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The Applied Data Science Workshop

You're reading from The Applied Data Science Workshop Get started with the applications of data science and techniques to explore and assess data effectively

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781800202504

Length 352 pages

Edition 2nd Edition

Languages

Python

Tools

Jupyter

Concepts

Data Science

Author (1):

Alex Galea

View More author details

Table of Contents (8) Chapters

Preface

Installing Libraries

1. Introduction to Jupyter Notebooks

2. Data Exploration with Jupyter FREE CHAPTER

3. Preparing Data for Predictive Modeling

4. Training Classification Models

5. Model Validation and Optimization

6. Web Scraping with Jupyter Notebooks

Appendix

Our First Analysis – the Boston Housing Dataset

The dataset we'll be looking at in this section is the so-called Boston Housing dataset. It contains US census data concerning houses in various areas around the city of Boston. Each sample corresponds to a unique area and has about a dozen measures. We should think of samples as rows and measures as columns. This data was first published in 1978 and is quite small, containing only about 500 samples.

Now that we know something about the context of the dataset, let's decide on a rough plan for the exploration and analysis stages. If applicable, this plan will accommodate the relevant questions under study. In this case, the goal is not to answer a question, but to show Jupyter in action and illustrate some basic data analysis methods.

Our general approach to this analysis will be to do the following:

Load the data into Jupyter using a pandas DataFrame
Quantitatively understand the features
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The Applied Data Science Workshop - Second Edition

Published in: Jul 2020

Publisher: Packt

ISBN-13: 9781800202504

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Authors (1)

Galea

Galea

Alex Galea has been professionally practicing data analytics since graduating with a masters degree in physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.

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