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

The epidemic models

When we want to describe how a pathogen or a disease is spread into a population, we need to create a model using mathematical, statistical, or computational tools. The most common model used in the epidemiology is SIR (susceptible, infected, and recovered) model, which was formulated in the paper A Contribution to the Mathematical Theory of Epidemics by McKendrick and Kermack published in 1927.

In the models presented in this chapter, we assume a closed population (without births or deaths) and that the demographics and socio-economic variables do not affect the spread of the disease.

The SIR model

The SIR epidemiological model describes the course of an infectious disease, as we can see in the following figure. Starting with a susceptible population (S), which comes into contact with an infected population (I), where the individual remains infected and once the infection period has passed, the individual is then in the recovered state (R):

In this chapter we will use two...