While amusing myself with a quick proof of concept for an MRP (material resource planning) for work, I was attempting to implement a login form without using any existing Django packages such as Django All-Auth. Luckily, Django conveniently provides a built-in login view as part of the authentication system. Using this, one can easily utilize the login view to implement a customized login page to their liking.
While preparing for my matriculation at Northwestern, I decided to get my hands dirty in some more data analytics. Upon delving deeper into the myriad of techniques, I soon stumbled upon a not-yet industry standard of Data Science/Analytic workflow.
I decided to learn Python and was recently admitted into Northwestern's Predictive Analytics Graduate program. So I figured, meh! Why not learn both at the same time? So my first foray into predictive analytics had to do with a supervised classification model called a decision tree. What is a decision tree? In essence it's a predictive algorithm that just so happens to be (when drawn out / visualized), well... a tree. I first encountered decision trees in the book published by O'Reilly called, Data Science for Business. I used most of Joe McCarthy's primer as the guide to my programming exercises and modified it a bit to better suit my nuances in programming style. It was the first predictive model they described and one of the more interesting ones in my opinion because of its relative simplicity. They cited using a data set of mushrooms samples, courtesy of UCI. The aim of the tree was to predict whether any additional samples based on its attributes, was either poisonous or edible. Which leads me to the question; how did they manage to find out whether the samples from the original data set was poisonous or edible? The sample data set can be found here: Mushrooms.
I was recently tasked with acquiring a certain number sense in regards to A/B testing. After Googling around a bit, I noticed that much of the information on A/B Testing were fairly rudimentary introductions that simply skimmed the concepts of said testing. There was a myriad of products and services that offered A/B Testing, but these got straight to the results of the sample tests. What I wanted was the nitty, gritty, gory, superfluous details of the math. Understanding the Wikipedia definition is one thing, actually being able to wield the math was another. What I thought would be a simple 30 minute Wikipedia read, quickly spiraled into a furious mad dash to derive the underlying principles of what is essentially, everything I forgot in AP Statistics back in high school. 15 hours and a bottle of 2 buck chuck later, I had an elementary grasp of the glorified math behind the aptly named, A/B Testing.
A few weeks ago I read an Etsy report from their blog regarding their prospect of utilizing small scale and local US manufacturers / contract manufacturers (CM) to create their seller's items. I found this intriguing and soon began to brainstorm how this could be beneficial to the myriad of Etsy shop owners and the countless variety of merchandise they sell.
Attention to detail? Nah, attention to the whole picture.