The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.Data scientists Akash Tandon, Sandy Ryza, Uri Laserson,...

In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.You’ll start with an introduction to Spark and its...

If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.Updated for R 2.14 and 2.15, this second edition includes...

The Essential Guide to Scaling Your Business SolutionWritten by Oracle ACE Director and MySQL expert Ronald Bradford, with coauthor Chris Schneider, Effective MySQL: Replication Techniques in Depth describes what is needed to understand and implement MySQL replication to build scalable solutions. This book includes detailed syntax examples to demonstrate the features, options, and limitations of native MySQL replication. Providing an evaluation of various new replication...

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youâ??ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includ...

What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company, from analysts and management to the C-Suite and the board.Through interviews and examples from data scientists and analytics leaders in a variety of industries, aut...

Data collection, processing, analysis, and moreAbout This BookYour entry ticket to the world of data science with the stability and power of JavaExplore, analyse, and visualize your data effectively using easy-to-follow examplesA highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks.Who This Book Is ForThis course is meant for...

Explore the world of data science from scratch with Julia by your sideKey Features\[\*\] An in-depth exploration of Julia's growing ecosystem of packages\[\*\] Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization\[\*\] Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data setsBook DescriptionJulia is a fast and high pe...

Key FeaturesBook DescriptionThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions,...

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take...

Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods.Finally, you’ll delve into the frontier of machine learning, using the caret package in R....

There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively—until now. This hands-on book teaches novices and experienced R users how to write efficient R code.Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a...