My writing engagement at Data Science Central came up unexpectedly. Back in August 2018, I stumbled upon an excellent write-up on Data Science Central. The author, Bill Vorhies, shared his thoughts on career transitioning toward data science. I wrote him an email, complimenting him on his blog post, and I dropped a few lines about my own transition. Here’s his response:
“Congratulations on your remarkable journey. Perhaps you’d like to write one or more articles around this theme as we get many inquiries from folks who wonder how or if they can make a mid-career transition into data science. If this appeals, let me know and I’ll send you the details on how to contribute.”
That’s how I got started.
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On August 30, I aired my first blog post on Data Science Central: “Career Transition Towards Data Analytics & Science. Here’s my Story”
Expect to meet and engage with exceptional people on Data Science Central
In response to my first write-up, I received dozens of comments, contact requests, and emails from readers. All of them were very encouraging, and I took every single opportunity to have a one-on-one conversation. Over the last three months, I had approximately 50 calls on Zoom with a broad range of data science enthusiasts:
- career transitioners towards data science (developers, project managers etc.)
- data science practitioners, both in academia and the corporate world
- students aiming to land a job in data science once they graduate
- data science and AI executives
At present, I am bootstrapping my business, a Data Literacy Consultancy. In essence, I am monetizing my learning journey: I develop learning strategies for myself, test and implement them, improve and adjust them for the needs of corporate customers, and then I sell them as a service.
It might seem like a natural choice to write about my business to get new customers. Bill gave me very clear instructions though:
“Our goal is to provide an opportunity for members of the data science and big data community to share information and insights and to enhance their professional reputation through broad readership not to provide advertising or build traffic for other sites.”
Hence, I took a different route: “What if I share insights, see how people respond to them, and that way gain new insights worth sharing again?” In a nutshell, I took my writing engagement at Data Science Central as an opportunity for openly shared R&D. Learn, give back, learn, give back, learn and give back. With every blog post, I learned new things which helped me tweak and enhance my business offering. Many of my blog posts were featured on Data Science Central, and some even were temporarily amongst the most read articles on this site.
Let me recap my lessons learned if you are contemplating to become an author on Data Science Central:
- If you want to learn, teach others. Whenever you teach others, their feedback is going to accelerate your learning journey.
- Talk to people, literally. I always wrote back each of my readers who emailed me if they had time for a one on one Zoom call. Most agreed.
- Don’t be afraid to fail. Some of my write-ups were not so stellar, and readers told me so. Just take them offline, rewrite, and post them again.
- Numbers (don’t) matter. It’s good to get thousands of readers and hundreds of likes. Some topics need to grow and evolve though before they attract a broader audience.
- Think loudly. Don’t wait for the perfect, blissful insight that will change the world of data science forever. Take writing as an opportunity to engage with real people in meaningful conversations.
- Embrace simplicity. Don’t be intimidated by the collective brainpower of the Data Science Central community. Clarity of thought in conjunction with simplicity of expression is highly appreciated here.
- Read the editorial guidelines. You can find them here. Once you are ready to get started, sign up as a member and get in touch with Bill. Here’s his email: [email protected]
I am happy to hear your thoughts. Leave a comment, write an email to [email protected] or connect with me via LinkedIn.