On Day 27, we had our strategy enhancement reveal. By modifying the arithmetic behind our error correction, we chiseled another 16% points of outperformance vs. buy-and-hold and the original 12-by-12 strategy. All that remains now is to run the pred...

It’s not so much the amount of information that we are swamped with, but how we are unable to control and interpret it. Though we collect and generate data at never before rates, a lot of it sits idle, waiting to be explored and utilized. In this blog, we ...

Introduction The tools developers and testers use to interact with the web are evolving. The journey has been revolutionary, from Selenium, first released in the early 2000s, to the contemporary frameworks that handle today’s dynamic and intricate web applications. In the middle of these advancements, Playwright has become a ...

On Day 26, we extended the comparative error analysis to the original, 12-by-12 strategy and showed how results were similar to the unadjusted strategy relative to the adjusted one. The main observation that emerged was that the adjusted strategy pe...

In a previous post I looked at the HTTP request headers used to manage browser caching. In this post I’ll look at a real world example. It’s a rather deep dive into something that’s actually quite simple. However, I find it helpful for my understanding to pick ...

Organisations lose millions of dollars each year due to data pipeline failures that cause duplicate transactions, inconsistent results, and corrupted datasets. These problems are systemic and point to one biggest problem: non-idempotent operations in data analytics systems. Idempotent operations in data analytics will give a consistent output no matter how ...

Data scientists often face a puzzling challenge. Their carefully crafted Principal Component Analysis (PCA) implementation gives different results compared to scikit-learn’s version. These differences create confusion and make debugging harder, even when both implementations appear mathematically sound. PCA is a significant dimensionality reduction technique in machine learning that helps ...

The last five days! On Day 25, we compared the peformance of the adjusted vs. unadjusted strategy for different prediction scenarios: true and false positives and negatives. For true positives and false negatives, the adjusted strategy performed bet...

If you’ve worked with statistical data, you’ve likely encountered z-scores. A z-score measures how far a data point is from the mean, expressed in terms of standard deviations. It helps identify outliers and compare data distributions, making it a vital tool in data science. In this guide, we’...

Developing a Shiny app is relatively straightforward, but deploying it to a production environment is a whole another set of challenges. If you’re unfamiliar, R Shiny is a powerful web application framework for building interactive data visualizations, dashboards, and other data-driven applications. At Appsilon, we’re a big fan ...