Is machine learning the actual focus of data scientists’ everyday work? Do you need to learn all the things to be a data scientist? And, most importantly: Do data scientists have a sense of humor?

On the Data Science Mixer podcast, I always ask our expert guests the same question in our “Alternative Hypothesis” segment: “What’s one thing that people think is true about data science or about being a data scientist that you have found to be incorrect?” (Be sure to check out the first roundup of debunked myths, too.)

Amazingly, we always get a fresh response. It seems there are enough myths about data science out there that there’s always something new for our guests to highlight.

Check out these responses from some of our recent episodes. It’s fascinating to see what these experts highlight from their experiences in data science.

Danielle Lyles, Ph.D., data and evaluation scientist, University of Colorado Boulder

The machine learning part, when you understand it, is actually very easy and very fast.

One thing that people often think is true is that if you’re a data scientist, you’re just doing machine learning all the time, and that’s it. And I’ll say that I’ve done a ton of exploratory data analysis, and I also did quality analysis on models built by outside companies.

The machine learning part, when you understand it, is actually very easy and very fast. The hard part is understanding and learning the data, as my boss says … sometimes called cleaning the data! But it’s really about understanding it and learning it — and you end up also cleaning it up while you’re in there.

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