How to cross the “My very first project” bridge

Photo by Fabio Comparelli on Unsplash


I’ve been there (in fact I’m still there), in the skin of a person who tries hard to find her “own problem to solve”. I’ve gone through quite a lot of online tutorials, which we know already doesn’t teach you independence. In fact, I’ve participated in one online course, with a Kaggle challenge in the end. The idea was to compile the experiences and knowledge of what we learned during the course in practice. Although I tried hard to take part in it, I gave up fairly quickly.


You don’t know how to start. You’re not sure what you don’t know, or what you’re supposed to know. Sounds bizarre, but this is how it feels. Every dataset is different, so you can’t apply the same steps you saw on the online course (the one where you tricked yourself into thinking you could code but just followed the instructor). Indeed, heading your very own first project is way more difficult. Nobody can really give you a template on how to proceed.


It would be good to start your analysis with a captivating dataset. You can certainly work with the well-known Iris or Titanic dataset, but what about finding a problem that you REALLY care about? Look at it this way: We are all different. There’s a small chance you will get captivated by the Football dataset (i.e: soccer) as quickly as your male-colleagues. Particularly, if you are a big fan of movies, then you should definitely explore the movies dataset.

Photo by Artem Beliaikin on Unsplash

Dataset is ready, what’s next?

Ask yourself: Do you really understand this dataset? What are the potential problems out there? How can you address them? Do you like your dataset well enough to commit to accomplish your task? Ask questions, think about them out loud, consult them, write them down. Understand your data.


I cannot argue with the idea of getting your hands dirty and starting your own project to learn things faster. Do it wisely. Don’t get into just any dataset. Especially at the beginning of the journey, your dataset must be fascinating, compelling, and captivating for you, especially FOR YOU.

“It’s not the Destination, It’s the journey.” ― Ralph Waldo Emerson

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