We are updating the requirements for Community registration. As of 7/21/21 all users will be required to register a phone number with their My Alteryx accounts. If you have already registered, you will be prompted on your next login to add your phone number.

Data Science

Machine learning & data science for beginners and experts alike.
SusanCS
Alteryx Community Team
Alteryx Community Team

Is machine learning the daily focus of data scientists' work? Can data scientists magically enhance low-quality data? 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?"

 

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. 

 

 

 

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.

 

 

 

 

 

Robbie Booth, Senior Director, Cognitive AI Engines, Veritone

 

For me, it would be that data science still is not magic.

 

Different companies have different levels of maturity. We'll talk to a company and they'll have audio that's recorded at an extremely low bit rate with a ton of background noise. You can barely understand what somebody's saying. And there's an expectation that somehow you would get accurate transcription from that. There's certain things you can do, obviously; you can run a bunch of noise reduction. You can try and pop the signal a little bit. But at the end of the day, it's an algorithm somebody wrote, operationalized, turned into a model, trained  —  and so while you're looking for signal in that noise, your noise can't be garbage. I would say a good percentage of the time that there's just a bunch of corrupt stuff, and there's very little you can do with that. I think dealing with those types of data sources is going to be a real thing —  like grainy video feeds. That's going to be a reality for quite some time to come.

 

 

 

 

 

John K. Thompson, global head of advanced analytics and artificial intelligence at CSL Behring

 

Data scientists have no sense of humor.

 

People often think that data scientists have no sense of humor, that they're all very straight-laced and very nerdy, and that's not really true. I've hung out with some data scientists that are pretty funny and kind of a little out there in their behavior. So the stereotypes are beginning to break down.

 

 

 

 

 

These responses have been lightly edited for length and clarity.

 

Read our first roundup of Alternative Hypotheses on Towards Data Science

 

What's your Alternative Hypothesis about doing data science or being a data scientist? Share it in the comments below, and subscribe to the blog to get future articles. Be sure to check out the Data Science Mixer podcast, too, and join us for our special video interviews with Alberto Cairo and Renee Teate at our virtual Inspire conference!

 

 

Blog teaser image by Deva Darshan on Unsplash.

Susan Currie Sivek
Senior Data Science Journalist

Susan Currie Sivek, Ph.D., is the data science journalist for the Alteryx Community. She explores data science concepts with a global audience through blog posts and the Data Science Mixer podcast. Her background in academia and social science informs her approach to investigating data and communicating complex ideas — with a dash of creativity from her training in journalism. Susan also loves getting outdoors with her dog and relaxing with some good science fiction.

Susan Currie Sivek, Ph.D., is the data science journalist for the Alteryx Community. She explores data science concepts with a global audience through blog posts and the Data Science Mixer podcast. Her background in academia and social science informs her approach to investigating data and communicating complex ideas — with a dash of creativity from her training in journalism. Susan also loves getting outdoors with her dog and relaxing with some good science fiction.

Comments
mceleavey
16 - Nebula

"You can help me win the lottery."

 

No. No we can't.

SusanCS
Alteryx Community Team
Alteryx Community Team

giphy-downsized

Such a bummer, @mceleavey😂