A couple of years ago, I started this website with a very simple idea: write short, handy how-to articles on data science. Practical things. The kinds of quick guides I used to search for all the time. Finding the domain name datascienceiscool.com felt like striking gold. It seemed perfect for what I wanted to build — a small place on the internet where someone could quickly learn how to solve common data science problems.
At the time, the workflow of learning or solving problems in data science was almost ritualistic. You’d open Google and type something like:
“pandas groupby multiple columns example”.
Then you’d click through several blog posts, skim through code snippets, copy one that looked promising, adapt it to your dataset, run it, hit an error, and repeat the search for the next issue. Five tabs open. Ten tabs open. Stack Overflow threads, Medium articles, documentation pages — a whole ecosystem of people sharing little solutions.
And that wasn’t long ago. Just a couple of years, really.
Now, that entire process feels… oddly outdated.
Today you can simply open an AI tool and type the same question as if you were asking a friend:
“How do I group a pandas dataframe by two columns and calculate the mean?”
Within seconds you get a clean explanation and working code. And if you want to refine it, you just continue the conversation:
“Now handle missing values.”
That’s it. No ten tabs. No hunting through blogs. No piecing together fragments of solutions. Conversational problem solving has quietly replaced search-driven problem solving.
So naturally, a question started creeping into my mind as I sat down to write this: Is there even a point in writing these kinds of articles anymore?
And then I realized something else. What I’m writing right now almost sounds like a rant. And that made me pause for a moment. Because when was the last time an AI truly ranted about something? AI can explain things. It can summarize knowledge. It can even generate tutorials faster than any of us could write them. But it doesn’t really experience the shift. It doesn’t sit there wondering if the thing it started building a couple of years ago suddenly feels obsolete.
Maybe that’s the value I can bring here. Not just instructions, but the human side of working in data science during a time when the field is changing incredibly fast.
How about shifting the focus a bit — exploring the experience of working with data, not just for data scientists, but also ML engineers, data analysts, and anyone in the broader world of data? Things like what a first job feels like, reflections a few years into a career, thoughts on what comes next in a rapidly changing industry, lessons learned from real projects, or small victories and frustrations that don’t fit neatly into a code snippet.
And hopefully this space won’t just be my perspective. It would be far more interesting to hear from other data scientists, engineers, and professionals about their journeys as well.
The tools we use are changing quickly. The way we learn is changing. Even the way we search for answers is evolving.
But the human stories behind the work — the mistakes, the doubts, the small wins, the career pivots — those are still worth sharing.
Maybe now more than ever.
Exploring Data Science 