Applied Data Science with Python and Jupyter / Galea, Alex.

Format:

Book

Author/Creator:

Galea, Alex, author.

Language:

English

Subjects (All):

Machine learning.

Information visualization.

Electronic data processing.

Python (Computer program language).

Physical Description:

1 online resource (192 pages)

Edition:

1st edition

Place of Publication:

Packt Publishing, 2018.

System Details:

text file

Summary:

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learn Get up and running with the Jupyter ecosystem Identify potential areas of investigation and perform exploratory data analysis Plan a machine learning classification strategy and train classification models Use validation curves and dimensionality reduction to tune and enhance your models Scrape tabular data from web pages and transform it into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a 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 such as Pandas, Matplotlib, and Pandas providing you a useful head start.

Contents:

Intro

Preface

Jupyter Fundamentals

Introduction

Basic Functionality and Features

What is a Jupyter Notebook and Why is it Useful?

Navigating the Platform

Exercise 1: Introducing Jupyter Notebooks

Jupyter Features

Exercise 2: Implementing Jupyter's Most Useful Features

Converting a Jupyter Notebook to a Python Script

Python Libraries

Exercise 3: Importing the External Libraries and Setting Up the Plotting Environment

Our First Analysis - The Boston Housing Dataset

Loading the Data into Jupyter Using a Pandas DataFrame

Exercise 4: Loading the Boston Housing Dataset

Data Exploration

Exercise 5: Analyzing the Boston Housing Dataset

Introduction to Predictive Analytics with Jupyter Notebooks

Exercise 6: Applying Linear Models With Seaborn and Scikit-learn

Activity 1: Building a Third-Order Polynomial Model

Using Categorical Features for Segmentation Analysis

Exercise 7: Creating Categorical Fields From Continuous Variables and Make Segmented Visualizations

Summary

Data Cleaning and Advanced Machine Learning

Preparing to Train a Predictive Model

Determining a Plan for Predictive Analytics

Exercise 8: Explore Data Preprocessing Tools and Methods

Activity 2: Preparing to Train a Predictive Model for the Employee-Retention Problem

Training Classification Models

Introduction to Classification Algorithms

Exercise 9: Training Two-Feature Classification Models With Scikit-learn

The plot_decision_regions Function

Exercise 10: Training K-nearest Neighbors for Our Model

Exercise 11: Training a Random Forest

Assessing Models With K-fold Cross-Validation and Validation Curves

Exercise 12: Using K-fold Cross Validation and Validation Curves in Python With Scikit-learn

Dimensionality Reduction Techniques.

Exercise 13: Training a Predictive Model for the Employee Retention Problem

Web Scraping and Interactive Visualizations

Scraping Web Page Data

Introduction to HTTP Requests

Making HTTP Requests in the Jupyter Notebook

Exercise 14: Handling HTTP Requests With Python in a Jupyter Notebook

Parsing HTML in the Jupyter Notebook

Exercise 15: Parsing HTML With Python in a Jupyter Notebook

Activity 3: Web Scraping With Jupyter Notebooks

Interactive Visualizations

Building a DataFrame to Store and Organize Data

Exercise 16: Building and Merging Pandas DataFrames

Introduction to Bokeh

Exercise 17: Introduction to Interactive Visualization With Bokeh

Activity 4: Exploring Data with Interactive Visualizations

Appendix A

Index

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Notes:

Online resource; Title from title page (viewed October 31, 2018)

Description based on publisher supplied metadata and other sources.

ISBN:

9781789951929

OCLC:

1104662251