With a fast-growing field like data science, it is important to keep some amount of skepticism. Tools can be overhyped, buzzwords can be overemphasized, and people can forget the fundamentals.
If you have bad data, you will get bad results in your experimentation. If you don’t know what statistical approach you want to take to your data, it doesn’t matter how well you know Spark or TensorFlow. And if you aren’t passionate about the work you are doing, you are unlikely to finish the projects you start (whether we are talking data science or otherwise).
Kyle Polich hosts the Data Skeptic podcast, a show at the intersection of data science and skepticism. As a podcaster, Kyle takes himself seriously and is prepared for his shows–which I admire. Having met him recently at the Microsoft Build conference, he’s a great guy and I look forward to doing more podcasts with him in the future.
In this episode, Kyle is interviewed by Sid Ramesh, a data engineering correspondent for Software Engineering Daily.
Transcript provided by We Edit Podcasts. Software Engineering Daily listeners can go to weeditpodcasts.com/sed to get 20% off the first two months of audio editing and transcription services. Thanks to We Edit Podcasts for partnering with SE Daily. Please click here to view this show’s transcript.
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