You likely know that Python is one of the fastest growing languages for data science.

This is a discipline that combines the scientific inquiry of hypothesis and tests,

the mathematical intuition of probability and statistics, the AI foundations of machine learning,

a fluency in big data processing, and the Python language itself.

That's a very broad set of skills you'll need to be a good data scientist,

and yet each one is deep and often hard to understand.

That's why I'm excited to speak with Joel Gruse, a data scientist from Seattle.

He wrote a book to help us all understand what's actually happening when we employ libraries such as Psykit Learn or Numpi.

It's called Data Science from Scratch, and that's the topic of this week's episode.

This is Talk Python

to me, episode number 56 recorded April 19, 2016. I'm a developer, developers, developer. I'm a developer

in many senses of the word, because I make these applications, but I also use these verbs

to make this music. I constructed line by line,

just like when I'm coding another software design. In both cases, it's about design patterns.

Anyone can get the job done. It's the execution that matters. I have many interests.

Welcome to Talk Python to Me, a weekly podcast on Python, the language, the libraries, the ecosystem, and the personalities.

This is your host, Michael Kennedy. Follow me on Twitter where I'm at M. Kennedy.

Keep up with the show and listen to past episodes at talk python.fm.

And follow the show on Twitter via at talk python.

This episode is brought to you by SnapCi and Hired. Think them for supporting the show on Twitter via

at Snap underscore CI and at Hired underscore H.K. Hey everyone, it's great to be back with you. I have something

to share before we get to the main part of the show. I had a chance to be on the partially derivative

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