The power of correctly framing your data science results...
Let's say you create a model that predicts sales leads with 3% accuracy, while only 1% of the population are true leads.
1. Your model is wrong 97% percent of the time. (Ouch.)
2. Your model is right 300% as often as a baseline approach. (Nice.)
Which one sounds better?
They're both saying the exact same thing, but presented in a different way. They have been framed differently.
And people will react to them very differently.
👉 What does this mean for data scientists?
It means that when you have a good result, it is not enough to simply present the numbers. You must frame them appropriately for the audience to understand the value of the work.
When presenting, make sure that you understand the needs and expectations of your audience so that you can communicate in way that presents your results in a favorable light.
✅ Focus on the positive, not the negative.
✅ Focus on improvements, not shortcomings.
✅ Focus on opportunities, not problems.
✅ Focus on what you learned, not where you failed.
#datascience #cognitivebiases #communication
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
Let's say you create a model that predicts sales leads with 3% accuracy, while only 1% of the population are true leads.
1. Your model is wrong 97% percent of the time. (Ouch.)
2. Your model is right 300% as often as a baseline approach. (Nice.)
Which one sounds better?
They're both saying the exact same thing, but presented in a different way. They have been framed differently.
And people will react to them very differently.
👉 What does this mean for data scientists?
It means that when you have a good result, it is not enough to simply present the numbers. You must frame them appropriately for the audience to understand the value of the work.
When presenting, make sure that you understand the needs and expectations of your audience so that you can communicate in way that presents your results in a favorable light.
✅ Focus on the positive, not the negative.
✅ Focus on improvements, not shortcomings.
✅ Focus on opportunities, not problems.
✅ Focus on what you learned, not where you failed.
#datascience #cognitivebiases #communication
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
If you’re learning #datascience or #analytics, then really take the time to understand the art of making the complex simple.
No matter where you go, the data that you use for your:
- visualizations
- machine learning
- statistical analysis
- presentations
All boils down to how well you can communicate the results.
So build the habit on documentation, storytelling, and simplifying your thoughts on papers.
Spend that extra time to articulate your thoughts and think deeply on how you want to present your data.
Because that’s a skill that will always be needed in any place you go.
And not only will you thank yourself for doing this in the future, but your team will love you for making it so simple for them. 🙂
Also, who doesn’t love a simple and meaningful story.
#machinelearning #storytelling #communication
✴️ @AI_Python_EN
No matter where you go, the data that you use for your:
- visualizations
- machine learning
- statistical analysis
- presentations
All boils down to how well you can communicate the results.
So build the habit on documentation, storytelling, and simplifying your thoughts on papers.
Spend that extra time to articulate your thoughts and think deeply on how you want to present your data.
Because that’s a skill that will always be needed in any place you go.
And not only will you thank yourself for doing this in the future, but your team will love you for making it so simple for them. 🙂
Also, who doesn’t love a simple and meaningful story.
#machinelearning #storytelling #communication
✴️ @AI_Python_EN