May 2018
Beginner to intermediate
364 pages
7h 43m
English
Content preview from Hands-On Data Science with Anaconda
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Reasons for using Jupyter via Anaconda
In data science or data analytics, we usually work in a team. This means that each developer, researcher, or team member, might have his/her favorite programming language, such as Python, R, Octave, or Julia. If we could have a platform to run all of those languages, it would be great. Fortunately, Jupyter is such a platform, since this platform can accommodate over 40 languages, including Python, R, Julia, Octave, and Scala.
In Chapter 2, Anaconda Installation, we will show you how to run those four languages via Jupyter. Of course, there are other benefits of using Anaconda: we might worry less about the dependency of installed packages, manage packages more efficiently, and share our programs, projects, ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.