393: The Importance of Keeping Science in Data Science

John Peach joins to discuss his passion for bringing more scientific approaches to the data science field, making it smarter and more efficient.

An episode of the Super Data Science: ML & AI Podcast with Jon Krohn podcast, hosted by Jon Krohn, titled "393: The Importance of Keeping Science in Data Science" was published on August 19, 2020 and runs 68 minutes.

August 19, 2020 ·68m · Super Data Science: ML & AI Podcast with Jon Krohn

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Summary

John Peach joins to discuss his passion for bringing more scientific approaches to the data science field, making it smarter and more efficient. In this episode you will learn: John’s move from Canada to the US [3:37] John’s new position at Oracle [8:31] Data Science Workflows [9:34] John’s solution to data science workflow exploration [12:06] John’s data science design thinking framework [21:20] Case study [34:21] Literate statistical programming [43:12] R or Python? [51:55] Data unit testing [53:28] What drives John? [1:00:56] Additional materials: www.superdatascience.com/393

Episode Description

John Peach joins to discuss his passion for bringing more scientific approaches to the data science field, making it smarter and more efficient. In this episode you will learn: John’s move from Canada to the US [3:37] John’s new position at Oracle [8:31] Data Science Workflows [9:34] John’s solution to data science workflow exploration [12:06] John’s data science design thinking framework [21:20] Case study [34:21] Literate statistical programming [43:12] R or Python? [51:55] Data unit testing [53:28] What drives John? [1:00:56] Additional materials: www.superdatascience.com/393

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