What You Can Learn About Writing From Data Scientists
Three tips from some of the most prolific writers on Medium
Data scientists create some of the most-read content on Medium, in publications like Towards Data Science and elsewhere. Whether you’re interested in data science or not, there’s a lot to be learned from their work. Here are three truths that will help any writer come up with ideas of what to write and find readers for that work:
Use your expertise to address a universal question
Check out this headline, written by Cassie Kozyrkov, the head of decision intelligence at Google: “How to hack your motivation, according to a decision scientist.” Pretty appealing, eh? We all want to be motivated and to cultivate better habits, and the idea of a scientific, data-backed way to do this sounds great. Data scientists tend to be very good at this — after all, data can really apply to anything, from cracking Wordle (thank you very much barrysmyth), to how to earn a higher salary (as Khuyen Tran writes).
What do you have some special insight into, because of your field, area of expertise, or life experience? How does this give you a unique insight into something everyone is talking about right now?
Share your career story
Often we don’t really think of our career trajectories as narratives unless we’re specifically called upon to, for example in a job interview process. Chances are your path to your current job seems ordinary to you, or haphazard, or not worth telling about. But people just starting out in your field — whatever that field may be — are hungry for stories like yours.
Take a look at Ani Madurkar’s story Zero Coding to Senior Data Scientist in 4 Years. “My story starts at what was a hard rock bottom at the time and now I get to work on fascinating end-to-end machine learning problems,” he writes. “My Data Science journey is not at all a traditional one.” It draws the reader in, all while providing a great service to rookies in the field.
Another great example of this: Piero Paialunga’s This is what happened after my Bachelor’s Degree in Physics. His story of how he went from studying physics to working in data science and machine learning includes many twists and turns. And he even includes some of his friends’ career stories at the end. The end result: A deeply useful resource for data scientists — and anyone interested in the many shapes career paths can take.
Tell your readers what you’re about to tell them
Your potential readers have a lot of options for how they can spend their time and attention. Why should they read your piece? It’s your job to tell them; let them know what to expect, and then make sure your piece lives up to that promise.
Eugenia Anello’s story How to create a strong Data Science Portfolio for free tells the reader clearly what they are about to get from the piece, and how they will benefit from reading it. As she wisely writes, “Perseverance and patience are the keys to solving any problem.” Then there is Rose Day, who writes in Why You Should Push Yourself Out of Your Comfort Zone, “One of the many reasons I enjoy working in the technology and data space is the many possibilities.” Dr. Varshita Sher makes a very specific promise in her piece Step by step guide to explaining your ML project during a data science interview. If you have an ML project to explain, well, you know this is the article for you.
These stories all share one important feature: They all center the reader. You are spoken to directly in the title, and the pieces are all about offering you services and wisdom that will help you. And isn’t that, in the end, why many of us read?
