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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

Chapter 5: Cleaning and Visualizing Data

According to Anaconda's latest State of Data Science Report (https://bit.ly/3F2D8YM), 39% of your time as a data scientist will be spent on either data preparation or cleaning. This might come as no surprise, but being able to set up a problem correctly is vital to being able to get good answers from your data.

Rarely will data come to you in a perfect form, and even then, you might want to manipulate it to answer different questions from it. Being able to quickly find general statistics, discovering and removing bad columns, and altering fields in place will all be needed.

After it's in the right form, visualization is a key tool to be able to not only present your findings to those that might care about it but also as a guide for yourself at this data exploration stage. Cleaning and visualization go hand in hand, and many times you'll see that certain aspects of data need to be adjusted after seeing them. This chapter...

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