Transforming your data science journey, that’s a significant promise. Now that you’ve started reading, I have to deliver (and trust me, I will). What you’re about to read is nothing fancy. I saw many experts do this, and I adapted it for me. It worked for many, so I know it’ll work for you too.
Instead of presenting these into numbered steps, I choose to disguise it behind my story. When you look for it, and thou shall receive it.
Finding myself in a challenging situation.
It was a Monday, and we had to do a demo for the stakeholders on a product we have been working on by Wednesday. To give you some context, I was a Machine Learning Engineer who was mostly involved with researching, prototyping, and developing AI products.
We got terrific results for the experiments we ran; the product was in good shape. But we also knew we’d have to build a working application for the demo, and the problem was we were short of time. And on top of it, we didn’t have any experience in building apps.
With the limited time I had, I searched all over google for tutorials, walkthrough videos, documentation, and tools to build machine learning applications. I stumbled upon a tool/library called Streamlit and understood this could do the job.
All of us find ourselves in challenging situations, how we approach them makes all the difference.
Starting to learn all over again.
I love to learn all over again. I naturally learn every day. With years of experience, I’ve mastered the art of learning how to learn, and I’m relatively quick about it.
I knew the time I had in hand was limited. Still, I went on to watch walkthrough videos on YouTube, navigate through articles written on Streamlit, and finally, the rabbit hole of official documentation.
I was pleasantly surprised when I discovered how simple it was and built my machine learning app within the day, thereby meeting the Wednesday deadline. A day of learning and developing, boom, we have the working product for the demo!
Learning is truly the first step for mastery, and don’t let anybody tell you otherwise.
Grabbing when an opportunity knocks.
We finally delivered the presentation and demo to the stakeholders. My manager, who was also at the demo, wanted me to introduce this tool to the team.
I didn’t hesitate for a second. I was more than happy to share everything about machine learning app development with the team. I prepared and conducted a workshop, had a live-coding session, and helped the team upskill.
More than anything, I saw it as an opportunity to grow and become better. Post-workshop, I asked for feedback from the team. I listened to what I did well and what I could improve. I couldn’t have been more satisfied; it was a win-win!
Opportunities are everywhere, making it big or letting it go is all on us.
Sharing the experience with the world.
In most of the feedback I received, one thing stood out. My app development process was easy to grasp and saved the team a lot of hours. What if I could share this with the world for everyone’s benefit?
Soon enough, I started writing my heart out. I took the time to create a new real-world example, push the codes to a Github repository, embed code snippets, and finally published it on Towards AI.
This article went on to one of my most viewed articles. (If you’re curious to read the article, you can do so here.) Everyone who read enjoyed it and my LinkedIn was full of positive feedback. Eventually, Towards AI publication featured my writing for the month's newsletter. I was tasting success—the success of my approach.
And before I could process everything, I became a Top Writer. Here’s a thing about Medium, most Top Writers on medium publish a lot. I don’t. I have hardly 10 articles published. Instead of quantity, I focussed on quality.
Every time I publish, I make sure I give something of value to the reader. While delivering value, I focus on improving myself, becoming a better data scientist, and becoming a better machine learning engineer with every article I publish. The top Writer tag means nothing to me; a better data scientist means everything to me.
Enough of my story.
I didn’t say all of this to boast about me.
In fact, this has never been about me.
It’s always been about you.
It’s to show you the results of this approach.
It’s to show you proof that this approach works.
The 3 step approach
This is not the first time I’m talking about this approach, nor will it be the last. This is what I have been doing all along. When beginners ask how to get started and become better at data science, I tell them this. To learn, to create, and finally to share. To be honest, I don’t know any other way.
Adapting this doesn't need a year or two; you only need to alter your mindset. You can do it now before you finish this article.
I sincerely hope you do.
- Learn. The challenging situations will come. When it does, don’t hesitate to start learning from scratch. Remember that learning will always be the first step; don’t let anybody tell you otherwise.
- Create. Remember that you can learn as long as you wish, but quickly turnaround and create something out of what you learned. You will be surprised when you realize how much you can improve while building projects, all while you create a demonstrable portfolio.
- Share. Finally, since you have done all the hard work — to learn and to create, why not take the time to share it with the world? I share so you benefit, and you share, and someone would thank you for it.
You stuck with me till the end, so here’s a bonus.
Feedback is your life-time teacher. Please seek feedback whenever you can. While you’re learning, after you create something, when you share your work, explicitly ask for feedback. When you ask, people give, and you update your knowledge. (Recall how machine learning works in the first place, haha!)
Adapting this approach doesn’t need a year or two, only the change in your mindset!
The Change of Mindset
All it takes is this change of mindset. Every day in this data science world, keep this simplest approach — Learn, Create, and Share in your mind. It’s never too late to start learning. It’s never too late to start building a portfolio. It’s never too late to start sharing. If you look closely, the experts in our industry do it on a daily basis.
If you take most of these experts, there’s a pattern. The industry knows they’re already experts. But no, they still keep on improving themselves. They keep on creating research papers, projects, videos, articles, posts, walkthroughs, and more. Then they keep sharing their knowledge with the world.
Trust in this approach and start working on it. With time you’ll see your journey wholly transformed. You’ll see the transformation for yourself. I can’t wait to see you succeed!
I hope you found this article useful. If you enjoyed it, I’d like to hear your feedback, and let’s engage in the comments. I share my learnings, valuable resources, and experiences from my data science journey here at Medium. Want to connect and keep in touch?