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
- Hi, I'm Dan Sullivan, and in this course, I'll be describing how to use Scala for data science. We'll start by reviewing the basics of Scala data types and expressions. We'll cover how to use collections like arrays, sets, and maps. And we'll discuss the advantages of Scala's functional programming features for working with collections and analyzing data. We'll also use parallel collections which allow us to easily implement parallel processing and take advantage of multicore processors.
Next, we'll review the basics of using JDBC drivers and SQL queries to work with relational databases. We'll also explore how to use Scala with Spark, a popular distributed processing system widely used for big data analytics and data science. So let's start learning how to use Scala for data science.
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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