Feb 6, 2016 · 3 minute read

There’s a pretty impressive selection of high-quality podcasts out there these days on topics in data science. Here are four that I am really enjoying right now, along with my take on what is good about each of them. Not So Standard Deviations NSSD podcast on SoundCloud Two very smart people with PhDs in biostatistics, one still in academia and the other working as a data scientist for Etsy, Roger and Hilary sure do ramble on but the ramblings are great :) They cover all sorts of topics, always at least loosely related to data science and my favourite things about the podcast are 1.

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Jan 26, 2016 · 1 minute read

Here are the “slides” from a talk I gave on machine learning last week. The idea is to give an overview of the different topics and how they fit together. I may end up building on it as I learn about more facets of ML. In case you’re wondering, the slides were created using Hovercraft which is a python tool for creating impress.js slides but authoring them in reStructuredText instead of HTML.

Dec 21, 2015 · 18 minute read
This documents my efforts to learn both neural networks and, to a certain extent, the Python programming language. I say “to a certain extent” because far from feeling all “yay! I know Python now!” I feel more like “I can use Python 2.7 in certain ways to do certain things… yay?” And what of my understanding of neural nets as a result of this exercise? After battling with my naïve implementation of a multi-layer perceptron as described below, I felt I had a pretty visceral understanding of them.

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Oct 15, 2015 · 1 minute read
I wrote some code for doing a Welch’s T-Test in Go. You can read up on what a Welch’s t-test is here but in short it’s a significance test for the difference between two treatments (like in an A/B test) where the distributions may have unequal variances. * * * * * * * * * * * ** * *** * ***** * * * *********** -----------------|-----|----------- So if you are doing an A/B test and you have the mean and variance of each treatment, you can get a confidence measure for whether the mean of one is truly higher than the mean of the other.

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