How a Start in Reality Television Showed Me Success in Data Science How a Start in Reality Television Showed Me Success in Data Science
How did I become a success in data science? Fun trivia fact: I have a background in reality television. No, I... How a Start in Reality Television Showed Me Success in Data Science

How did I become a success in data science?

Fun trivia fact: I have a background in reality television. No, I wasn’t on camera, but I was one of the guys behind the scenes making sure all the magic happened. Somehow, now I am immersed in the data science and technology space. I’ve had people ask me, “How on earth did that happen? Do you know how to code and all that stuff?”

I reply, “It’s been quite the adventure. And I’m learning ‘to code and all that stuff.’” Data science might be a hot field to pursue a career but for people without a technical background + fundamentals, it appears daunting. This does not mean, however, it is darn near impossible to succeed. Success might just take a different course. I have a liberal arts background with a dual master’s in Management Information Systems (MIS) + MBA and here I am.

People with liberal arts background or who are more prone to be “non-technical” CAN understand, grow, and become a success in data science space. Looking back on the twists and turns I took to be where I am today, there are a few things, core fundamentals so to speak, that come to mind that help non-technical people make sense of this naturally technical field.

Ask Questions

Something I’ve noticed: working with your left brain alone or right brain alone won’t work anymore. It is necessary to use both sides of the brain effectively to succeed. So, this is where I started to question HOW I could unite both my natural creative characteristics with the necessary analytic skills.

success in data scienceFrom there, I talked to people and asked questions about how I could get more technical while still leveraging my strengths. Unfortunately, there is no “go-to” guide for how non-technical people can jump into data science. As I found out, it’s necessary to continually ask questions, explore all options based on the answers you get and piece together the various parts of the puzzle that will get you where you want to go.

Sometimes the answers won’t be what you were expecting and may not lead you to where you want to be but keep asking and eventually the most appropriate answer will reveal itself. I do firmly believe that there is no such thing as a stupid question when you want to understand what you don’t know.

Who to ask? Anyone you think who might be remotely involved in data science. Heck, ask me and we can strike up a conversation. Some good places to start are digital avenues such as Quora, Twitter, Facebook Groups, Github and other similar platforms in which you can ask questions, interact with other members of the community and discover various possibilities in the data science space.

Get Involved

success in data science

Based on the several thousands of questions I’ve asked, I connected with new people via networking in-person and digitally. Jump in and start getting involved. Whether it be going to a local open source meet-up from Meetup.com or connecting with someone on an informational phone call, involving yourself in the larger community takes you one step closer to clarifying how to get into data science.

New acquaintances and connections open doors to new events, summits, conferences, meet-up groups and more which in turn leads to more questions, more introductions, more connections. Wash. Rinse. Repeat. All of this allows you to get involved.

Align Your Passions to the Appropriate Focus

success in data science

We all have something that make us jump up and down. Whatever you enjoy doing, you can align it to some aspect of data science. Love storytelling? Look into data visualization and data discovery. Like to build things? Look into developing and programming. Enjoy organizing and keeping things organized? Check out dataops and management. No matter, take your natural passions and what you already love doing and apply them to a particular focus in data science. It’s doable. You just need to ask the right questions, get involved and experiment with what works for you.

Get Hands-On and Learn

Experiment, experiment, experiment. You learn by doing in this field. If you have the opportunity to learn something, accept the challenge. You might discover that the project is not necessarily what you want to do but you learned something new (what you do NOT want to do in the long run) and you also understand how the particular components of that data science project work. Look to local bootcamps, conferences and meet-ups to expand your skill sets. See if your job has a stretch process and a supportive IT team that will help you in a venture in the case you have a new idea. Commit, get hands-on and build.

Be Prepared for a Stiff Challenge

Confession: this is NOT easy. Learning specific components of this space, particularly the very technical aspects, is intricate and time consuming. This holds especially true when you first start out. When I waded into the waters of data science, I had to first understand the extreme basics of statistics and computer science. This meant googling things like “how does a computer work?” From there, it was research, interviews, questions and writing. I learned and am still learning. Challenge fully accepted.

How can you potentially “jump start” this entire process? Check out local groups or national conferences. These places enable you, as it did for me, to connect with like-minded people, ask any sort of question and get well-meant answers that hopefully will provide clarify to you on how you can dive into the world of data science. It’s possible and very achievable. It doesn’t matter where you might have started your career. It could be in reality television like me or in art history or in the biology lab. No matter, if you have an interest and the passion to learn, you will find a way. Ask the questions, get hands-on (or at least read about the subject), and you can become a success in data science.