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Super Data Science: ML & AI Podcast with Jon KrohnEpisode from the podcastSuper Data Science: ML & AI Podcast with Jon Krohn
253: Solving Problems With Data Science & Uber

253: Solving Problems With Data Science & Uber

Released Wednesday, 17th April 2019
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253: Solving Problems With Data Science & Uber

253: Solving Problems With Data Science & Uber

253: Solving Problems With Data Science & Uber

253: Solving Problems With Data Science & Uber

Wednesday, 17th April 2019
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In this episode of the SuperDataScience Podcast, I chat with Associate Professor at the University of California San Diego, Bradley Voytek, who was the first data scientist, and the person to kickstart data science at Uber. You will hear a lot about his past work at Uber and his current work at UCSD. You will learn 4 valuable philosophical points about data science being a separate field and you will also learn what data science skills can help you resist automation.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/253

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Super Data Science: ML & AI Podcast with Jon Krohn

1330
The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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