Get a mentor in the field
  6. Prepare for the interview
  7. Never stop learning

1. Skill Up with a Curated Curriculum

One of the top things a degree in data science would give you would be a structured curriculum with plenty of feedback and practice in the form of exams and assignments. The structure is especially important given how broad data science is.

Curating the resources you need to succeed would take significant amounts of time, especially if you didn’t know where to begin at all.

Springboard has a curated learning path that can serve as your curriculum.

2. Do Real-Life Projects

Once you’re done learning different skills and tools, there’s nothing better than to practice on real-life situations. Often the best way to substitute for the lack of demonstrated technical knowledge that a degree brings is the creation of real-life projects that drive impact.

A portfolio of your projects can help get you noticed and build your credentials as an aspiring data scientist. You will also learn how to apply your skills and improve them at a much faster pace.

You’ll want to tackle a substantive problem, and see if you can come up with a solution based on insights hidden in reams of data. Is it possible to predict electoral results based on turnout trends? It is possible to trace a basketball player’s performance to tweets of them partying the night before? The world is your canvas here–you can take any dataset and lend a fresh perspective to it with your new skills.

If you can’t find datasets, check out this list of 19 free public data sets, or look into a dataset search engine such as Quandl.

If you can’t think of anything to do, platforms like Kaggle, Datakind and Datadriven allow you to work with real corporate or social problems. By using your data science skills, you can show your ability to make a difference, and create the strongest portfolio asset of all: a demonstrated bias to action.

3. Join Data Science Communities

Inevitably, you’re going to want to branch out and look for data science communities to get the latest news and to discuss any problems you might be having. You’ll also begin seeing how data scientists interact with one another.

If you want the latest and greatest data science content, you might want to check out KDNuggets or Datatau. Datatau is an aggregator of data science content where people can upvote the best selection of data science content.

You’ll also want to follow data scientists on Twitter, and listen to different podcasts on data science. We have a list of the best accounts and podcasts to follow.

4. Attend Data Science Events

To really get immersed in the data science community, you’ll have to attend physical events. Thankfully there are many, from large-scale conferences to smaller meetups.

The three largest conferences are the Strata Conference, KDD (Knowledge Discovery in Data Science), and NIPS (Neural Information Processing Systems). These tend to be attended by hundreds if not thousands of industry practitioners, and they often feature technical tutorials and different talks that will help you gain a new perspective on data science.

Strata tends to be oriented to the latest trends in industry, from exciting startups, to established giants. KDD is more focused on the theory and knowledge of data science, and NIPS is more focused on academic advances in the field.

You don’t have to go to these conferences to get a sneak peek at what happens when you interact in-person with data science communities. You can attend the smaller data science meetups that happen all around the world.

The San Francisco Bay Area tends to have the most data meetups, though there is usually one in every major city in America. You can look up data science meetups near you with Some of the largest data science meetups, with more than 4,000 members, are SF Data Mining, Data Science DC, Data Science London, and the Bay Area R User Group.

5. Get a Mentor in the Field

It can be hard to navigate data science, especially if you come from a non-technical background. You won’t have access to the networks that run through Silicon Valley based on degree. Once you feel like you are on firm ground it’s more important than ever for you to connect with somebody who is in the industry so that they can give you feedback on where you need to improve and importantly, an inside contact and reference when you need it. Much of the hiring that happens in data science, like in any other field, often requires networking. Most jobs aren’t posted openly, they’re discussed among networks of contacts.

Make sure that you’re involved with the community and that you have somebody to go to bat for you. It can mean the difference between trying to find a job, and working your first day as a data scientist.

You can also use a solution such as Springboard’s mentored Data Science Career Track to advance your data science career.

6. Prepare for the Intervieവ്

If you come from a non-technical background, a data science interview can be especially intimidating because it wraps up software engineering questions with statistics and math. To get a preview of what you’re in for, check out the Data Science Interview Handbook packed with 120 sample questions.

The data science interview is a very strange beast. You’ll be asked about what you know on the business and industry and about your past experiences–like any typical interview. You’ll also be asked about your data engineering skills, and in many ways aspects of the interview will be like software engineering interviews with a bit of statistics and data science theory. Make sure that you’re prepared for anything that might come up.

7. Never Stop Learning

To perfect every skill data science requires would mean spending several lifetimes on the topic. You’re never going to finish learning, and you should always keep the spirit of intellectual curiosity that brought you to data science in the first place.

Paul Kalanithi, a Stanford surgeon who had to confront early mortality, wrote poetically about his outlook on life in his memoir, When Breath Becomes Air: “You can’t ever reach perfection, but you can believe in an asymptote toward which you are ceaselessly striving.”

You will have to embrace that mentality if you’re going to get into data science and continue your career. Maybe someday you’ll become a mentor yourself, teaching as you learn, and completing the data science cycle!