Learning resources

Modelos de Regresión 🇪🇸

Víctor Aceña, Carmen Lancho & Isaac Martín (Jan. 2025)

urjcdslab.github.io/Regresion

An overview of regression models, including linear, variable selection, regularization, non-linear transformations, feature engineering, and generalized regression techniques, aimed at data science students.

Introducción al software estadístico R 🇪🇸

Emilio L. Cano (Jun. 2024)

www.lcano.com/b/iser/_book/

This book provides practical guidance on using software for statistical data analysis, aimed at helping companies and institutions leverage their data to maintain competitiveness in the digital age.

Inferencia Estadística 🇪🇸

Carmen Lancho, Víctor Aceña & Isaac Martín (May. 2024)

urjcdslab.github.io/InferenciaEstadistica

An exploration of statistical inference, covering key concepts and practical applications essential for data science and engineering students.

Source code is available at GitHub: github.com/URJCDSLab/InferenciaEstadistica.

The sources and PDF version are also published as open-source at BURJC: hdl.handle.net/10115/41304.

Fundamentos de ciencia de datos con R 🇪🇸

With contributions by Emilio L. Cano (Jan. 2024)

cdr-book.github.io/

This book guides readers through the complexities of data science, from understanding big data and ethical data governance to mastering statistical techniques and using R for practical applications in various fields.

Aprendizaje Automático 🇪🇸

Carmen Lancho & Isaac Martín (Dec. 2023)

urjcdslab.github.io/AprendizajeAutomaticoI

An introduction to the fundamentals of Machine Learning, focusing on equipping students with the skills to extract valuable insights from data and make informed decisions.

Source code is available at GitHub: github.com/URJCDSLab/AprendizajeAutomaticoI.

The sources and PDF version are also published as open-source at BURJC: hdl.handle.net/10115/27431.

Estadística Aplicada a las Ciencias y la Ingeniería 🇪🇸

Emilio L. Cano (Apr. 2023)

emilopezcano.github.io/estadistica-ciencias-ingenieria

This book covers the typical curriculum of Statistics and Probability courses for Science and Engineering degrees at Spanish universities, aiming to provide a comprehensive and accessible resource for both students and a broader audience.

Matemáticas Discreta y Álgebra 🇪🇸

With contributions by Marina Cuesta (Sep. 2022)

hdl.handle.net/10115/20345

A foundational synthesis of Discrete Mathematics and Linear Algebra as the bedrock for Computer Science and Cybersecurity.

Ciencia de datos para la ciberseguridad 🇪🇸

Alberto Fernández & Isaac Martín (Oct. 2020)

ra-ma.es/libro/ciencia-de-datos-para-la-ciberseguridad_115724/

This book presents Data Science as a tool for understanding, preventing, detecting, and addressing cybersecurity threats, aimed at professionals, students, engineers, and mathematicians.

Six Sigma with R 🇬🇧

Emilio L. Cano, Javier M. Moguerza & Andrés Redchuk (Aug. 2012)

www.sixsigmawithr.com

A comprehensive guide that integrates the Six Sigma methodology with the R programming language to improve processes and solve problems using statistical techniques.

Virtual Intelligent Tutor

github.com/URJCDSLab/ViLT

ViLT is a comprehensive framework that integrates an intelligent chatbot to support virtual tutoring for students across various degree programs at ETSII, URJC. It leverages large language models, LangChain, and RAG techniques to optimize natural language queries. The platform allows instructors to upload course materials in PDF format, manage user access for teachers and students, and collect feedback to continuously enhance its performance.

SixSigma

github.com/emilopezcano/SixSigma

Functions and utilities to perform Statistical Analyses in the Six Sigma way.

CSViz method

github.com/URJCDSLab/CSViz

Implementation of CSViz: Class Separability Visualization for High-Dimensional Datasets.

Read more about it at: doi.org/10.1007/s10489-023-05149-4

CPRViz method

github.com/URJCDSLab/CPRViz

Implementation of CPRViz: Consistent Probability Regions Visualization for model understanding.

KRAKEN-SND

github.com/URJCDSLab/KRAKEN-SND

Implementation of KRAKEN-SND: Knowledge Recovering Architecture based on Keywords Extraction from Narratives for Suspicious News Detection.

