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  • Writer's pictureAdmond Lee

Why Do I Write About Data Science?

Updated: Jan 14, 2020

And how writing helps in my data science career

But WHY did I even bother to write in the first place?

I like writing. Passion.

If you’ve been following my articles, you might have probably noticed the word: Passion — one way or another — appeared in most of the articles. And YES, it sounds so clichéd that very often people just talk about their passion — and I act on mine. And that’s why I am still writing now on a rainy Sunday early in the morning at 7am.

Ever since I was young, I had been following the traditional academic path in science stream in which technical skills were greatly emphasized. Writing, unfortunately, was not part of the focus. And I felt something was missing.

The best way to communicate is speaking. The second best way is to write.

I believe that our findings and ideas will only remain stagnant and nothing to ourselves if there’s NO communication carried out to bring values to others. And this is why I started writing since secondary school.

However, I can tell you now that the hardest part of writing is to start writing. Once you’ve overcome the activation energy to start writing, the flow will come in naturally. Trust me.

The process of writing and the power of words have never failed to amaze me.

Process of writing. Very often, I like to put my thoughts into words and share my view with others. Putting my thoughts into words is not easy as I need to calm my Monkey Mind down and be in a collected and peaceful state of mind. Guess what? In fact, this is the part that I like the most. I enjoy the peace, the flow, the thinking process that really questions my understanding and scrutinizes every bit of information that I have before sharing to the world. Clarity matters here.

Power of words. You’ll never know how your words could impact others. People have their own perspective and your piece of writing could resonate with different people at an entirely different level. I first realized the power of words when reading the book — Life Is What You Make It written by Peter Buffett. I was utterly inspired and motivated by the rich emotion and meaning carried by the words, all without even seeing the author speak.

Authenticity is key

And now, I hope to share my thoughts through writing that hopefully can share my learning experience in data science as well as inspire and motivate more people, regardless of their background and career choice.

Why Do I Write About Data Science

Data science field is still very young. Thus writing about data science comes to me naturally at the sweet intersection between writing and data science.

Well, there are more reasons than that.

1. Help and guide aspiring data scientists and enthusiasts

I started out in data science from ZERO, literally.

As a self-started and self-learner in the beginning, I lost count of the challenges, obstacles and the moments of frustration that I encountered. Even for an aspiring data scientist to google search: How to Become a Data Scientist, chances are you’ll be overwhelmed with tons of resources out there, each of which claims to be one of the greatest guides ever. But who knows?

Google Search: How to Become a Data Scientist

Frustrated. I feel you, and I truly understand that. It is so devastating to me that I am still receiving many messages on how to become a data scientist (or a data professional in general).

With this in mind, I decided to document my data science journey (if not all) by writing and sharing to everyone.

I’d not say my articles cover the full spectrum of the must-have skills in order to succeed in data science. Nonetheless, I hope that by sharing my understanding and knowledge of data and the current job landscape, your journey in data science could be less bumpy and hopefully more enjoyable and faster to reach your goals.

A well-written quote by Kirill Eremenko in one of his articles:

As data scientists, it’s important that we share this understanding and knowledge of data with everyone else, because chances are they don’t know. And they should know — Kirill Eremenko

2. Improve communication skills

Probably you’ve seen these two elements in some of my articles — 5 Lessons I Have Learned From Data Science In Real Working Experience and Storytelling with Data. If it wasn’t obvious by now, communication skills and storytelling are so important that I can’t stress enough of it.

Don’t get me wrong. I’m not saying that technical aspects are less important but rather it is the matter of how we need to communicate results better to really make an impact.

Personally, I’m in total agreement with what William Koehrsen wrote in his article —The most important part of a data science project is writing a blog post:

Writing a blog post gives you practice in one of the most critical parts of data science: communicating your work to a wide audience. Well-written code and a thorough analysis is a good start, but to complete your project, you need to tie it into a compelling narrative. An article is the perfect medium to explain your results and make people care about all your hard work William Koehrsen

Therefore, writing really helps in consolidating the technical aspects of codes and and communicating better by means of compelling storytelling. Not only do I share what I learned, but also learn from what others shared.

William Koehrsen is the best data science communicator that I have ever found on Medium. His ability in breaking down complex ideas and codes into simple yet compelling stories and concepts are so inspiring and fascinating. Special thanks to William Koehrsen for your contribution to the data science community! 😄

How Writing Helps In My Data Science Career



I wish I could have known the importance of building valuable connections few years ago. Through writing — believe it or not — your articles are shared and posted across different social media platforms (Medium, LinkedIn, Facebook, Instagram etc.) with the potential to reach thousands of people within seconds.

The power of the Internet is unparalleled, given that every person is placed on this level playing field to demonstrate their capabilities and showcase to the world.

Many people try to feed data to some fancy models without first understanding their data and company’s business; some people understand their data and company’s business but are not able to communicate results to stakeholders; few people understand their data and company’s business and are still able to communicate results to stakeholders and even wider range of audience.

Through writing, I am continuously learning how to communicate results and provide actionable insights to different stakeholders and a wide range of audience. The benefits have been immense so far and there is so much more to learn! In fact, writing about data science shows people that you are really passionate about it and care about educating and sharing to others.

Opportunities come to those who are prepared. And being prepared simply means being consistent with what you’re doing — writing in this case.

I started writing this year and have formed valuable connections and relationships with potential employers, collaborators in some projects, editors, many enthusiastic data professionals and aspiring data scientists, and even led to speaking engagement!


Final Thoughts


Thank you for reading. Hopefully this article sheds light on my motivation to write about data science. If you’re a writer in data space as well, feel free to ping me for collaboration (or whatever reason it may be 😊)!

If you’re just about to start writing (data science or other topics, it doesn’t matter), remember the hardest part of writing is getting started. To overcome it, reduce your activation energy and minimize procrastination by writing down your ideas on a piece of paper and start restructuring and developing your ideas from there.

As famously quoted by Mark Zuckerberg in his commencement address at Harvard:

Ideas don’t come out fully formed. They only become clear as you work on them. You just have to get started.

As always, if you have any questions, feel free to leave your comments below. Till then, see you in the next post!

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