D

data science

Recommend.Games blog: https://blog.recommend.games/

https://app.prospect.energy/

TheoryFlow backend's repository.

Mirrored to https://gitlab.b-data.ch/docker/deployments/traefik and https://github.com/b-data/docker-deployment-traefik.

Provide FRCC DSIR the ability to securely, confidently, and efficiently answer questions informed from our team’s codebases. In places where confidence is low, it should be flagged as low to inform the person interacting with the system.

Data: Inception to Publication & Beyond

This is a resource for all questions related to data sharing, collaboration, management, and publication.

Este repositório reúne a infraestrutura do site e os materiais didáticos utilizados na disciplina Ciência de Dados Aplicada à Engenharia de Produção da UFRGS. O conteúdo apresenta fundamentos, métodos e práticas da ciência de dados com foco em aplicações reais na engenharia de produção. Os exemplos e exercícios utilizam as linguagens R e Python, de forma complementar. O site é construído em Quarto e publicado automaticamente por meio do GitLab Pages.

Fundamental theory and practice in Machine Learning (ML) and Data Science (DS). 🧮

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

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 LARA python-django app for managing projects and experiments in lab automation systems and scientific laboratories.

The Restaurant Management System (RMS) is an integrated platform designed to streamline and automate restaurant operations. It combines POS, inventory, production costing, HR & attendance, employee management, accounting, and business intelligence into a single scalable solution.

The goal is to build a modern, data-driven system that supports multi-branch restaurants, enables accurate cost control, and provides real-time operational insights.

Core Features

Sales & POS: Order lifecycle, table management, waiter assignment, payment handling.

Inventory Management: Ingredient-level tracking, purchase orders, supplier management, stock movements, recipe-based auto-deduction.

Production & Costing: BOM/recipe setup, real cost calculation, batch production, COGS monitoring.

HR & Attendance: Employee roles, clock-in/out, break tracking, loans/advances, payroll logic.

Accounting Layer: Light GL engine, COA structure, linked transactions for sales, purchases, payroll, and inventory.

BI & Analytics: Dashboards for sales, costs, performance, forecasting, and data exports for external BI tools.

Technical Focus

Backend: Python / Django

Database: PostgreSQL with a clean ERD

Testing: Selenium, PyTest, automated workflows

Data & BI: Python analytics, Power BI dashboards

Architecture: Modular, scalable, multi-location support

Current Status

Business analysis completed

Initial UI prototype for Inventory done

Database design phase in progress

Parallel work on BI strategy and project structure

Vision

RMS aims to become a commercial-grade SaaS product with cloud/on-prem deployment, mobile apps for staff, AI-driven forecasting, and full operational analytics.

Bahn-Vorhersage - The best Train Delay Prediction System.

Mirrored to https://gitlab.b-data.ch/r/rsi and https://github.com/b-data/rsi.

Découverte de ce monde (Data / ML) via un projet perso. Basés sur des relevés météo de différentes sources, avec des outils comme Pandas, Dask, Spark, Polars, ..., du ML et du DL. Une couche de visualisation via PowerBI.

Mirrored to https://gitlab.b-data.ch/docker/deployments/jupyter and https://github.com/b-data/docker-deployment-jupyter.