Below are the video training courses included in this Learning Path.
Expert Data Wrangling With R
Presented by Garrett Grolemund3 hours 50 minutes
Data scientists often spend 50–80% of their time preparing and transforming data sets before they begin more formal analysis work. This video tutorial shows you how to streamline your code—and your thinking—by introducing a set of principles and R packages like tidyr, dplyr, and ggvis that make this work much faster and easier.
Data Science with Microsoft Azure and R
Presented by Stephen Elston6 hours 48 minutes
In this video, you’ll learn how to develop and deploy effective machine learning models in the Microsoft Azure Machine Learning (ML) environment. You’ll learn feature selection and dimensionality reduction, functional programming with R, and R object communications, as well as Azure ML web services, including how to create and update an Azure ML web service.
Using R for Big Data with Spark
Presented by Manuel Amunategui2 hours 19 minutes
This video will show you how to leverage the power of Spark, distributed computing, and cloud storage to work with massive data sets not possible on a single computer. You’ll set-up your own extremely low-cost, easily terminated AWS account to work hands-on to create Spark clusters on the Amazon Web Services (AWS) platform; perform cluster based data modeling using Gaussian generalized linear models, binomial generalized linear models, Naive Bayes, and K-means modeling; access data from S3 Spark DataFrames and other formats like CSV, JSON, and HDFS; and do cluster-based data manipulation operations with tools like SparkR and SparkSQL.