The lifecycle of DataScience Project
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The lifecycle of DataScience Project

For years I thought that every Data Science project has 4 stages:

  • Prepare the data,
  • Analyse it,
  • Visualize the insights, and
  • Present your findings.

Without a doubt, those are the four core components without which a project wouldn’t be complete. However, the very important starting step is missing from that list: Identify the question.

[Kirill from SuperDataScience]

 

I’ve suggested this slide from SuperDataScience site cause I’ve spent my first part of life in software farms. To be honest my career was really good there (also salary) cause I was able to follow these steps. But I was in love with Physics from my first meet so when I had chance I was back to research also in a technician role to follow my dreams.

But my first impact was a bit hard, collegues was thinking that ask for software could be solved really fast.

This a terrible mistake in my opinion cause if you don’t spend enough time to project your work when you go in maintenance phase you’ll spend 10 more time to correct or implementing new things.

Of course depends from the project in which you’re involved. If we’re involved in a easy project (I mean few data and lifetime of project) there is no reason to make a complicated project. But if we’re involved in a more complicated one or with a life longer than 6 month take a breath and suggest your solution after analyze all aspects that are involved.

Paola Celio

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