Book Image

Practical Data Analysis

By : Hector Cuesta
Book Image

Practical Data Analysis

By: Hector Cuesta

Overview of this book

Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB. Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.
Table of Contents (24 chapters)
Practical Data Analysis
About the Author
About the Reviewers

Data-Driven Documents (D3)

D3 is a project featured by the Stanford Visualization Group developed by Mike Bostock.

D3 provides us with web-based visualization, which is an excellent way to deploy information and help us to see things such as proportions, relationships, correlations, and patterns, and discover things previously unknown. Since web browsers provide us with a very flexible and interactive interface in practically any device such as PC, tablet, and smart phone, D3 is an amazing tool for visualization based on data using HTML, JavaScript, SVG, and CSS.

In Chapter 1, Getting Started, we saw the importance of data visualization and in this chapter, we will present examples in order to understand the use of D3.js. In the following screenshot, we can see the basic structure of an HTML document. D3 is going to be included in a basic script tag or into a JavaScript file (.js):


HyperText Markup Language (HTML) provides the basic skeleton for our visualization. An HTML document will...