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data science

https://app.prospect.energy/

This book presents a concise and accessible introduction to Explainable Artificial Intelligence (XAI), a field that has become essential for understanding, auditing, and trusting modern machine learning systems. As predictive models grow in complexity and influence, the need to interpret their decisions becomes fundamental for ethical, legal, and technical reasons. Aimed at students, researchers, and professionals entering data science or artificial intelligence, this text provides the conceptual foundations and methodological structure required to navigate the landscape of model explainability.

Grounded in applied knowledge and supported by established academic literature, the book introduces the motivation behind XAI, clarifies its conceptual framework, and presents the main families of methods used to explain machine learning models. Rather than offering an exhaustive catalog of algorithms, it focuses on the essential principles that enable readers to understand model behavior, assess risks, detect biases, and make informed decisions based on predictive systems.

This book presents a clear and accessible introduction to Causal Inference, one of the main conceptual pillars of modern data science. Aimed at students, researchers, and beginners, it explains how to identify, estimate, and interpret cause-and-effect relationships in data, emphasizing the essential distinction between correlation and causation for reliable, evidence-based decisions.

The text combines applied knowledge with recognized academic literature, offering a solid foundation to understand how causal inference supports analytical reasoning in statistics, economics, social sciences, and machine learning. The focus is on key principles and simple examples that make causal reasoning intuitive, even for readers without advanced technical background.

This book offers a clear and accessible introduction to Data Engineering, designed for students and beginners who want to grasp the fundamental principles of the field without unnecessary technical complexity. It focuses on what truly matters at the foundational level — understanding the role of data, how to organize it effectively, and how to build the groundwork for data science and machine learning applications. The text combines applied knowledge with established academic literature, maintaining a balance between conceptual rigor and practical comprehension.

This book provides a clear and accessible introduction to Reinforcement Learning (RL), a branch of artificial intelligence focused on how agents learn to make decisions through interaction and feedback from their environment. Aimed at students and beginners in data science, machine learning, and engineering, it presents the fundamental principles behind learning from experience, emphasizing intuition, clarity, and applied understanding.

Combining applied knowledge with well-established academic literature, the text introduces the essential logic of RL without excessive mathematical formality. It helps readers grasp how algorithms balance exploration and exploitation, evaluate rewards, and learn optimal strategies to achieve long-term goals.

A library of blog posts and tutorials. Part of the End-to-End Machine Learning School at e2eml.school/courses

ML for exploring and analyzing telemetry data from the SatNOGS network and beyond 🚀

Mirrored to https://gitlab.b-data.ch/git/git-training and https://github.com/b-data/git-training.

Creation of a trading robot for data analysis and manipulation of the libraries Pandas, Numby, MatplotLib, Yfinance, Seaborn

Proyecto personal para la presentación de clases por medio de diapositivas. La temática de las diapositivas está inspirada en el famoso anime Neon Genesis Evangelion.

Vamos a contruir un Chatbot Inteligente capaz de interpretar el lenguaje humano y generar respuestas coherentes con tu propia información

Blog on data science, geospatial analysis, energy, infrastructures and finance.

Shiny is becoming a popular publishing service, unfortunately, not every application can be deployed on servers. This tutorial demonstrates a simple means by which to deploy a shiny app to desktop.

O objetivo do projeto é desenvolver um sistema de recuperação textual utilizando diferentes mecanismos de indexação. Além disso, o projeto consiste em comparar as diferentes estratégias desenvolvidas por meio de métricas de avaliação.

Personal website forked from the example Hugo site using GitLab Pages: https://pages.gitlab.io/hugo

Webapp which can display and forecast COVID-19 pandemic data

Repositório com todo o conteúdo para a disciplina de "Modelos e Frameworks de Machine Learning", da Escola de Negócios Sustentare.

Blogdown site using GitLab Pages.