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Practical Data Science Cookbook, Second Edition - Second Edition

By : Tattar, Bhushan Purushottam Joshi, Sean P Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

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Practical Data Science Cookbook, Second Edition

By: Tattar, Bhushan Purushottam Joshi, Sean P Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Buy this Book

Overview of this book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

Preparing Your Data Science Environment

Understanding the data science pipeline

Installing R on Windows, Mac OS X, and Linux

Installing libraries in R and RStudio

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

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

Creating Application-Oriented Analyses Using Tax Data and Python

Introduction

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

Modeling Stock Market Data

Introduction

Acquiring stock market data

Summarizing the data

Cleaning and exploring the data

Generating relative valuations

Screening stocks and analyzing historical prices

Visually Exploring Employment Data

Introduction

Preparing for analysis

Importing employment data into R

Exploring the employment data

Obtaining and merging additional data

Adding geographical information

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

Benchmarking performance for some common tasks

Driving Visual Analyses with Automobile Data

Introduction

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

Working with Social Graphs

Introduction

Preparing to work with social networks in Python

Importing networks

Exploring subgraphs within a heroic network

Finding strong ties

Finding key players

Exploring the characteristics of entire networks

Clustering and community detection in social networks

Visualizing graphs

Social networks in R

Recommending Movies at Scale (Python)

Introduction

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

Harvesting and Geolocating Twitter Data (Python)

Introduction

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

Forecasting New Zealand Overseas Visitors

Introduction

The ts object

Visualizing time series data

Simple linear regression models

ACF and PACF

ARIMA models

Accuracy measurements

Fitting seasonal ARIMA models

German Credit Data Analysis

Introduction

Simple data transformations

Visualizing categorical data

Discriminant analysis

Dividing the data and the ROC

Fitting the logistic regression model

Decision trees and rules

Decision tree for german data

Harvesting and Geolocating Twitter Data (Python)

In this chapter, we will cover the following recipes:

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 the 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

Tech Concepts

Programming languages

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Practical Data Science Cookbook, Second Edition

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Practical Data Science Cookbook, Second Edition - Second Edition

By : Tattar, Bhushan Purushottam Joshi, Sean P Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Practical Data Science Cookbook, Second Edition

By: Tattar, Bhushan Purushottam Joshi, Sean P Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

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