This is a collection of articles, experiments, and projects that I enjoy exploring and taking notes on—spanning statistics, machine learning, economics, mathematics, and other fields that catch my curiosity. Some of these will be more discursive, some more computational, and others more mathematical, but each piece tries to maintain an applied approach to make explanations clearer.
As J.R.R. Tolkien once wrote:
"It is not our part to master all the tides of the world, but to do what is in us [...] uprooting the evil in the fields that we know, so that those who live after may have clean earth to till."
Sharing these notes and striving for clarity is simply my own way of trying to leave behind a clean, cultivated earth for whoever comes next.
From manual workarounds to a first-class language feature: how Python went from 15 lines of boilerplate to one keyword across 3.4–3.15. Includes benchmarks, PEP history, and code patterns for every version.
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An autonomous AI coding agent embedded directly into your JetBrains IDE. Multi-agent architecture with offline-first local LLM and cloud fallback.
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Sensing the Pattern: Multi-method anomaly detection for tabular and time series data using consensus voting. Narrated through the lore of The Wheel of Time.
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A complete guide to the Model Context Protocol — the Universal Translator for AI systems. Build your own MCP Server, Starfleet Academy style.
1. First Contact: What is the Model Context Protocol
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2. Activating the Ship Computer with Ollama
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3. Initializing the Warp Core: Server Setup
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4. Sensor Array Active: Exposing Resources
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5. Armory and Replicators: Creating Tools
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6. Subspace Communications: Managing Prompts
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7. The Kobayashi Maru: Testing and Deployment
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Advanced Cardinality Estimation for Privacy-Preserving Analytics.
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The Astromech Arm for Your Python Projects. May the code be with you.
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A complete pytest course, Hogwarts-style. From marks to plugins, learn to protect your code from the Dementors of Production.
1. The Time-Turner: Marks & Parametrization
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2. The Room of Requirement: Fixtures Basics
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3. The Room of Requirement: Advanced Magic
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4. Ministry-Issued Spells: Built-in Fixtures
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5. Riddikulus! Defeating the Boggart of Dependencies
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6. Lumos! Illuminating Failing Tests
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7. Weasleys' Wizard Wheezes: The Plugin Ecosystem
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8. The Imperius Curse: Property-Based Testing with Hypothesis
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9. Becoming a Wandmaker: Writing Custom Plugins
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0. Introduction to geostatistical data.
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1. Introduction to spatial statistics
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2. Spatial data in R
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3. Spatial Point Processes: The Poisson process
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4. Spatial Point Processes: The test for CSR
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5. Spatial Point Processes: Estimation of the intensity function
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6. Introduction to Geostatistics, large and small scale variability.
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7. Geostatistical Model.
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8. Exploratory analysis EDA and ESDA.
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9. Theoretical introduction to spatial prediction and kriging and application in R on Ordinary and Universal Kriging.
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Summary of the steps of an analysis, from data to prediction.
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What is data science.
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Record Linkage and Knowledge Discovery
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Best subset, stepwise methods, information criteria (AIC, BIC), filter/wrapper/embedded approaches.
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Quantifying feature contributions: model-specific methods, permutation importance, SHAP values, and drop-column importance.
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1. Introduction to State Space models
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2. Introduction to UCM models in State Space form with the KFAS library
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3. State Space models estimation of unknown parameters
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4. Application of State Space models estimation of unknown parameters and auxiliary residuals
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5. Forecasting, filtering and smoothing
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Convolutional neural networks: definition and elements.
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A Python package to speed up any image and text tagging procedures.
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Least Absolute Shrinkage and Selection Operator: L1 sparsity, coordinate descent, regularization path, and variants.
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L2 regularization: closed-form solution, SVD interpretation, multicollinearity, Bayesian connection.
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Ridge (L2), LASSO (L1), and Elastic Net regularization. Bias-variance tradeoff and choosing lambda via cross-validation.
