I’m about to start blogging about data science again, and I wanted to preface it with a little story about my journey towards Data Science.

A couple of years ago something happened. As I was browsing a list of project ideas that my teammates have suggested for internal Hackathon at JetBrains, one particular project caught my attention. Someone suggested to have fun and build a tool generating lexical jokes. The project didn’t get much attention and no one actually worked on it during said Hackathon, but reading about it made something inside me click. I had my “Aha!” moment.

Let me explain, and I’ll go way back when. Back in school, my favourite subject has always been math, partly due to having an incredible math teacher. Another thing I couldn’t get enough of was literature. Yep, I was that kid who solved math problems FOR FUN, and could easily spend a week at home reading Honoré de Balzac, one book after another. Fast forward, I’m a student at the faculty of Mathematics and Mechanics doing “Applied Informatics”. I study all sorts of math branches: algebra, probability theory, statistics, differential equations, you name it. I study Java (sort of), and computer science too. There are coding projects, but nothing really clicks for me. I’m toying with the idea of going deeper into math and research, but in the reality of those times (Russia, 2007), pursuing scientific career would practically mean never earning any money. At the same time, I start working as a technical writer as a side gig. And when I need to pick a graduation project in 2007, I go with something familiar - technical texts. I try to build a tool for text style analysis and suggestions. For this being a graduation project, given time restrictions and very limited programming skills that was an overly ambitious idea, and, the implementation ended up being half-baked and semi-working. It left me disappointed but apparently it was good enough to graduate.

After graduation I ditch the idea of following the path of math, and move on as a technical writer. A number of times throughout my life I find myself regretting breaking up with science. Every time I get my hands on some data, I see potential for analysis, for predictions being based on it, actions being driven from it, I get this brain itching to do something with it but I don’t quite know how.

The moment I read about that hackathon project I feel the excitement I had when I was a kid solving math problems. That’s when it finally clicks to me as I put together all those moments that would make me feeling excited about data and helpless at the same time. But this time I know what’s missing, I know what I need to do to stop being helpless and start solving those puzzles - I need to learn. Even better, now I know what it is I need to learn - Data Science, Machine Learning, Deep Learning, - the terms I haven’t even heard of back when I was a student.

And that’s how it began for me. I started this blog to record the things that I learned on my way of exploring data science and rediscovering those fields of math that I have half-forgotten I started taking courses, one after another, reading books on data science, tutorials, watching recordings of conference talks, and with each step I made there was 10x times more stuff to learn! At some point life got in the way, so I had to put blogging on pause. But I didn’t stop learning. And now that I’ve sorted things out, and I’ve accumulated some knowledge that is simmering inside my head and is asking to be brought out to this world in some structured manner. So, cheers to take two and I hope you’ll find the upcoming series of posts on Data Science useful. Stay tuned and let’s keep learning together!