5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022
A curated list of data science projects that mimic real-life problems
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“Say goodbye to useless side-projects.”
It’s been almost two years since I started writing articles — that’s equated to just over 175 articles! One fault in some of my previous articles is that I suggested data science projects that were interesting, but not practical.
One of the easiest ways to get a job as a data scientist is to show that you’ve already completed similar projects and work as the job posting itself. Therefore, I wanted to share with you some practical data science projects that I’ve personally done throughout my career that will beef up your experience and your portfolio.
With that said, let’s dive into it:
1. Customer Propensity Modelling
What?
A propensity model is a model that predicts the likelihood that someone will do something. To give a few examples:
- The likelihood that website visitors will register an account
- The likelihood that a registered user will pay and subscribe
- The likelihood that a user will refer another user
As well, propensity modeling does not only entail “who” and “what” — it also entails “when” (when should you target the users you’ve identified) and “how” (how should you deliver your message to your targeted users?).
Why?
Propensity modeling allows you to allocate your resources more wisely, resulting in greater efficiencies, while achieving better results. To give an example, think of this: instead of sending an email advertisement where there’s a 0%-100% chance of a user clicking it, with propensity modeling, you can target users with a 50%+ chance of clicking it. Fewer emails, more conversions!
