Being able to build a machine learning model typically takes most data scientists years of study and an advanced degree. As opposed to being able to string a coherent sentence together, which most of us could manage before we reached age 10.
So, it's easy to see why data scientists often think that model building is the "rocket science" part of their job, while communication is the "easy" bit.
Yet, if communicating the outcomes of technical analysis is so easy, why do so many data scientists struggle to do it right?
The truth is, explaining technical concepts to non-technical stakeholders is hard and, as a result, is painful for many data scientists to do. But without being able to communicate the benefits of your work, it's unlikely your machine learning model is ever going to succeed.
If you can master the maths necessary to fit ML models, you certainly have it
in you to learn how to explain them, too.
After all, it's not exactly rocket science.
Talk again soon,
Dr Genevieve Hayes.
p.s. This post was inspired by a conversation I recently had with Dr Eric Siegel, author of The AI Playbook. You can listen to our entire conversation here: Episode 48: Overcoming the Machine Learning Deployment
Challenge
p.p.s. Thanks to friend-of-the-list James Turner for first introducing me to the YouTube video shared above through his excellent daily emails.
