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Practical Data Science Cookbook - Second Edition电子书

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作       者:Prabhanjan Tattar,Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta

出  版  社:Packt Publishing

出版时间:2017-07-07

字       数:50.3万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Over 85 recipes to help you complete real-world data science projects in R and Python About This Book ? Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data ? Get beyond the theory and implement real-world projects in data science using R and Python ? Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn ? Learn and understand the installation procedure and environment required for R and Python on various platforms ? Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python ? Build a predictive model and an exploratory model ? Analyze the results of your model and create reports on the acquired data ? Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
目录展开

Title Page

Copyright

Practical Data Science Cookbook

Second Edition

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

Preparing Your Data Science Environment

Understanding the data science pipeline

How to do it...

How it works...

Installing R on Windows, Mac OS X, and Linux

How to do it...

How it works...

See also

Installing libraries in R and RStudio

Getting ready

How to do it...

How it works...

There's more...

See also

Installing Python on Linux and Mac OS X

Getting ready

How to do it...

How it works...

See also

Installing Python on Windows

How to do it...

How it works...

See also

Installing the Python data stack on Mac OS X and Linux

Getting ready

How to do it...

How it works...

There's more...

See also

Installing extra Python packages

Getting ready

How to do it...

How it works...

There's more...

See also

Installing and using virtualenv

Getting ready

How to do it...

How it works...

There's more...

See also

Driving Visual Analysis with Automobile Data with R

Introduction

Acquiring automobile fuel efficiency data

Getting ready

How to do it...

How it works...

Preparing R for your first project

Getting ready

How to do it...

There's more...

See also

Importing automobile fuel efficiency data into R

Getting ready

How to do it...

How it works...

There's more...

See also

Exploring and describing fuel efficiency data

Getting ready

How to do it...

How it works...

There's more...

Analyzing automobile fuel efficiency over time

Getting ready

How to do it...

How it works...

There's more...

See also

Investigating the makes and models of automobiles

Getting ready

How to do it...

How it works...

There's more...

See also

Creating Application-Oriented Analyses Using Tax Data and Python

Introduction

An introduction to application-oriented approaches

Preparing for the analysis of top incomes

Getting ready

How to do it...

How it works...

Importing and exploring the world's top incomes dataset

Getting ready

How to do it...

How it works...

There's more...

See also

Analyzing and visualizing the top income data of the US

Getting ready

How to do it...

How it works...

Furthering the analysis of the top income groups of the US

Getting ready

How to do it...

How it works...

Reporting with Jinja2

Getting ready

How to do it...

How it works...

There's more...

See also

Repeating the analysis in R

Getting ready

How to do it...

There's more...

Modeling Stock Market Data

Introduction

Requirements

Acquiring stock market data

How to do it...

Summarizing the data

Getting ready

How to do it...

How it works...

There's more...

Cleaning and exploring the data

Getting ready

How to do it...

How it works...

See also

Generating relative valuations

Getting ready

How to do

How it works...

Screening stocks and analyzing historical prices

Getting ready

How to do it...

How it works...

Visually Exploring Employment Data

Introduction

Preparing for analysis

Getting ready

How to do it...

How it works...

See also

Importing employment data into R

Getting ready

How to do it...

How it works...

There's more...

See also

Exploring the employment data

Getting ready

How to do it...

How it works...

See also

Obtaining and merging additional data

Getting ready

How to do it...

How it works...

Adding geographical information

Getting ready

How to do it...

How it works...

See also

Extracting state- and county-level wage and employment information

Getting ready

How to do it...

How it works...

See also

Visualizing geographical distributions of pay

Getting ready

How to do it...

How it works...

See also

Exploring where the jobs are, by industry

How to do it...

How it works...

There's more...

See also

Animating maps for a geospatial time series

Getting ready

How to do it...

How it works...

There is more...

Benchmarking performance for some common tasks

Getting ready

How to do it...

How it works...

There's more...

See also

Driving Visual Analyses with Automobile Data

Introduction

Getting started with IPython

Getting ready

How to do it...

How it works...

See also

Exploring Jupyter Notebook

Getting ready

How to do it...

How it works...

There's more...

See also

Preparing to analyze automobile fuel efficiencies

Getting ready

How to do it...

How it works...

There's more...

See also

Exploring and describing fuel efficiency data with Python

Getting ready

How to do it...

How it works...

There's more...

