From the course: 15 Mistakes to Avoid in Data Science

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Having a data set that is too small

Having a data set that is too small

From the course: 15 Mistakes to Avoid in Data Science

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Having a data set that is too small

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- My name is Louis Tremblay. I'm a Senior System Engineer and I work at FLIR Systems. A common mistake that I often see is kind of using data that's not ready. And not necessarily just data that's not ready, but not enough data. And that can be a real challenge for a data scientist when you're presented with too small of a dataset or too small of a benchmark, you're going to be spinning your wheels trying to make things work. When in reality, the number one thing that you could do, the number one thing that you can improve your day-to-day efficiency is actually just spending the time to go get more data. So I actually have an example in my job where our team was working on a self-driving car dataset. And we started off with a couple hundred video clips, but ultimately, it wasn't enough. We were spending so much time trying to improve it. And the way I solved it, I just grabbed a camera and went driving for several hours.…

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