Practical data science cookbook : practical recipes on data pre-processing, analysis and visualization using R and Python / Prabhanjan Tattar [and four others].

Format:

Book

Author/Creator:

Tattar, Prabhanjan, author.

Language:

English

Subjects (All):

Python (Computer program language)--Textbooks.

Python (Computer program language).

Physical Description:

1 online resource (407 pages) : illustrations

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing, 2017.

Contents:

Cover

Copyright

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: 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

See also

Installing libraries in R and RStudio

Getting ready

There's more...

Installing Python on Linux and Mac OS X

Installing Python on Windows

Installing the Python data stack on Mac OS X and Linux

Installing extra Python packages

Installing and using virtualenv

Chapter 2: Driving Visual Analysis with Automobile Data with R

Introduction

Acquiring automobile fuel efficiency data

Preparing R for your first project

Importing automobile fuel efficiency data into R

Exploring and describing fuel efficiency data

Analyzing automobile fuel efficiency over time

Investigating the makes and models of automobiles

How it works.

There's more...

Chapter 3: Creating Application-Oriented Analyses Using Tax Data and Python

An introduction to application-oriented approaches

Preparing for the analysis of top incomes

Importing and exploring the world's top incomes dataset

Analyzing and visualizing the top income data of the US

Furthering the analysis of the top income groups of the US

Reporting with Jinja2

Repeating the analysis in R

Chapter 4: Modeling Stock Market Data

Requirements

Acquiring stock market data

Summarizing the data

Cleaning and exploring the data

Generating relative valuations

How to do

Screening stocks and analyzing historical prices

Chapter 5: Visually Exploring Employment Data

Preparing for analysis

Importing employment data into R

Exploring the employment data

Obtaining and merging additional data

Adding geographical information.

Getting ready

Extracting state- and county-level wage and employment information

Visualizing geographical distributions of pay

Exploring where the jobs are, by industry

Animating maps for a geospatial time series

There is more...

Benchmarking performance for some common tasks

Chapter 6: Driving Visual Analyses with Automobile Data

Getting started with IPython

Exploring Jupyter Notebook

Preparing to analyze automobile fuel efficiencies

Exploring and describing fuel efficiency data with Python

Analyzing automobile fuel efficiency over time with Python

Investigating the makes and models of automobiles with Python

Chapter 7: Working with Social Graphs

Understanding graphs and networks

Preparing to work with social networks in Python

Importing networks

Exploring subgraphs within a heroic network

Finding strong ties

Finding key players

The betweenness centrality

The closeness centrality

The eigenvector centrality

Deciding on centrality algorithm

Exploring the characteristics of entire networks

Clustering and community detection in social networks

Visualizing graphs

Social networks in R

Chapter 8: Recommending Movies at Scale (Python)

Modeling preference expressions

Understanding the data

Ingesting the movie review data

Finding the highest-scoring movies

Improving the movie-rating system

Measuring the distance between users in the preference space

Computing the correlation between users

Finding the best critic for a user

Predicting movie ratings for users

Collaboratively filtering item by item

Building a non-negative matrix factorization model

Loading the entire dataset into the memory

Dumping the SVD-based model to the disk

Training the SVD-based model

Testing the SVD-based model

Chapter 9: Harvesting and Geolocating Twitter Data (Python)

Creating a Twitter application

Understanding the Twitter API v1.1

Determining your Twitter followers and friends

Pulling Twitter user profiles

Making requests without running afoul of Twitter's rate limits

Storing JSON data to disk

Setting up MongoDB for storing Twitter data

Storing user profiles in MongoDB using PyMongo

Exploring the geographic information available in profiles

Plotting geospatial data in Python

See also.

Chapter 10: Forecasting New Zealand Overseas Visitors.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed July 31, 2017).

ISBN:

1-78712-326-X