Project Jupyter
Free software, open standards, and web services for interactive computing across all programming languages JupyterLab: A Next-Generation Notebook Interface JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and ma
Switching from RSpec to Minitest | The Art and Science of Ruby
I like the “specification” style of testing – it makes more sense to me than the “assertion” style. But I’ve recently switched from RSpec to Minitest (with Mocha). Why? RSpec is huge. It makes lovely tests that read like English, but it’s huge. Plus its recent switch from object.should == something to expect(object).to be == something negates some of the elegance of its syntax (although I understa
Finding the K in K-Means Clustering
A couple of weeks ago, here at The Data Science Lab we showed how Lloyd’s algorithm can be used to cluster points using k-means with a simple python implementation. We also produced interesting visualizations of the Voronoi tessellation induced by the clustering. At the end of the post we hinted at some of the shortcomings of this clustering procedure. The basic k-means is an extremely simple and
7 Command-Line Tools for Data Science | Jeroen Janssens
Data science is OSEMN (pronounced as awesome). That is, it involves Obtaining, Scrubbing, Exploring, Modelling, and iNterpreting data. As a data scientist, I spend quite a bit of time on the command-line, especially when there’s data to be obtained, scrubbed, or explored. And I’m not alone in this. Recently, Greg Reda discussed how the classics (e.g., head, cut, grep, sed, and awk) can be used for
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