Practical Data Analysis


Practical Data Analysis
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Overview
Table of Contents
Author
Support
Sample Chapters
  • Explore how to analyze your data in various innovative ways and turn them into insight
  • Learn to use the D3.js visualization tool for exploratory data analysis
  • Understand how to work with graphs and social data analysis
  • Discover how to perform advanced query techniques and run MapReduce on MongoDB

Book Details

Language : English
Paperback : 360 pages [ 235mm x 191mm ]
Release Date : October 2013
ISBN : 1783280999
ISBN 13 : 9781783280995
Author(s) : Hector Cuesta
Topics and Technologies : All Books, Big Data and Business Intelligence, Data, Open Source

Table of Contents

Preface
Chapter 1: Getting Started
Chapter 2: Working with Data
Chapter 3: Data Visualization
Chapter 4: Text Classification
Chapter 5: Similarity-based Image Retrieval
Chapter 6: Simulation of Stock Prices
Chapter 7: Predicting Gold Prices
Chapter 8: Working with Support Vector Machines
Chapter 9: Modeling Infectious Disease with Cellular Automata
Chapter 10: Working with Social Graphs
Chapter 11: Sentiment Analysis of Twitter Data
Chapter 12: Data Processing and Aggregation with MongoDB
Chapter 13: Working with MapReduce
Chapter 14: Online Data Analysis with IPython and Wakari
Appendix: Setting Up the Infrastructure
Index
  • Chapter 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
    • Chapter 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
      • Chapter 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
        • Chapter 4: Text Classification
          • Learning and classification
          • Bayesian classification
            • Naïve Bayes algorithm
          • E-mail subject line tester
          • The algorithm
          • Classifier accuracy
          • Summary
              • Chapter 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
                • Chapter 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
                  • Chapter 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
                    • Chapter 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
                      • Chapter 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
                        • Chapter 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
                          • Chapter 13: Working with MapReduce
                            • MapReduce overview
                            • Programming model
                            • Using MapReduce with MongoDB
                              • The map function
                              • The reduce function
                              • Using mongo shell
                              • Using UMongo
                              • Using PyMongo
                            • Filtering the input collection
                            • Grouping and aggregation
                            • Word cloud visualization of the most common positive words in tweets
                            • Summary
                            • Chapter 14: Online Data Analysis with IPython and Wakari
                              • Getting started with Wakari
                                • Creating an account in Wakari
                              • Getting started with IPython Notebook
                                • Data visualization
                              • Introduction to image processing with PIL
                                • Opening an image
                                • Image histogram
                                • Filtering
                                • Operations
                                • Transformations
                              • Getting started with Pandas
                                • Working with time series
                                • Working with multivariate dataset with DataFrame
                                • Grouping, aggregation, and correlation
                              • Multiprocessing with IPython
                                • Pool
                              • Sharing your Notebook
                                • The data
                              • Summary
                              • Appendix: Setting Up the Infrastructure
                                • Installing and running Python 3
                                  • Installing and running Python 3.2 on Ubuntu
                                  • Installing and running IDLE on Ubuntu
                                  • Installing and running Python 3.2 on Windows
                                  • Installing and running IDLE on Windows
                                • Installing and running NumPy
                                  • Installing and running NumPy on Ubuntu
                                  • Installing and running NumPy on Windows
                                • Installing and running SciPy
                                  • Installing and running SciPy on Ubuntu
                                  • Installing and running SciPy on Windows
                                • Installing and running mlpy
                                  • Installing and running mlpy on Ubuntu
                                  • Installing and running mlpy on Windows
                                • Installing and running OpenRefine
                                  • Installing and running OpenRefine on Linux
                                  • Installing and running OpenRefine on Windows
                                • Installing and running MongoDB
                                  • Installing and running MongoDB on Ubuntu
                                  • Installing and running MongoDB on Windows
                                  • Connecting Python with MongoDB
                                • Installing and running UMongo
                                  • Installing and running Umongo on Ubuntu
                                  • Installing and running Umongo on Windows
                                • Installing and running Gephi
                                  • Installing and running Gephi on Linux
                                  • Installing and running Gephi on Windows

                                Hector Cuesta

                                Hector Cuesta holds a B.A in Informatics and M.Sc. in Computer Science. He provides consulting services for software engineering and data analysis with experience in a variety of industries including financial services, social networking, e-learning, and human resources. He is a lecturer in the Department of Computer Science at the Autonomous University of Mexico State (UAEM). His main research interests lie in computational epidemiology, machine learning, computer vision, high-performance computing, big data, simulation, and data visualization. He helped in the technical review of the books, Raspberry Pi Networking Cookbook by Rick Golden and Hadoop Operations and Cluster Management Cookbook by Shumin Guo for Packt Publishing. He is also a columnist at Software Guru magazine and he has published several scientific papers in international journals and conferences. He is an enthusiast of Lego Robotics and Raspberry Pi in his spare time. You can follow him on Twitter at https://twitter.com/hmCuesta.
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                                What you will learn from this book

                                Work with data to get meaningful results from your data analysis projects
                                Visualize your data to find trends and correlations
                                Build your own image similarity search engine
                                Learn how to forecast numerical values from time series data
                                Create an interactive visualization for your social media graph
                                Explore the MapReduce framework in MongoDB
                                Create interactive simulations with D3js

                                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.

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