After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Explore the Advantages of R, Spark, and sparklyr
R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. The sparklyr package lets you write dplyr R code that runs on a Spark cluster, giving you the best of both worlds. This 4-hour course teaches you how to manipulate Spark DataFrames using both the dplyr interface and the native interface to Spark, as well as trying machine learning techniques.Load Data into Spark and Manipulate Spark DataFrames
You’ll start this Spark course by investigating how Spark and R work well together and practicing loading data, ready for cleaning, transformation, and analysis. You’ll use Spark frames and dplyr syntax to manipulate your data by filtering and arranging rows, and mutating and summarizing columns.Delve into Big Data Analysis with Spark MLib
This course focuses on building your skills and confidence in analyzing huge datasets. The final chapters take you through Spark’s machine learning data transformation features and offer you the chance to practice sparklyr’s machine learning routines by using it to make predictions using gradient boosted trees and random forests. "Prerequisites
Curriculum
Course outline
1
Light My Fire: Starting To Use Spark With dplyr Syntax
In which you learn how Spark and R complement each other, how to get data to and from Spark, and how to manipulate Spark data frames using dplyr syntax.
- Getting started50 XP
- Made for each other50 XP
- Here be dragons50 XP
- The connect-work-disconnect pattern100 XP
- Copying data into Spark100 XP
- Big data, tiny tibble100 XP
- Exploring the structure of tibbles100 XP
- Selecting columns100 XP
- Filtering rows100 XP
- Arranging rows100 XP
- Mutating columns100 XP
- Summarizing columns100 XP
2
Tools of the Trade: Advanced dplyr Usage
In which you learn more about using the
dplyr interface to Spark, including advanced field selection, calculating groupwise statistics, and joining data frames.3
Going Native: Use The Native Interface to Manipulate Spark DataFrames
In which you learn about Spark's machine learning data transformation features, and functionality for manipulating native DataFrames.
4
Case Study: Learning to be a Machine: Running Machine Learning Models on Spark
A case study in which you learn to use
sparklyr's machine learning routines, by predicting the year in which a song was released.R
Introduction to Spark with sparklyr in R
Course
Complete
