Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0
About This Book
- Perform data analysis and build predictive models on huge datasets that leverage Apache Spark
- Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges
- Work through practical examples on real-world problems with sample code snippets
Who This Book Is For
This 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 Learn
- Consolidate, clean, and transform your data acquired from various data sources
- Perform statistical analysis of data to find hidden insights
- Explore graphical techniques to see what your data looks like
- Use machine learning techniques to build predictive models
- Build scalable data products and solutions
- Start programming using the RDD, DataFrame and Dataset APIs
- Become an expert by improving your data analytical skills
This 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 approach
This 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.