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

Chapter 9. Modeling Infectious Disease with Cellular Automata

One of the goals of data analysis is to understand the system we are studying and modeling is the natural way to understand a real-world phenomenon. A model is always a simplified version of the real thing. However, through modeling and simulation we can try scenarios that are hard to reproduce, or are expensive, or dangerous. We can then perform analysis, define thresholds, and provide the information needed to make decisions. In this chapter, we will model an infectious disease outbreak through cellular automaton simulation implemented in JavaScript using D3.js. Finally, we will contrast the results of the simulation with the classical ordinary differential equations.

In this chapter, we will cover:

  • Introduction to epidemiology

    • The epidemiology triangle

  • The epidemic models:

    • The SIR model

    • Solving ordinary differential equation for the SIR model with SciPy

    • The SIRS model

  • Modeling with cellular automata:

    • Cell, state, grid, and neighborhood...