Preface
Practical Data Analysis provides a series of practical projects in order to turn data into insight. It covers a wide range of data analysis tools and algorithms for classification, clustering, visualization, simulation, and forecasting. The goal of this book is to help you understand your data to find patterns, trends, relationships, and insight.
This book contains practical projects that take advantage of the MongoDB, D3.js, and Python language and its ecosystem to present the concepts using code snippets and detailed descriptions.
What this book covers
Chapter 1, Getting Started, discusses the principles of data analysis and the data analysis process.
Chapter 2, Working with Data, explains how to scrub and prepare your data for the analysis and also introduces the use of OpenRefine which is a data cleansing tool.
Chapter 3, Data Visualization, shows how to visualize different kinds of data using D3.js, which is a JavaScript Visualization Framework.
Chapter 4, Text Classification, introduces the binary classification using a Naïve Bayes algorithm to classify spam.
Chapter 5, Similarity-based Image Retrieval, presents a project to find the similarity between images using a dynamic time warping approach.
Chapter 6, Simulation of Stock Prices, explains how to simulate stock prices using Random Walk algorithm, visualized with a D3.js animation.
Chapter 7, Predicting Gold Prices, introduces how Kernel Ridge Regression works and how to use it to predict the gold price using time series.
Chapter 8, Working with Support Vector Machines, describes how to use support vector machines as a classification method.
Chapter 9, Modeling Infectious Disease with Cellular Automata, introduces the basic concepts of computational epidemiology simulation and explains how to implement a cellular automaton to simulate an epidemic outbreak using D3.js and JavaScript.
Chapter 10, Working with Social Graphs, explains how to obtain and visualize your social media graph from Facebook using Gephi.
Chapter 11, Sentiment Analysis of Twitter Data, explains how to use the Twitter API to retrieve data from Twitter. We also see how to improve the text classification to perform a sentiment analysis using the Naïve Bayes algorithm implemented in the Natural Language Toolkit (NLTK).
Chapter 12, Data Processing and Aggregation with MongoDB, introduces the basic operations in MongoDB as well as methods for grouping, filtering, and aggregation.
Chapter 13, Working with MapReduce, illustrates how to use the MapReduce programming model implemented in MongoDB.
Chapter 14, Online Data Analysis with IPython and Wakari, explains how to use the Wakari platform and introduces the basic use of Pandas and PIL with IPython.
Appendix, Setting Up the Infrastructure, provides detailed information on installation of the software tools used in this book.
What you need for this book
The basic requirements for this book are as follows:
Python
OpenRefine
D3.js
mlpy
Natural Language Toolkit (NLTK)
Gephi
MongoDB
Who this book is for
This book is for software developers, analysts, and computer scientists who want to implement data analysis and visualization in a practical way. The book is also intended to provide a self-contained set of practical projects in order to get insight about different kinds of data such as, time series, numerical, multidimensional, social media graphs, and texts. You are not required to have previous knowledge about data analysis, but some basic knowledge about statistics and a general understanding of Python programming is essential.
Conventions
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "In this case, we will use the integrate method of the SciPy module to solve the ODE."
A block of code is set as follows:
beta = 0.003
gamma = 0.1
sigma = 0.1
def SIRS_model(X, t=0):
r = scipy.array([- beta*X[0]*X[1] + sigma*X[2]
, beta*X[0]*X[1] - gamma*X[1]
, gamma*X[1] ] –sigma*X[2])
return rWhen we wish to draw your attention to a particular part of a code block, the relevant lines or items are highlighted as follows:
[[215 10 0] [153 72 0] [ 54 171 0] [ 2 223 0] [ 0 225 0] [ 0 178 47] [ 0 72 153] [ 0 6 219] [ 0 0 225] [ 47 0 178] [153 0 72] [219 0 6] [225 0 0]]
Any command-line input or output is written as follows:
db.runCommand( { count: TweetWords })
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Next, as we can see in the following screenshot, we will click on the Map Reduce option."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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