Starting Out in Data Science

If you want to be a data scientist, what do you do? I began with a simple Google search of the phrase. But I was soon inundated with abstract terms like machine learning, big data, and Pandas (the package variety, not the bamboo-eating, Instagram-worthy, residents of Chengdu). None of these phrases had any meaning at the time and the multitude of paths leading to data scientist seemed overwhelming.

Listening to the popular lifestyle podcast, The Art of Charm, I came across Chris Hadfield. Hadfield, a retired astronaut and test fighter pilot, spoke about the difference between wanting something and turning yourself into something. I wanted to become a data scientist but Hadfield’s advice told me to take an alternative route. I needed to turn myself into a data scientist. It was time to walk like the data scientists I looked up to.

I had already signed up for General Assembly’s Data Science Immersive program. That twelve-week full-time program would be a sure-fire way to kick-start my career as a data scientist. But what were the other things data scientists do that I wasn’t doing?

Udacity, Coursera, DataCamp, and Dataquest.io were all amazing resources in order to get my python chops to acceptable novice status. But after the third introduction tutorial on working with Pandas, I found myself forgetting why I thought data science was right for me.

I read Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t and then Seth Stephens-Davidowitz’s Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are.These books quickly reinvigorated my passion for data science. Silver and Stephens-Davidowitz were detectives in the fields of political science, crime, epidemiology, weather, baseball, poker, and much more. They used data science to bring light into areas of unsolved mystery. They were modern-day, real-life versions of Sherlock and Watson. I remembered why I enjoyed data science.

The hunt was on. After receiving recommendations from instructors at the General Assembly’s Data Science Immersive program, I began keeping up to date with people in the field like Jeremy Singer-Vine, Tony Chu, Chris Albon, and a few others. I began seeing data science in my inbox regularly with KDnuggests. I began hearing about data science during my morning commute with podcasts like Data Stories and The Data Skeptic. I was slowly turning myself into a data scientist.

The Data Science Immersive program began and I was soon talking data science with fellow students and instructors. It was a room full of data nerds. I could now talk about data science with twenty other people for hours on end without the other person falling asleep.

Now, I continue turning myself into a data scientist every day. My next step is attending meetups and conferences. Even if I don’t understand all of the subtle nuances of each talk, I will inevitably learn something. The path toward turning yourself into a data scientist is anything but linear. That said, I am enjoying the process.