Scala For Data Science [Paperback]

Leverage the power of Scala with different tools to build scalable, robust data science applications

About This Book

• A complete guide for scalable data science solutions, from data ingestion to data visualization
• Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations
• Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided

Who This Book Is For

If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions.

What You Will Learn

• Transform and filter tabular data to extract features for machine learning
• Implement your own algorithms or take advantage of MLLib's extensive suite of models to build distributed machine learning pipelines
• Read, transform, and write data to both SQL and NoSQL databases in a functional manner
• Write robust routines to query web APIs
• Read data from web APIs such as the GitHub or Twitter API
• Use Scala to interact with MongoDB, which offers high performance and helps to store large data sets with uncertain query requirements
• Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations
• Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive

In Detail

Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted l“p