Read more about it at: doi.org/10.1016/j.engappai.2021.104230

MOE

github.com/URJCDSLab/moe

Implementation and validation of MOE, “Minimally overfitted learners: a general framework for ensemble learning”

Read more about it at: doi.org/10.1016/j.knosys.2022.109669

Hostility measure

github.com/URJCDSLab/Hostility_measure

Generator Python code for the artificial datasets and some results of the Hostility measure from paper “Hostility measure for multi-level study of data complexity”.

Read more about it at: doi.org/10.1007/s10489-022-03793-w

Experts perception-based system to detect misinformation in health websites (datasets)

github.com/URJCDSLab/expert-perception-based-system-data

Data used in the paper “Experts perception-based system to detect misinformation in health websites”.

Read more about it at: doi.org/10.1016/j.patrec.2021.11.008

Explanation sets

github.com/URJCDSLab/​explanation_sets_experiments

Implementation and evaluation of “Explanation sets: A general framework for machine learning explainability”.

Read more about it at: doi.org/10.1016/j.ins.2022.10.084

Cybersecurity Risk Analysis and Simulation 🇬🇧

Emilio L. Cano & Javier Sánchez (Nov. 2024)

gondor.etsii.urjc.es

An evolution of the Open FAIR™ Risk Analysis Tool, redesigned as an interactive Shiny application to simplify and enhance Cyber Risk Simulation for cybersecurity students and professionals. It allows users to easily generate simulations using customizable statistical distributions, visualize results dynamically, and export simulated data for further analysis in external tools. Tailored for training and educational purposes, it provides a more accessible and hands-on approach to learning cyber risk modeling.

You can view the slides presenting the tool at useR! 2024 in Salzburg at lcano.com/p/user2024.

Data Explorer Assistant 🇬🇧

Carmen Lancho & Isaac Martín (Dec. 2023)

dslab-urjc.shinyapps.io/DEA_ML

Interactive app developed so that readers of book “Aprendizaje Automático” can put their knowledge into practice with tasks such as reading and cleaning data, EDA or working with models in an easy and interactive way.

The source code is also published as open-source at BURJC: hdl.handle.net/10115/27461.

Inferencia Estadística 🇪🇸

Carmen Lancho, Víctor Aceña & Isaac Martín (Nov. 2024)

hdl.handle.net/10115/41375

Slides from the book “Inferencia Estadística”.

Aprendizaje Automático 🇪🇸

Carmen Lancho & Isaac Martín (Dec. 2023)

hdl.handle.net/10115/27430

Slides from the book “Aprendizaje Automático”.

Introduction to Git (Seminar) 🇪🇸

With contributions by Elena García-Morato & Felipe Ortega (Jul. 2023)

github.com/URJCDSLab/Seminar-Intro-Git

Materials for the teaching innovation seminar “Introduction to Git” in the DSLAB-TI series.

Unsupervised Analysis (Short course) 🇪🇸

Isaac Martín (Jan. 2023)

github.com/URJCDSLab/​Short_Courses/​tree/​main/​Clustering

Materials for the “Unsupervised Analysis” Short Course.

Exploratory Data Analysis (Short course) 🇪🇸

Isaac Martín (Nov. 2022)

github.com/URJCDSLab/​Short_Courses/​tree/​main/​EDA

Materials for the “Exploratory Data Analysis” Short Course.

Inferencia Estadística (Labs) 🇪🇸

Carmen Lancho, Víctor Aceña & Isaac Martín (Nov. 2024)

github.com/URJCDSLab/​LaboratoriosRInferenciaEstadistica

Laboratories for the book “Inferencia Estadística”, including both the statements and their solutions in multiple readable formats, such as HTML and PDF. Used data and R sources are also included.

The content is also published as open-source at BURJC: hdl.handle.net/10115/41411.

Inferencia Estadística 🇪🇸

Carmen Lancho, Víctor Aceña & Isaac Martín (Nov. 2024)

github.com/URJCDSLab/​Ejercicios_InferenciaEstadistica

R scripts with exercises for the book “Inferencia Estadística”, including both the problems and their solutions.

The exercises and solutions are also published as open-source at BURJC: hdl.handle.net/10115/41413.

Aprendizaje Automático 🇪🇸

Carmen Lancho & Isaac Martín (Dec. 2023)

github.com/URJCDSLab/​EjerciciosAprendizajeAutomatico

R scripts with exercises for the book “Aprendizaje Automático”, including both the problems and their solutions.

The exercises and solutions are also published as open-source at BURJC: hdl.handle.net/10115/27432.