Practical data analysis / Hector Cuesta.

Print version record.

Includes index.

Cover; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; 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 analysisTools and toys for this book; Why Python?; Why mlpy?; Why D3.js?; Why MongoDB?; Summary; Chapter 2:Working with Data; Data sources; 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 facetsTransforming 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 5:Similarity-based Image Retrieval; Image similarity search; Dynamic time warping (DTW).

Processing the image datasetImplementing DTW; Analyzing the results; Summary; Chapter 6:Simulation of Stock Prices; Financial time series; Random walk simulation; Monte Carlo methods; Generating random numbers; Implementation in D3.js; 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 MachinesUnderstanding 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; Modelling with cellular automata cell, state, grid, and neighborhood.

Each chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis. Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle.

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