An Introduction and Example — This is the final post in a 3-part series on Fourier and Wavelet Transforms. In previous posts both the Fourier Transform (FT) and its practical implementation, the Fast-Fourier Transform (FFT) are discussed. In this post, a similar idea is introduced the Wavelet Transform. Once you have a solid understanding of…

Resolving the “I need experience to get a job, but need a job to get experience” loop — With its 6-figure salaries and rapid growth, data science is a highly sought-after industry [1]. However, for most, landing that first DS job and breaking into the field is a major challenge. It’s often the story of “you need experience to get a job, but you need a job to…

5 easy steps toward your next data science role — By the end of this article, you will be able to create your own data science portfolio from scratch using GitHub Pages. Technical Skills Aren’t Enough Getting a job as a data scientist is not all about your programming and ML chops. A big part of getting hired is selling yourself. Unfortunately, this tends…

And My Desperate Search For Data Entrepreneurs — A Lesson From Mr. Beast In an interview [1], ultra-mega-YouTuber Mr. Beast attributed his early YouTube success to 2 things. 1) being completely obsessed and 2) being surrounded by people who are also obsessed. Imagine either of these without the other. Obsession is powerful, but even a mediocre team can out-produce a highly motivated individual…

Our best defense against AI-induced inequality — Rice on a Chessboard Innovation builds on top of itself. Electricity enabled computers, computers enabled the internet, the internet enabled social media, and social media enabled me to post selfies from my trip to Cancun 😎. A key consequence of this (recursive) property is that innovation scales exponentially, which is like 1 -> 2…

How to reduce predictor count and segment variables — This is the 3rd article in a series on decision trees. Thus far, we’ve discussed how to build predictive models using decision trees and tree ensembles. While this is the core use of these techniques, we can use them for more than just making predictions. …

Breaking down bagging, boosting, Random Forest, and AdaBoost — This is the 2nd article in a series on decision trees. In the last post, we introduced decision trees and discussed how to grow them using data. While they are an intuitive machine learning approach, decision trees are prone to overfitting. …

Making data-informed decisions with Python — This is the first article in a series on Decision Trees. In this post, I introduce decision trees and describe how to grow them using data. The post concludes with example Python code showing how to create and use a decision tree to help make medical prognoses. Key Points: Decision Trees are…

Review and Summary of a 5-part Series — In this article, I review and summarize key points from a 5-part series on causal effects. While only a high-level contour is given here, further details and references can be found in the subsequent articles linked below. Additionally, each post has an associated video walkthrough on YouTube. Table of contents Introduction Propensity Score-based…