Practical Data Analysis - Second Edition Ebook
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Publication Date: September 2016
File Size: 28902 Kbytes
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A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book • Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data • Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images • A hands-on guide to understanding the nature of data and how to turn it into insight Who 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 Learn • Acquire, format, and visualize your data • Build an image-similarity search engine • Generate meaningful visualizations anyone can understand • Get started with analyzing social network graphs • Find out how to implement sentiment text analysis • Install data analysis tools such as Pandas, MongoDB, and Apache Spark • Get to grips with Apache Spark • Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like 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 the basic data algorithms without the theoretical jargon, and you’ll get hands-on 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 implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.
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