Scala Programming for Data Science

Data Scientists tend to favor one of three programming languages, Python, R, or Scala. Which to choose? Learn Scala if you are an aspiring or a seasoned Data Scientist (or Data Engineer) who is planning to work with Apache Spark to tackle Big Data with ease. This learning path has been developed by Lightbend (formerly Typesafe), the undisputed authority on all things Scala. Come along and start your journey to receiving the following badges: Scala Programming for Data Science – Level 1 and Scala Programming for Data Science – Level 2.

• Scala 101
  
  • Scala is a very unique programming language. It is compatible with Java yet a bit different, as it supports two programming paradigms: object-oriented programming (OOP) and functional programming (FP). Scala is also being used in Big data space along with Apache Spark, which has further fueled its adoption by many Java developers interested in Big Data Space. Learning Scala will certainly make you more marketable. Many companies are using or migrating to Scala these days, including Twitter, LinkedIn, Foursquare, and Quora.
• Data Science with Scala
  
  • Apache Spark™ is a fast and general engine for large-scale data processing, with built-in modules for streaming, SQL, machine learning and graph processing. This course shows how to use Spark’s machine learning pipelines to fit models and search for optimal hyperparameters using a Spark cluster.
• Spark Overview for Scala Analytics
  
  • The “Spark Overview for Scala Analytics” course will cover the history of Spark and how it came to be, how to build applications with Spark, establish an understanding of RDDs and DataFrames, and other advanced Spark topics. Apache Spark™ is a fast and general engine for large-scale data processing, with built-in modules for streaming, SQL, machine learning and graph processing. Having finished this class, a student would be prepared to leverage the core RDD and DataFrame APIs to perform analytics on datasets. This course is meant to be an overview of Spark and its associated ecosystem. For deeper understanding of Spark, we recommend that students take the Spark Fundamentals courses I and II.