Join Dan Sullivan for an in-depth discussion in this video Using the exercise files, part of Scala Essential Training for Data Science.
- [Instructor] If you have access to the Exercise Files for this course, you can follow along. The Exercise Files are organized into folders, one for each chapter that has an Exercise File. Each chapter folder is further organized into videos. Each video that has an Exercise File has a folder. Within that folder, you'll see one or more data files or a SQL file. The data files contain data in text and JSON format. We'll load data from these files and analyze them using Spark.
The SQL file contains SQL commands to create a database. We'll create that in Postgres and query it using JDBC.
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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