See also

Analyzing automobile fuel efficiency over time with Python

Getting ready

How to do it...

How it works...

There's more...

See also

Investigating the makes and models of automobiles with Python

Getting ready

How to do it...

How it works...

See also

Working with Social Graphs

Introduction

Understanding graphs and networks

Preparing to work with social networks in Python

Getting ready

How to do it...

How it works...

There's more...

Importing networks

Getting ready

How to do it...

How it works...

Exploring subgraphs within a heroic network

Getting ready

How to do it...

How it works...

There's more...

Finding strong ties

Getting ready

How to do it...

How it works...

There's more...

Finding key players

Getting ready

How to do it...

How it works...

There's more...

The betweenness centrality

The closeness centrality

The eigenvector centrality

Deciding on centrality algorithm

Exploring the characteristics of entire networks

Getting ready

How to do it...

How it works...

Clustering and community detection in social networks

Getting ready

How to do it...

How it works...

There's more...

Visualizing graphs

Getting ready

How to do it...

How it works...

Social networks in R

Getting ready

How to do it...

How it works...

Recommending Movies at Scale (Python)

Introduction

Modeling preference expressions

How to do it...

How it works...

Understanding the data

Getting ready

How to do it...

How it works...

There's more...

Ingesting the movie review data

Getting ready

How to do it...

How it works...

Finding the highest-scoring movies

Getting ready

How to do it...

How it works...

There's more...

See also

Improving the movie-rating system

Getting ready

How to do it...

How it works...

There's more...

See also

Measuring the distance between users in the preference space

Getting ready

How to do it...

How it works...

There's more...

See also

Computing the correlation between users

Getting ready

How to do it...

How it works...

There's more...

Finding the best critic for a user

Getting ready

How to do it...

How it works...

Predicting movie ratings for users

Getting ready

How to do it...

How it works...

Collaboratively filtering item by item

Getting ready

How to do it...

How it works...

Building a non-negative matrix factorization model

How to do it...

How it works...

See also

Loading the entire dataset into the memory

Getting ready

How to do it...

How it works...

There's more...

Dumping the SVD-based model to the disk

How to do it...

How it works...

Training the SVD-based model

How to do it...

How it works...

There's more...

Testing the SVD-based model

How to do it...

How it works...

There's more...

Harvesting and Geolocating Twitter Data (Python)

Introduction

Creating a Twitter application

Getting ready

How to do it...

How it works...

See also

Understanding the Twitter API v1.1

Getting ready

How to do it...

How it works...

There's more...

See also

Determining your Twitter followers and friends

Getting ready

How to do it...

How it works...

There's more...

See also

Pulling Twitter user profiles

Getting ready

How to do it...

How it works...

There's more...

See also

Making requests without running afoul of Twitter's rate limits

Getting ready

How to do it...

How it works...

Storing JSON data to disk

Getting ready

How to do it...

How it works...

Setting up MongoDB for storing Twitter data

Getting ready

How to do it...

How it works...

There's more...

See also

Storing user profiles in MongoDB using PyMongo

Getting ready

How to do it...

How it works...

Exploring the geographic information available in profiles

Getting ready

How to do it...

How it works...

There's more...

See also

Plotting geospatial data in Python

Getting ready

How to do it...

How it works...

There's more...

See also

Forecasting New Zealand Overseas Visitors

Introduction

The ts object

Getting ready

How to do it

How it works...

Visualizing time series data

Getting ready

How to do it...

How it works...

Simple linear regression models

Getting ready

How to do it...

How it works...

See also

ACF and PACF

Getting ready

How to do it...

How it works...

ARIMA models

Getting ready

How to do it...

How it works...

Accuracy measurements

Getting ready

How to do it...

How it works...

Fitting seasonal ARIMA models

Getting ready

How to do it...

How it works...

There's more...

German Credit Data Analysis

Introduction

Simple data transformations

Getting ready

How to do it...

How it works...

There's more...

Visualizing categorical data

Getting ready

How to do it...

How it works...

Discriminant analysis

Getting ready

How to do it...

How it works...

See also

Dividing the data and the ROC

Getting ready

How to do it...

Fitting the logistic regression model

Getting ready

How to do it...

How it works...

See also

Decision trees and rules

Getting ready

How to do it...

How it works...

See also

Decision tree for german data

Getting ready

How to do it ...

How it works...

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