The Best Python Packages for Data Science

This report is the second in a series analyzing data science related topics. This time around, specifically, we rank 15 top Python data science packages, hopefully with results of use to the data science community.


Improving automatic document production with R

In this post, I describe the latest iteration of my automatic document production with R. It improves upon the methods used in Rtraining, and previous work on this topic can read by going to the auto deploying R documentation tag. I keep banging on about this area because reproducible research / analytical document pipelines is an area I’ve a keen interest in. I see it as a core part of DataOps as it’s vital for helping us ensure our models and analysis are correct in data science and boosting our productivity.


How to interpret correspondence analysis plots (it probably isn’t the way you think)

Correspondence analysis is a popular data science technique. It takes a large table, and turns it into a seemingly easy-to-read visualization. Unfortunately, it is not quite as easy to read as most people assume. In How correspondence analysis works (a simple explanation), I provide a basic explanation of how to interpret correspondence analysis, so if you are completely new to the field, please read that post first. In this post I provide lots of examples to illustrate some of the more complex issues.


Machine Learning. Artificial Neural Networks (Strength of Concrete).

It is important to mention that the present posts series began as a personal way of practicing R programming and machine learning. Subsequently feedback from the community, urged me to continue performing these exercises and sharing them. The bibliography and corresponding authors are cited at all times and this posts series is a way of honoring and giving them the credit they deserve for their work. We will develop an artificial neural network example. The example was originally published in ‘Machine Learning in R’ by Brett Lantz, PACKT publishing 2015 (open source community experience destilled).


shinydashboard 0.6.0

Shinydashboard 0.6.0 is now on CRAN! This release of shinydashboard was aimed at both fixing bugs and also bringing the package up to speed with users’ requests and Shiny itself (especially fully bringing bookmarkable state to shinydashboard’s sidebar). In addition to bug fixes and new features, we also added a new “Behavior” section to the shinydashboard website to explain this release’s two biggest new features, and also to provide users with more material about shinydashboard-specific behavior.
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