Book Image

Building Data Science Solutions with Anaconda

By : Dan Meador
5 (1)
Book Image

Building Data Science Solutions with Anaconda

5 (1)
By: Dan 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)
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

Evaluating a new tool or library

The only constant is change, and there is no doubt as I type this, a new tool that "fixes" all the things that are broken with framework X but is simpler to use is being developed. This section will help you navigate the new world where a constant stream of new software is available for free. You will learn what attributes and factors to look at to decide whether something is worth using or not.

There are a few heuristics that you could use when evaluating a new tool. Feel free to adjust which ones you use based on your specific needs:

  • The number of stars the tool has on GitHub
  • The tool's age
  • How long it's been since the tool has been updated
  • The number of maintainers
  • The number of open issues/PRs
  • The number of dependencies

I want to add a big asterisk to all of these. The answer to how important each of these are is the same as the answer to which architecture style is right for your code base...