How to “make it” in any field?

Last week at the Open Data Science Conference, a young volunteer graduate student asked me what she needed to do to "make it" in Data Science. I told her to keep doing what she was doing. Conferences are a great way to learn new skills and meet new people. And nothing beats in-person events for networking. So many of the colleagues I can now call my friends I met at an event.

Then, she asked me in what other ways conferences have helped me. I said, "I was once a graduate international student volunteer at ODSC just like you, and I don't think I'd be speaking here today had I not learned other skills that are not taught in courses — at conferences!". She was in disbelief and asked me what skills?

I told her that, unfortunately, most students obsess over academics, such as maximizing GPAs and publications, but they genuinely miss out on understanding the following:

💬 How do practitioners talk about data? It's a different language than that found in academia. For instance, it focuses on scalable deliverables and proving their consistency and performance with quality control. Speaking in these terms at job interviews will give them an edge over other candidates.

🔥 What methods/topics are popular in industry right now? In an ever-evolving field, it's essential to know what to focus on so you can tailor your portfolio and resume to match what employers are looking for.

🎯 What inspires you? It would be best if you had examples of how data science is leveraged in many fields to decide where to focus your efforts. Maybe you don't like what you thought you liked. Perhaps you found a niche you want to explore further.

🎨 How to think creatively about solving problems with data? There are countless ways to solve the very same problem correctly with data. Don't let anybody tell you that there's one optimal way. But it would help if you saw how other data scientists approach problems to spark creativity.

The volunteer nodded in agreement. She had been learning these things without knowing that she had! And it truly hadn't dawned on me that what seems like such a long way from data science conference volunteer to author and speaker was made shorter by eagerly attending every data science event I could. Learning is not always about toiling in the coursework but attending social events such as conferences, meetups, and hackathons.

Above picture by: Serg Masís, Featured image by: Ratta Pak @ Wikimedia

So, to sum up my advice to any newcomer: you need more than textbook knowledge to make it in any field, so you have to have the proactiveness and tenacity to seek opportunities to put yourself out there among your peers (much like the volunteer did). And that will rise you up!