Introduction No matter your exposure to data science & the world of statistics, at the very least, you’ve very likely heard of regression. In this post we’ll be talking about multiple regression, as a precursor, you’ll definitely want some familiarity with simple linear regression. If you aren’t familiar you can start here! Otherwise, let’s dive […]

# Tag Archives: data analytics

## Visualizing Multiple Linear Regression with Heatmaps

Introduction No matter your exposure to data science & the world of statistics, it’s likely that at some point, you’ve at the very least heard of regression. As a precursor to this quick lesson on multiple regression, you should have some familiarity with simple linear regression. If you aren’t, you can start here! Otherwise, let’s […]

## The Intuitive Explanation of Logistic Regression

Introduction Logistic regression can be pretty difficult to understand! As such I’ve put together a very intuitive explanation of the why, what, and how of logistic regression. We’ll start with some building blocks that should lend well to clearer understanding so hang in there! Through the course of the post, I hope to send you […]

## Three Key Charts for Visualizing Proportion Data

Proportion data examples Whatever your application of data analytics & data science, there are proportions everywhere. Proportions are all about understanding the different parts that make up a whole. Proportions are pretty much just a count of something across a given categorical variable. That could be number of customers across different industries, number of sales […]

## 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: Planning Design Analysis Planning & Design Planning should always begin with a well […]

## 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 […]

## Rules of Thumb for Getting Started with Data Visualization

Intro: Whether you’re trying to break into the world of data analytics or data science, if you’re a product manager, sales leader, or anybody seeking to understand their business being able to utilize data in a meaningful way is key. Whether you’re using data visualization software like Tableau, Domo, PowerBI, etc. or you’re using a […]