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

Chapter 4: Working with Jupyter Notebooks and NumPy

Data is naturally something that is talked about any time that you hear data science discussed, and this data will rarely be in the exact format you need to create your models. In this chapter, we will learn the core skill of data cleaning using NumPy while working in a Jupyter notebook, two of the foundational tools for any data scientist.

By default, you won't see many of the needed operations for multidimensional arrays included with Python, and that's where NumPy comes in. With it, you can perform linear algebra, perform operations on each element, and do it all quickly, which was a challenge before. These core features are what make this package one of the fundamental tools for scientific computing that many other packages are built upon, including pandas and scikit-learn.

We'll also take a visual approach to this work by getting to know Jupyter notebooks. Jupyter notebooks make it incredibly easy to work...