Data Science: Sparklyr Basics For Beginners

Learn to interact with data in Apache Spark through sparklyr and simplify machine learning model implementations.

What you'll learn
Learn to perform exploratory data analysis in Spark using sparklyr
Understand the differences between working with data frames in R and Spark
Learn how to connect to Spark locally or to a remote Spark cluster
Learn how to build data products in R that don't rely on storing big data locally
Learn how to interact with data in Apache Spark through sparklyr and Spark SQL
Requirements
A PC or Mac
Internet Access
Description
Welcome to this course: Data Science - Sparklyr Basics for Beginners. Apache Spark has been increasingly adopted for the development of distributed applications. In the past year, transforming the world using data is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. 
In this course, you'll learn:
Understand the differences between working with data frames in R and SparkLearn to perform exploratory data analysis in Spark using sparklyrLearn how to connect to Spark locally or to a remote Spark clusterLearn how to build data products in R that don't rely on storing big data locallyLearn how to interact with data in Apache Spark through sparklyr and Spark SQL
At the end of this course, you will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks.

Overview

Section 1: Welcome

Lecture 1 Introduction

Section 2: Spark and Sparklyr

Lecture 2 Introduction

Lecture 3 Sparklyr Deployment Options

Lecture 4 Running Spark And R

Lecture 5 Sparklyr Livy Connections

Section 3: Getting Acquainted

Lecture 6 Set Up RStudio

Lecture 7 Spark Data Tables and R Data References

Lecture 8 Sparklyr Cheat Sheet

Section 4: Sparklyr And SparkSQL

Lecture 9 Dplyr Basics

Lecture 10 Dplyr Basics - 2

Lecture 11 Dplyr Basics - 3

Lecture 12 Lazy Execution

Lecture 13 Programming In Dplyr

Lecture 14 Extending Sparklyr With Replyr

Section 5: Hands-On Analysis Project

Lecture 15 Introduction

Lecture 16 Exploratory Analysis

Lecture 17 ML Feature Generation - 1

Lecture 18 ML Feature Generation - 2

Section 6: Course Summary

Lecture 19 Summary

Section 7: Working Files

Lecture 20 Working Files

Lecture 21 Thank You

Web Developers,Software Developers,Anyone who wants to learn Spark,Anyone interested in data science