Data science without data is like gardening without seeds | by Kamel Badar | November 2023

Here is what you should understand about Data Science projects

A picture from Francesco Gallarotti we Unsplash

If you were to build a house, would you start by putting a roof on it or building walls?

This is a question I could ask my 7 year old sister and she would choose the correct answer.

However, people often seem to lack logical thinking about Data Science and have a bad habit of starting from scratch.

Dear readers, take a deep breath and go back to the basics to focus on what is truly valuable, the foundation of every Data Science project: current data.

We are in a time where everything is moving so fast that people are afraid to jump on the bandwagon when a new trend comes along.

This phenomenon is called Fear Of Missing Out (FOMO) and is usually one of the reasons why many individuals or companies lose money.

FOMO is what happens when you read an article about a new $2000 NFT featuring a monkey with a baseball cap.

You’re not sure you want to buy it (mostly because it’s ugly art), but part of you says: if not, I can be sitting on huge benefits.

The same is happening with artificial intelligence.

This buzzword is on everyone’s lips these days: people want to surf the wave because of the big money that is promised to them.

Your CEO reads a few things about it and schedules a meeting with senior executives the next day to tell them the company needs to do AI.

What kind of AI exactly? They do not know. The cool one, the one that makes chatbots answer you when you ask a question.

Maybe I’m stretching the line because not all CEOs and managers are completely ignorant, but sometimes it really does happen.

And wanting to do AI without knowing its benefits is almost always a bad idea.

If you want to initiate AI projects, you should know one very important fact:

Most Data Science projects don’t all work

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