Practical Data Analysis

Hector Cuesta

• Éditeur: Packt Publishing
• Année: 2013
• ISBN:
  9781783280995
• Sujet: Computer Science
• Sous-sujet: Databases

In Detail

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.

Approach

Practical Data Analysis is a practical, step-by-step guide to empower small businesses to manage and analyze your data and extract valuable information from the data.

Who this book is for

This book is for developers, small business users, and analysts who want to implement data analysis and visualization for their company in a practical way. You need no prior experience with data analysis or data processing; however, basic knowledge of programming, statistics, and linear algebra is assumed.

Practical Data Analysis

---

Table of Contents

Practical Data Analysis

Credits

Foreword

About the Author

Acknowledgments

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers and more

Why Subscribe?

Free Access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Getting Started

Computer science

Artificial intelligence (AI)

Machine Learning (ML)

Statistics

Mathematics

Knowledge domain

Data, information, and knowledge

The nature of data

The data analysis process

The problem

Data preparation

Data exploration

Predictive modeling

Visualization of results

Quantitative versus qualitative data analysis

Importance of data visualization

What about big data?

Sensors and cameras

Social networks analysis

Tools and toys for this book

Why Python?

Why mlpy?

Why D3.js?

Why MongoDB?

Summary

2. Working with Data

Datasource

Open data

Text files

Excel files

SQL databases

NoSQL databases

Multimedia

Web scraping

Data scrubbing

Statistical methods

Text parsing

Data transformation

Data formats

CSV

Parsing a CSV file with the csv module

Parsing a CSV file using NumPy

JSON

Parsing a JSON file using json module

XML

Parsing an XML file in Python using xml module

YAML

Getting started with OpenRefine

Text facet

Clustering

Text filters

Numeric facets

Transforming data

Exporting data

Operation history

Summary

3. Data Visualization

Data-Driven Documents (D3)

HTML

DOM

CSS

JavaScript

SVG

Getting started with D3.js

Bar chart

Pie chart

Scatter plot

Single line chart

Multi-line chart

Interaction and animation

Summary

4. Text Classification

Learning and classification

Bayesian classification

Naïve Bayes algorithm

E-mail subject line tester

The algorithm

Classifier accuracy

Summary

5. Similarity-based Image Retrieval

Image similarity search

Dynamic time warping (DTW)

Processing the image dataset

Implementing DTW

Analyzing the results

Summary

6. Simulation of Stock Prices

Financial time series

Random walk simulation

Monte Carlo methods

Generating random numbers

Implementation in D3.js

Summary

7. Predicting Gold Prices

Working with the time series data

Components of a time series

Smoothing the time series

The data – historical gold prices

Nonlinear regression

Kernel ridge regression

Smoothing the gold prices time series

Predicting in the smoothed time series

Contrasting the predicted value

Summary

8. Working with Support Vector Machines

Understanding the multivariate dataset

Dimensionality reduction

Linear Discriminant Analysis

Principal Component Analysis

Getting started with support vector machine

Kernel functions

Double spiral problem

SVM implemented on mlpy

Summary

9. Modeling Infectious Disease with Cellular Automata

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

Global stochastic contact model

Simulation of the SIRS model in CA with D3.js

Summary

10. Working with Social Graphs

Structure of a graph

Undirected graph

Directed graph

Social Networks Analysis

Acquiring my Facebook graph

Using Netvizz

Representing graphs with Gephi

Statistical analysis

Male to female ratio

Degree distribution

Histogram of a graph

Centrality

Transforming GDF to JSON

Graph visualization with D3.js

Summary

11. Sentiment Analysis of Twitter Data

The anatomy of Twitter data

Tweet

Followers

Trending topics

Using OAuth to access Twitter API

Getting started with Twython

Simple search

Working with timelines

Working with followers

Working with places and trends

Sentiment classification

Affective Norms for English Words

Text corpus

Getting started with Natural Language Toolkit (NLTK)

Bag of words

Naive Bayes

Sentiment analysis of tweets

Summary

12. Data Processing and Aggregation with MongoDB

Getting started with MongoDB

Database

Collection

Document

Mongo shell

Insert/Update/Delete

Queries

Data preparation

Data transformation with OpenRefine

Inserting documents with PyMongo

Group

The aggregation framework

Pipelines

Expressions

Summary

13. Working with MapReduce

MapReduce overview

Programming model

Using MapReduce with MongoDB

The map func...