Understanding The General Modeling Framework

When it comes to building statistical models, we do so with the purpose of understanding or approximating some aspect of our world. The concept of the general modeling framework lends well to breaking down the purposes and approaches that we might take to generate said understanding. What is the General Modeling Framework? Take a look …

COVID-19: Data Visualization Mastery

I recently made a post where we explored the data recently put out by John Hopkins University on COVID-19; while we were able to make some interesting discoveries, it seemed pertinent to gather data that provided a more full picture. In my search I came across the following dataset acquired and distributed by Tableau. This …

Guide to Exploratory Data Analysis with JHU COVID-19 Data

There is a lot of pandemonium and energy around covid-19 and it’s potential implications. There are many parties out there saying many things. One of the amazing about being a data scientist is having the ability to dive into available data on your own. Lets dive into some data currently being accumulated by John Hopkins …

Why Bias in Covid-19 Reporting Will Drive New Risks & Challenges

How incomplete information & bias are driving bad assumptions and inappropriate action Right now the world is in pandemonium about the risks associated with covid-19; most of which appear to be less about virus symptoms, and more about the larger social implications of the panic. What are the current data limitations? Our information is currently …

Intro to Bayesian Statistics

Bayesian Statistics at the Heart of Data Science Data science has deep roots in bayesian statistics & rather than giving the historical background of Sir Thomas Bayes, I’ll give you a high level perspective on bayesian statistics, bayes’ theorem, and how to leverage it as a tool in your work! Bayesian statistics are rooted in …

Foundations of Probability that Every Data Scientist Should Know

Understanding Random Events Customers and Your Application Lets say that you have a random likelihood that a user will click on a call-to-action(I’ll call it a CTA from here on out, but this is anytime you invite the reader to buy, shop, give an email, etc.) within your application. Once they have clicked the call-to-action …

Revolutionize Product with AB Testing in R

Introduction What is Ab testing? When it comes to your typical product or engineering org, team members are often left wondering whether the thing they did had an impact, or whether the option they went with among many different design options was actual the best. As these organizations want to move towards data informed design …

Getting Started with Experimental Design in R

This quick blog is designed to help you get off to the races quickly in world of data science; and here specifically, Experimental design. Enjoy! When it comes to experiemental design there are three main streps it can be broken down to: PlanningDesignAnalysis Planning & Design Planning should always begin with a well formed hypothesis. …

Principal Component Analysis in R

Hi there! Welcome to my blog on pricipal component analysis in R. Purpose: PCA is a dimensionality rediction technique; meaning that each additional variable you’re including in your modeling process represents a dimension. What does it do?: In terms of what PCA actually does, it takes a dataset with high dimensionality, and reduces them down …