Practical Data Analysis - Second Edition

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache SparkAbout 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 ForThis 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 or forecastingIn DetailBeyond 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 approachThis is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.