Beginning Data Science with Python and Jupyter

Use powerful industry-standard tools to unlock new, actionable insight from your existing data

Alex Galea

• Editorial: Packt Publishing
• Año: 2018
• ISBN:
  9781789534658
• Edición: 1
• Categoría: Ciencia de la computación
• Categoría: Tratamiento de datos

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.

Key Features

• Get up and running with the Jupyter ecosystem and some example datasets
• Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests
• Discover how you can use web scraping to gather and parse your own bespoke datasets

Book Description

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.

What you will learn

• Get up and running with the Jupyter ecosystem and some example datasets
• Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests
• Plan a machine learning classification strategy and train classification, models
• Use validation curves and dimensionality reduction to tune and enhance your models
• Discover how you can use web scraping to gather and parse your own bespoke datasets
• Scrape tabular data from web pages and transform them into Pandas DataFrames
• Create interactive, web-friendly visualizations to clearly communicate your findings

Who this book is for

This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.

Beginning Data Analysis with Python and Jupyter

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Table of Contents

Beginning Data Analysis with Python and Jupyter

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Contributors

About the author

About the reviewer

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Preface

What This Book Covers

What You Need for This Book

Installation and Setup

Installing Anaconda

Updating Jupyter and Installing Dependencies

Who This Book is for

Conventions

Reader Feedback

Customer Support

Downloading the Example Code

Errata

Piracy

Questions

1. Jupyter Fundamentals

Lesson Objectives

Basic Functionality and Features

Subtopic A: What is a Jupyter Notebook and Why is it Useful?

Subtopic B: Navigating the Platform

Introducing Jupyter Notebooks

Subtopic C: Jupyter Features

Explore some of Jupyter's most useful features

Converting a Jupyter Notebook to a Python Script

Subtopic D: Python Libraries

Import the external libraries and set up the plotting environment

Our First Analysis - The Boston Housing Dataset

Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame

Load the Boston housing dataset

Subtopic B: Data Exploration

Explore the Boston housing dataset

Subtopic C: Introduction to Predictive Analytics with Jupyter Notebooks

Linear models with Seaborn and scikit-learn

Activity B: Building a Third-Order Polynomial Model

Subtopic D: Using Categorical Features for Segmentation Analysis

Create categorical fields from continuous variables and make segmented visualizations

Summary

2. Data Cleaning and Advanced Machine Learning

Preparing to Train a Predictive Model

Subtopic A: Determining a Plan for Predictive Analytics

Subtopic B: Preprocessing Data for Machine Learning

Explore data preprocessing tools and methods

Activity A: Preparing to Train a Predictive Model for the Employee-Retention Problem

Training Classification Models

Subtopic A: Introduction to Classification Algorithms

Training two-feature classification models with scikit-learn

The plot_decision_regions Function

Training k-nearest neighbors for our model

Training a Random Forest

Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves

Using k-fold cross validation and validation curves in Python with scikit-learn

Subtopic C: Dimensionality Reduction Techniques

Training a predictive model for the employee retention problem

Summary

3. Web Scraping and Interactive Visualizations

Lesson Objectives

Scraping Web Page Data

Subtopic A: Introduction to HTTP Requests

Subtopic B: Making HTTP Requests in the Jupyter Notebook

Handling HTTP requests with Python in a Jupyter Notebook

Subtopic C: Parsing HTML in the Jupyter Notebook

Parsing HTML with Python in a Jupyter Notebook

Activity A: Web Scraping with Jupyter Notebooks

Interactive Visualizations

Subtopic A: Building a DataFrame to Store and Organize Data

Building and merging Pandas DataFrames

Subtopic B: Introduction to Bokeh

Introduction to interactive visualizations with Bokeh

Activity B: Exploring Data with Interactive Visualizations

Summary

Index

Beginning Data Analysis with Python and Jupyter

Copyright © 2018 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Acquisition Editor: Aditya Date

Content Development Editor: Murtaza Haamid

Production Coordinator: Vishal Pawar

First published: May 2018

Production reference: 1310518

Published by Packt Publishing Ltd.

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ISBN 978-1-78953-202-9

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Contributors

About the author

Alex Galea has been professionally practicing data analytics since graduating with a Master's 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.

About the reviewer

Elie Kawerk likes to solve problems using the analytical skills he has accumulated over the years. He uses the data science process, including statistical methods and machine learning, to extract insights from data and get value out of it.

His formal training is in computational physics. He used to simulate atomic and molecular physi...