Practical data analysis / Hector Cuesta.

Online resource; title from PDF title page (EBSCO, viewed November 16, 2016).

Includes index.

Cover ; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started; [Computer science]; Computer science; Artificial intelligence; Machine learning; Statistics; Mathematics; Knowledge domain; Data, information, and knowledge; Inter-relationship between 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?Quantified self; Sensors and cameras; Social network analysis; Tools and toys for this book; Why Python?; Why mlpy?; Why D3.js?; Why MongoDB?; Summary; Chapter 2: Preprocessing 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; Parsing a CSV file with the CSV module; Parsing CSV file using NumPy; JSON; Parsing JSON file using the JSON module; XML; Parsing XML in Python using the XML module; YAML; Data reduction methods.

Filtering and samplingBinned algorithm; Dimensionality reduction; Getting started with OpenRefine; Text facet; Clustering; Text filters; Numeric facets; Transforming data; Exporting data; Operation history; Summary; Chapter 3: Getting to Grips with Visualization; What is visualization?; Working with web-based visualization; Exploring scientific visualization; Visualization in art; The visualization life cycle; Visualizing different types of data; HTML; DOM; CSS; JavaScript; SVG; Getting started with D3.js; Bar chart; Pie chart; Scatter plots; Single line chart; Multiple line chart.

Interaction and animationData from social networks; An overview of visual analytics; Summary; Chapter 4: Text Classification; Learning and classification; Bayesian classification; NaÃv̄e Bayes; E-mail subject line tester; The data; The algorithm; Classifier accuracy; Summary; Chapter 5: Similarity-Based Image Retrieval; Image similarity search; Dynamic time warping; Processing the image dataset; Implementing 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 D3jsQuantitative analyst; Summary; Chapter 7: Predicting Gold Prices; Working with time series data; Components of a time series; Smoothing time series; Lineal regression; The data -- historical gold prices; Nonlinear regressions; Kernel Ridge Regressions; 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 (LDA); Principal Component Analysis (PCA).

About This BookLearn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your dataApply Machine Learning algorithms to different kinds of data such as social networks, time series, and imagesA hands-on guide to understanding the nature of data and how to turn it into insightWho This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will LearnAcquire, format, and visualize your dataBuild an image-similarity search engineGenerate meaningful visualizations anyone can understandGet started with analyzing social network graphsFind out how to implement sentiment text analysisInstall data analysis tools such as Pandas, MongoDB, and Apache SparkGet to grips with Apache SparkImplement machine learning algorithms such as classification and forecastingIn Detail Beyond buzzwords such as big data or data science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains basic data algorithms without the theoretical jargon, and you'll get hands-on experience of turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data, such as text, images, social network graphs, documents, and time series, showing you how to process large amounts of data with MongoDB and Apache Spark.

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