6 Data Science Mistakes That Made Me Look Stupid (So You Don’t Have To)
Data analysis can be rewarding, especially when your analysis produces expected results and is used for strategic actions.
But it can also be humbling.
Over the years, I’ve made mistakes that slowed me down and left me with my fair share of “facepalm” moments.
Mistakes that made me cringe and go, “Whaaaat?”
If you’re an experienced analyst, you can relate to these.
The good thing is these blunders became lessons that shaped my approach to analysis.
In this article, I’m sharing the top five data analysis mistakes that made me look less like a data professional and more like an amateur.
Hopefully, you can avoid these pitfalls and save yourself the embarrassment.
1. Ignoring Data Context
I have analyzed some datasets a few times without understanding their real-world context. This was common when analyzing sales data obtained from databases or online resources.
I just wanted to have some analysis to build a portfolio and show my capabilities.
What happened? A good look at the analysis interpreted customer churn as seasonal, but my dataset didn’t account for users outside a…
