Spark for Data Science (PDF)

Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0About This BookPerform data analysis and build predictive models on huge datasets that leverage Apache SparkLearn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challengesWork through practical examples on real-world problems with sample code snippetsWho This Book Is ForThis book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you!What You Will LearnConsolidate, clean, and transform your data acquired from various data sourcesPerform statistical analysis of data to find hidden insightsExplore graphical techniques to see what your data looks likeUse machine learning techniques to build predictive modelsBuild scalable data products and solutionsStart programming using the RDD, DataFrame and Dataset APIsBecome an expert by improving your data analytical skillsIn DetailThis is the era of Big Data. The words 'Big Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages.Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R.With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.Style and approachThis book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.