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 to understand your data to find patterns, trends, relationships and insight.
This book contains practical projects that take advantage of the MongoDB, D3.js, Python language and its ecosystem to present the concepts using code snippets and detailed descriptions.
Chapter 1, Getting Started, In this chapter, we discuss the principles of data analysis and the data analysis process.
Chapter 2, Preprocessing Data, explains how to scrub and prepare your data for the analysis, also introduces the use of OpenRefine which is a Data Cleansing tool.
Chapter 3, Getting to Grips with 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 a Stock Price 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 Diseases 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, Working with 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 Jupyter and Wakari, explains how to use the Wakari platform and introduces the basic use of Pandas and PIL with IPython.
Chapter 15, Understanding Data Processing using Apache Spark, explains how to use distributed file system along with Cloudera VM and how to get started with a data environment. Finally, we describe the main features of Apache Spark with a practical example.
The basic requirements for this book are as follows:
Python
OpenRefine
D3.js
Mlpy
Natural Language Toolkit (NLTK)
Gephi
MongoDB
This book is for Software Developers, Analyst 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 from different kinds of data like, 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 assumed.
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