Join Dan Sullivan for an in-depth discussion in this video What you should know, part of Scala Essential Training for Data Science.
- [Instructor] Before we get started, I want to briefly describe some assumptions about background knowledge. For this course, I assume you have some familiarity with programming languages such as Python, Java, or C. We'll work with a relational database and SQL in one chapter. If at any time you would like a refresher on relational databases, I suggest you take a closer look at the Relational Database Fundamentals course or the SQL Essential Training course. I also assume you're familiar with installing software on your Windows, Mac, or Linux computer.
We'll be installing Scala, PostgreSQL, a relational database, and Spark, a distributed processing system. We'll also make extensive use of the Scala command line tool.
Dan also focuses on using Scala with Spark, a distributed processing platform. He first describes how to work with Resilient Distributed Datasets (RDDs)—a fundamental Spark data structure—and then explains how to use Scala with Spark DataFrames, a new class of data structure specially designed for analytic processing. He wraps up the course by providing a summary of advantages of using Scala for data science.
- The advantages of Scala for data science
- Scala data types
- Scala arrays, vectors, and ranges
- Parallel processing in Scala
- Mapping functions over parallel collections
- When and when not to use parallel collections
- Using SQL in Scala
- Scala and Spark RDDs
- Scala and Spark DataFrames
- Creating DataFrames
Skill Level Intermediate
Java for Data Scientists Essential Trainingwith Charles Kelly2h 43m Intermediate
1. Introduction to Scala
2. Parallel Processing in Scala
3. Using SQL in Scala
4. Scala and Spark RDDs
5. Scala and Spark DataFrames
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