Tidyverse, ggplot2, and the secret to a tech company’s longevity: Hadley Wickham talks to Jon Krohn about Posit’s rebrand, Tidyverse and why it needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career.

This episode is brought to you by Intel and HPE Ezmeral Software (https://bit.ly/hpeintel). Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• All about the Tidyverse [04:46]
• Hadley’s favorite R libraries [17:10]
• The goal of Posit [30:29]
• On bringing multiple programming languages together [36:02]
• The principles for a long-lasting tech company [52:10]
• How Hadley developed ggplot2 [55:24]
• How to contribute to the open-source community [1:05:43]

Additional materials: www.superdatascience.com/779

The Super Data Science Podcast: Machine Learning | AI | Success

779: The Tidyverse of Essential R Libraries and their Python Analogues, with Dr. Hadley Wickham

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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