Scala Programming for Data Science

To light a fire, do you use a match, a lighter, or a torch? Depends on the size of the fire, much like the decisions that lead one to use Python, R, or Scala. Spark your interest in selecting the tools you need to tackle Big Data with ease, that will not just blow out.

About this learning path

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.

Courses

Scala 101

Effort: 6-8 hours
Level: Beginner
Available in: English
About the course

Scala, short for scalable language, is future ready, but are you? Move beyond your regular java, and check out what is brewing in this language. Would that be two sugars and one milk or vice versa?

Spark Overview for Scala Analytics

Effort: 7 hours
Level: Beginner
Available in: English
About the course

On-line reviews show the preference to use Scala with Spark vs Python for enterprise scale big data analytical platforms. Why is that? This question will be addressed in this course as well as many others that will guide your use of this program to address your need for speed.

Data Science for Scala

Effort: 6-8 hours
Level: Intermediate
Available in: English
About the course

Apache Spark™ is a fast and general engine for large-scale data processing, with built-in modules for streaming, machine learning and graph processing. This course shows you how to use Spark’s machine learning pipelines to fit models and search for optimal hyperparameters using a Spark cluster.

Complete Scala Programming for Data Science Learning path

Our learning paths are designed to build on the content learned in the first course and then build upon the concepts in courses that follow. We recommend that they are completed in the order outlined in this learning path to ensure you get the most out of your investment of time. If you like what you see here, come and discover other learning paths and browse our course catalog.
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