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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

Cleaning data with pandas

One of the most important aspects that come into play when working with data is ensuring that it's in the correct format that you need. Along with getting enough data, this might be the most vital component to training an accurate model. In this section, we're going to walk through the steps of importing a CSV file and then seeing how to analyze and clean it to make sure that it's prepped for us.

The example that we are going to look at is the data for various US university majors and how it relates to pay. Having a general sense of the domain we are looking into is critical, and this is an area that you might already have a grasp of. This dataset is provided by the excellent FiveThirtyEight site, and more information can be found here: https://github.com/fivethirtyeight/data/tree/master/college-majors.

Our goal is to see whether we can figure out whether we should have chosen another major using this data. We might even find out that...

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