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Alright, I’m kind of in a time crunch this week because I took off for Labor Day weekend. As such I just want to write a quick little post so that you guys don’t think I’m slacking in my posting something every week on Wednesday. So this is basically a quick tutorial on how to… Continue reading SKLearn in Production

Alright, so that title isn’t very clickbaity unless you are a real data nerd. Linear models do terrible at learning XOR. Simply because XOR is highly non-linear. It requires a non-linear model to learn it. And my model is non-linear too. But the only model that I use is a linear regression, straight up OLS.… Continue reading Learning XOR with Only Linear Regression (Yeah it’s Possible)

So for the past month or so, I have been writing a lot of posts about Bayesian Statistics.You might be wondering why I’ve spent so much time in the area. You might even suspect that you know the answer, but let me share 3 reasons why I enjoyed working with Bayesian Statistics this last month… Continue reading 3 Reasons to Fall in Love With Bayesian Statistics

Data science is a huge and ever expanding field. It deals heavily with math, computers, statistics, and all sorts of other disciplines. Jumping into data science often feels overwhelming, because frankly, it is. I often hear questions similar to this one from my buddy from grad school,  Nii Amon Neequaye, PhD: “… do u have… Continue reading 5 Resources to Get you Started in Data Science

My last post was pretty intense, so I thought that I would drop back down to something a little bit simpler for this post, before I go off and start in on the March Madness stuff again. It turns out that writing that last post really kicked my butt, so I’m taking a bit of… Continue reading Monte Carlo Integration in Python

So in the last two blog posts, I talked about how to do some bayesian inference in the realm of some linear models. And that is all well and good, but we can have even more fun in a bayesian framework. That’s because we are just sampling things from probability distributions. In this post I… Continue reading Predicting March Madness Winners with Bayesian Statistics in PYMC3!

In my last post I talked about bayesian linear regression. A fairly straightforward extension of bayesian linear regression is bayesian logistic regression. Actually, it is incredibly simple to do bayesian logistic regression. If you were following the last post that I wrote, the only changes you need to make is changing your prior on y… Continue reading Bayesian Logistic Regression in Python using PYMC3

This post is a direct response to the request made by @Zecca_Lehn on twitter (Yes I will write tutorials on your suggestions). What he wanted to know was how to do a Bayesian Poisson A/B tests. So for those of you that don’t know what that is let’s review the poisson distribution first. The poisson… Continue reading Bayesian Poisson A/B Testing in PYMC3 on Python

Okay, so normally I do frequentist statistics. Most of my training has been in that realm. However, I do recognize that bayesian is really the way to go. The idea of updating a prior is really appealing to me. The only problem that I have ever had with it, is that I really haven’t had… Continue reading Bayesian Regression with PYMC3 in Python

I’m going to take a break from my last couple of posts which have been about time-series analysis in python. I’m switching gears this week to answer a question my brother asked me today at a party. I couldn’t really give him a satisfactory answer, because we were at a party. He asked me, “I’ve… Continue reading Linear Algebra and Data Science