Webinar: Model-Based Machine Learning and Probabilistic Programming using RStan

I am glad to announce that I shall be presenting a live webinar with Domino Data Labs on July 20, 2016 from 11:00 - 11:30 AM PST on Model-Based Machine Learning and Probabilistic Programming using RStan. If you are interested in adopting machine learning but are overwhelmed by the vast amount of learning algorithms, this webinar will show how to overcome that challenge.

Incase you missed it: My Webinar on Spatial Data Analysis with R

In case you missed my free webinar on “Getting Started with Spatial Data Analysis with R”, here is the recording. If you have any questions, please do not hesitate to contact me.

Using Apache SparkR to Power Shiny Applications: Part I

The objective of this blog post is demonstrate how to use Apache SparkR to power Shiny applications. I have been curious about what the use cases for a “Shiny-SparkR” application would be and how to develop and deploy such an app.

Launch Apache Spark on AWS EC2 and Initialize SparkR Using RStudio

In this blog post, we shall learn how to launch a Spark stand alone cluster on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) for analysis of Big Data. This is a continuation from our previous blog, which showed us how to download Apache Spark and start SparkR locally on windows OS and RStudio

Installing and Starting SparkR Locally on Windows OS and RStudio

With the recent release of Apache Spark 1.4.1 on July 15th, 2015, I wanted to write a step-by-step guide to help new users get up and running with SparkR locally on a Windows machine using command shell and RStudio. SparkR provides an R frontend to Apache Spark and using Spark’s distributed computation engine allows R-Users to run large scale data analysis from the R shell