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  • Book Overview & Buying Practical Data Science Cookbook, Second Edition
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Practical Data Science Cookbook, Second Edition

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean P Murphy, ABHIJIT DASGUPTA, Anthony Ojeda, RATNADIP ADHIKARI, Rajib Bhattacharya, Hashmat Rohian, Abhinav Prakash Rai
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Practical Data Science Cookbook, Second Edition

Practical Data Science Cookbook, Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean P Murphy, ABHIJIT DASGUPTA, Anthony Ojeda, RATNADIP ADHIKARI, Rajib Bhattacharya, Hashmat Rohian, Abhinav Prakash Rai
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.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Sections
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Conventions
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Reader feedback
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Customer support
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1
Preparing Your Data Science Environment
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Preparing Your Data Science Environment
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Understanding the data science pipeline
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Installing R on Windows, Mac OS X, and Linux
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Installing libraries in R and RStudio
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Installing Python on Linux and Mac OS X
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Installing Python on Windows
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Installing the Python data stack on Mac OS X and Linux
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Installing extra Python packages
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Installing and using virtualenv
2
Driving Visual Analysis with Automobile Data with R
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Driving Visual Analysis with Automobile Data with R
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Introduction
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Acquiring automobile fuel efficiency data
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Preparing R for your first project
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Importing automobile fuel efficiency data into R
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Exploring and describing fuel efficiency data
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Analyzing automobile fuel efficiency over time
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Investigating the makes and models of automobiles
3
Creating Application-Oriented Analyses Using Tax Data and Python
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Creating Application-Oriented Analyses Using Tax Data and Python
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Introduction
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Preparing for the analysis of top incomes
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Importing and exploring the world's top incomes dataset
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Analyzing and visualizing the top income data of the US
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Furthering the analysis of the top income groups of the US
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Reporting with Jinja2
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Repeating the analysis in R
4
Modeling Stock Market Data
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Modeling Stock Market Data
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Introduction
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Acquiring stock market data
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Summarizing the data
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Cleaning and exploring the data
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Generating relative valuations
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Screening stocks and analyzing historical prices
5
Visually Exploring Employment Data
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Visually Exploring Employment Data
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Introduction
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Preparing for analysis
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Importing employment data into R
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Exploring the employment data
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Obtaining and merging additional data
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Adding geographical information
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Extracting state- and county-level wage and employment information
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Visualizing geographical distributions of pay
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Exploring where the jobs are, by industry
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Animating maps for a geospatial time series
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Benchmarking performance for some common tasks
6
Driving Visual Analyses with Automobile Data
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Driving Visual Analyses with Automobile Data
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Introduction
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Getting started with IPython
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Exploring Jupyter Notebook
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Preparing to analyze automobile fuel efficiencies
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Exploring and describing fuel efficiency data with Python
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Analyzing automobile fuel efficiency over time with Python
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Investigating the makes and models of automobiles with Python
7
Working with Social Graphs
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Working with Social Graphs
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Introduction
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Preparing to work with social networks in Python
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Importing networks
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Exploring subgraphs within a heroic network
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Finding strong ties
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Finding key players
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Exploring the characteristics of entire networks
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Clustering and community detection in social networks
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Visualizing graphs
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Social networks in R
8
Recommending Movies at Scale (Python)
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Recommending Movies at Scale (Python)
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Introduction
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Modeling preference expressions
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Understanding the data
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Ingesting the movie review data
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Finding the highest-scoring movies
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Improving the movie-rating system
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Measuring the distance between users in the preference space
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Computing the correlation between users
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Finding the best critic for a user
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Predicting movie ratings for users
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Collaboratively filtering item by item
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Building a non-negative matrix factorization model
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Loading the entire dataset into the memory
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Dumping the SVD-based model to the disk
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Training the SVD-based model
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Testing the SVD-based model
9
Harvesting and Geolocating Twitter Data (Python)
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Harvesting and Geolocating Twitter Data (Python)
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Introduction
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Creating a Twitter application
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Understanding the Twitter API v1.1
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Determining your Twitter followers and friends
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Pulling Twitter user profiles
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Making requests without running afoul of Twitter's rate limits
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Storing JSON data to disk
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Setting up MongoDB for storing Twitter data
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Storing user profiles in MongoDB using PyMongo
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Exploring the geographic information available in profiles
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Plotting geospatial data in Python
10
Forecasting New Zealand Overseas Visitors
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Forecasting New Zealand Overseas Visitors
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Introduction
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The ts object
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Visualizing time series data
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Simple linear regression models
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ACF and PACF
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ARIMA models
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Accuracy measurements
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Fitting seasonal ARIMA models
11
German Credit Data Analysis
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German Credit Data Analysis
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Introduction
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Simple data transformations
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Visualizing categorical data
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Discriminant analysis
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Dividing the data and the ROC
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Fitting the logistic regression model
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Decision trees and rules
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Decision tree for german data

Simple linear regression models

Linear regression models can be built to obtain preliminary insight about the trend and seasonal impact on the time series variable. The trend and seasonal components are specified as independent variables while the time series, visitors count here, is the dependent variable. We make the following assumptions while building the linear regression model:

  1. The time series is linear in the trend and seasonal variables.
  2. The trend and seasonal components are independent of each other.
  3. The observations, time series values, are independent of each other.
  4. The error associated with the observation follows normal distribution.

Let Yt 1 < t < T, denote the time series which observations at the time points 1, 2, ..., T. For example, in our overseas visitors data, we have T = 228. In the simplistic regression model, the trend variable is the vector 1, 2, ..., T, that is, XTr = (1, 2, ..., T). We know that for monthly data, we have the month name as the seasonal indicator...

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