Data Visualization, Data Storytelling Simplify your overwhelmed charts by using slope charts: a tutorial in Python Altair Image by AuthorWe may plot charts to include as many concepts as possible in our visualization. As a result, our chart could be difficult to read and distracting. For this reason, before plotting anything, sit in your chair…

During a road trip we discussed how this tool was interesting but not necessarily useful. My friend could plug in numbers and see what happened, but that didn’t help make staffing decisions any easier, as it was basically educated trial and error. What would be more helpful was a system where you could set a…

A step-by-step walkthrough of inter-participant and intra-participant classification performed on wearable sensor data of runners Image by authorRunning data collected using wearable sensors can provide insights about a runner’s performance and overall technique. The data that comes from these sensors are usually time series by nature. This tutorial runs through a fatigue detection task where…

How can we think about thinking in the simplest way possible? Opening Pandora’s box (image by author)In the 17th century, René Descartes introduced a relatively new idea — the dictum “cogito ergo sum” (“I think, therefore I am”). This simple formulation served as the basis of Western philosophy and defined for centuries our ideas on…

How cloud computing and analytics engineering forced the transition from ETL to ELT Image generated via DALL-EETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) are two terms commonly used in the realm of Data Engineering and more specifically in the context of data ingestion and transformation. While these terms are often used interchangeably, they refer to slightly different…

How to improve the performance of your Retrieval-Augmented Generation (RAG) pipeline with these “hyperparameters” and tuning strategies Tuning Strategies for Retrieval-Augmented Generation ApplicationsD ata Science is an experimental science. It starts with the “No Free Lunch Theorem,” which states that there is no one-size-fits-all algorithm that works best for every problem. And it results in…

Experimenting with Large Language Models for free Artistic representation of the LangChain, Photo by Ruan Richard Rodrigues, UnsplashEverybody knows that large language models are, by definition, large. And even not so long ago, they were available only for high-end hardware owners, or at least for people who paid for cloud access or even every API…

In 3 words: timeliness, methodology, and digestibility A couple of weeks ago, I wrote about building systems to generate more quality insights. I presented how you could increase the output of your team by working on areas such as processes, tools, culture, etc., but I never defined what I meant by “quality” — so this…

A Glossary with Use Cases for First-Timers in Data Engineering An happy Data Engineer at workAre you a data engineering rookie interested in knowing more about modern data infrastructures? I bet you are, this article is for you! In this guide Data Engineering meets Formula 1. But, we’ll keep it simple. I strongly believe that…

Graph neural networks (GNNs) and large language models (LLMs) have emerged as two major branches of artificial intelligence, achieving immense success in learning from graph-structured and natural language data respectively. As graph-structured and natural language data become increasingly interconnected in real-world applications, there is a growing need for artificial intelligence systems that can perform multi-modal…