Learn about set collection types and operations on them.
- [Instructor] Let's talk about Scala arrays,…vectors and ranges.…First, we'll start with scala REPL.…Scala arrays are indexed collections…of values much like you'll find…in other programming languages.…Arrays are a mutable collection type.…Let's create an array of temperatures.…So, we'll use val temps…and we'll create an array…and we'll forget about units of measure now…and just simply work with integers,…50, 51, 56, 53 and 40.…
Arrays in Scala are zero based,…so getting the array value at index one…returns actually the second item in the array,…so for example, if we say temps of one…we'll get 51.…If we want the first item,…we should use zero.…Scala is an object-oriented language.…Variables, including arrays,…are objects and have methods associated with them.…To find the length of an array,…we can use the length method…which is invoked by specifying a value name…followed by a period and the word length…as in temps.length returns five…which is the length of the array.…
The values in array can be updated…by referencing the array variable…
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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