Remember this?
It’s a Pokedex and if you’re wondering, yes there will be many recurring references to our Pokepals along this journey. A Pokedex is basically your phone except its only ability is Googling Pokemon. Every hero has one. Not a Pokedex, but some sort of “know it all” sidekick. A person or device that stays by your side during the journey and coincidentally has answers to the most arbitrary questions and solutions to the most arduous problems you encounter. Luke had R2D2, Mr. Burns had Smithers, and even Quailman had someone to rely on.
And they all performed excellently with just enough face time to intrigue the audience, but not enough to take attention away from our hero or the journey because let’s face it….
This is why I’ve developed Datadex. What’s the Datadex, Nick? Great question fictitious audience I’m glad you asked. The Datadex takes in RDQs, Random Data Science Questions, and spits out answers. So, I basically find the answer to the random questions about Data Science that pop in and out of my head throughout the day and post the answers here in short but very informative (hopefully) articles.
This will accomplish two things. The first being a filler. The documentation will help slowly fill in the copious gaps in knowledge that I or you, my beloved fictitious audience, may have and the second being a filler (again). While you’re waiting for the next episode of “The Road to Kaggle Grandmaster” these RDQs will keep you updated on jargon, techniques, and anything useful we may find along the way.
So, with just enough face time to intrigue the audience, but not enough to take attention away from our hero (me), the Datadex will be a perfect addition on the road to Kaggle Grandmaster.
“Nick, I still don’t get it.” Alright, let’s go through a couple to get the ball rolling.
Random Data Science Questions (RDQs):
- How do I explain Data Science to my ugly little cousin?
“Imagine that you keep losing to some boss character on a video game and no matter how hard you try you can’t seem to win. You’ve noticed there’s some sort of pattern to the way they move and the way they attack, but you don’t have enough information to win just yet so you take to the internets. You search through forums and blogs for more information (data gathering).
Soon you have enough tips and tricks to fight the boss again, but this time you have much more information than before. This time you can use what you’ve learned to more accurately anticipate (prediction) the behavior of this nuisance. In this example little ugly, you are what we call a model. We filled you with a ton of data (tips and tricks from forums + what you learned via experience) in hopes that you will be able to predict the boss’s behavior and overcome the problem.
This is, loosely put, Data Science. You found a problem, collected a bunch of data, processed it, and then used it to build a solution to your problem.
Here’s a short 5 minute video incase my explanation was too abstract.
That’s Data Science….sort of.
Stay tuned for the next episode of “The Road to Kaggle Grandmaster.”
