It’s easy to find work in data science and analytics – provided you have the right skills. While the pandemic certainly slowed things down, the demand for data skills remains strong. People with professional experience in SQL, Python, and R will likely find themselves hearing from recruiters more and more – much like they did before the pandemic.

When work naturally finds its way towards you, you might find yourself asking – does networking really matter in data science?

Yes. 100% yes. There's two big reasons why.

Back when I was in college, I had a lunch meeting with the chief financial officer and founding partner of a hedge fund. I was in my junior or senior year back then and never had any prior relationship with this person. I had merely emailed him out of the blue.

My roommate at the time had told me that she was amazed that I could, without hesitation, email someone so successful and ask to meet them for lunch – and that the person would actually say yes!

I’ve continued this habit of reaching out and meeting with successful people ever since then. And by successful, I mean those who are well-respected or well-recognized in their field. That includes people in senior management, well-known public speakers, and authors of widely read books.

Reaching out to successful people like this makes some people anxious though.

That’s how my roommate had felt when I had met with the hedge fund partner. That would’ve made her self-conscious, she said. She knew it was a smart thing to do, but she had that nagging voice in her head that told her she’d somehow offend or annoy the person.

That voice would ask her, “why would someone so successful want to talk to me?”

It’s that question that keeps people from networking with those who would make all the difference in their career.

People often make machine learning out to be something from a science fiction novel. It sounds like teaching a computer to become sentient or something even more outlandish.

In reality, machine learning is usually just statistics, but it's a very specific use of statistics. In fact, my graduate course on machine learning was called “statistical learning.” (I must say that “machine learning” is certainly better branding though.)

But how exactly is machine learning different from other statistics?

There’s two key differences:

1. We care more about predicting an outcome and less about explaining how that outcome happens*
2. We write computer programs to improve those predictions

More on those differences below...

Everyone wants to be a data scientist, nowadays. It’s the “sexiest job of the 21st century,” according to Harvard Business Review. It's intellectually rewarding, pays well, and has consistently strong job growth.

The hard part, though, is landing your first data scientist job. It seems every company has that "two years experience" requirement, which makes it a challenge for newcomers to break into the field.

It took me five years after graduating with my undergrad degree to land my first data scientist job. I know others who transitioned into the role sooner, but I had to network and grow my skill set through graduate school before I was ever seriously considered for the job.

Like most analytics jobs, data scientist roles require a solid foundation in technology, with the addition of statistical expertise. It simply doesn’t matter how good of a strategist or communicator you are – you can’t succeed in a data scientist role without those skills.

Ironically, long-term success requires the opposite focus. After you mastered the technical and statistical expertise, you have to actively work to expand your soft skills. That will ensure your talents translate into a positive impact for your stakeholders.

There's a long list of things to learn, but it's helpful to simplify what you should focus on to achieve this goal. Down below are six specific skill groups that will help you either land a data scientist job or improve in your existing data scientist role.