Subject archive for "r," page 4
Python vs. R for Data Science
R and Python are both popular open source programming languages for data scientists. Each has its advantages for performing data science tasks. So, which one should you use? In this video, Eduardo Ariño de la Rubia, makes a case for each of them as the "best" language for data scientists.
By Sheila Doshi9 min read
A Quick Benchmark of Hashtable Implementations in R
UPDATE: I am humbled and thankful to have had so much feedback on this post! It started out as a quick and dirty benchmark but I had some great feedback from Reddit, comments on this post, and even from Hadley himself! This post now has some updates. The major update is that R's new.env(hash=TRUE) actually provides the fastest hash table if your keys are always going to be valid R symbols! This is one of the things I really love about the data science community and the data science process. Iteration and peer review is key to great results!
By Eduardo Ariño de la Rubia8 min read
High-performance Computing with Amazon's X1 Instance - Part II
When you have at your disposal 128 cores and 2TB of RAM, it’s hard not to experiment and attempt to find ways to leverage the amount of power that is at your fingertips. We’re excited to remind our readers that we support Amazon’s X1 instances in Domino, you can do data science on machines with 128 cores and 2TB of RAM — with one click:
By Eduardo Ariño de la Rubia5 min read
Using k-Nearest Neighbors (k-NN) in Production
What is k-Nearest Neighbors (k-NN)?
By Sheila Doshi1 min read
Join Us: An Introduction to Using k-NN in Production
Join us next Wednesday, October 5 for a webinar hosted by our Chief Data Scientist covering best practices for using k-NN in production.
By Sheila Doshi1 min read
An Introduction to Model-Based Machine Learning
This blog post follows my journey from traditional statistical modeling to Machine Learning (ML) and introduces a new paradigm of ML called Model-Based Machine Learning (Bishop, 2013). Model-Based Machine Learning may be of particular interest to statisticians, engineers, or related professionals looking to implement machine learning in their research or practice.
By Daniel Emaasit15 min read
Subscribe to the Domino Newsletter
Receive data science tips and tutorials from leading Data Science leaders, right to your inbox.
By submitting this form you agree to receive communications from Domino related to products and services in accordance with Domino's privacy policy and may opt-out at anytime.