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The measures underlying Network analysis in R
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The measures underlying Network analysis in Python
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Download dataset from_to.csv
Download dataset info.csv
Work in progress: Dynamic network representation
Work in progress: Cluster and community analysis
This article explains in an introductory way the concept of neural network and introduces the Multi-Layer Perceptron (MLP)
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Web Scraping procedures with R and Python to get emails from the Yellow Pages website.
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K-Means, hierarchical clustering, DBSCAN. Distance measures, silhouette scores, and practical guidelines.
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LDA, QDA, Fisher's criterion, regularized discriminant analysis, and connections to logistic regression.
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Principal Component Analysis: eigenvalue decomposition, SVD, proportion of variance explained, loadings, and biplots.
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It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models non-linearities and interactions between variables
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Four basic components of these models: Trend, Cycle, Seasonality, Noise (WN).
Also included a small insight on the use of regressors in these models.
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The multiple testing problem.
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Boosting is a machine learning technique that falls into the ensemble learning category. The idea of Boosting is to combine "weak" classifiers in order to create a classifier with better accuracy.
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Stacked models.
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Using Machine Learning, Deep Learning and Ensemble Learning to create a bin that understands waste type through image recognition and sensor data.
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Ensemble method combining bagging with random feature selection for decorrelation. OOB error, feature importance.
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Non-parametric supervised learning with recursive binary splitting, Gini index, entropy, and cost-complexity pruning.
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Bootstrap Aggregating: reducing variance of high-variance models through averaging bootstrap replications.
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Bootstrap (parametric and nonparametric), Jackknife, bootstrap for regression, and bootstrap confidence intervals (Normal, Studentized, Basic, Percentile, BC, BCa).
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The three main learning paradigms: supervised (classification, regression), unsupervised (clustering, dimensionality reduction), and reinforcement learning.
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Introduction to nonparametric methods: sign test, Wilcoxon signed-rank test, McNemar test, and permutation tests with the Conditional Monte Carlo algorithm.
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Introduction to the Bayesian approach
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Prior selection:
a) Direct assignment
b) Conjugate distributions to the Model
c) Non-informative distributions
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Hyperparameter selection methods
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Predictive inference with the Bayesian approach:
a) Point estimation
b) Interval estimation
c) Hypothesis testing
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Posterior synthesis
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Correlation vs causality, causal vs predictive models, Rubin's causal model, Difference-in-Differences, Matching, Regression Discontinuity, Instrumental Variables, and Structural Models.
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From classical statistics to Machine Learning evolution of data.
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Analyzing which risk factors most influence healing time and which factors can determine non-healing of an injury.
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Stationarity and non-stationarity of a time series
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Multivariate stochastic processes: VARMA and VAR models
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Cointegration
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Correlation vs causality
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Stochastic processes, Markov property, discrete-time Markov chains, Chapman-Kolmogorov equations, state classification, limiting distributions, and the Gambler's Ruin problem.
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Forecasting theory
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Ames House Price prediction
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OK Cupid profile analysis
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Bike sharing demand prediction
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Fisher's exact test is a hypothesis testing test used in non-parametric statistics in situations with two dichotomous nominal variables.
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Exploratory analysis of the film industry to identify interesting elements.
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This article covers the Monte Carlo Method, also analyzing variance reduction methods such as: Control variables method and antithetic variables method.
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This article covers the basic methods of random and pseudo-random number generation.
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A Bayesian model to predict football match results using 25 years of data from 5 European leagues, combined with Machine Learning to identify high-confidence predictions.
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Likelihood, sufficiency, exponential family, Fisher information, Cramér-Rao inequality, Rao-Blackwell theorem, Lehmann-Scheffé theorem, MLE, Delta method, and Wald test.
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Psychometrics and the Myers Briggs personality test as used in market analysis
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Schematic notions on the steps in market research
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Multi-attribute attitude model: A = sum(b_i * e_i). Importance-Performance Analysis matrix and Theory of Reasoned Action.
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Psychometric scaling from paired comparisons: Thurstone's law, Z-score transformation, and interval scale construction.
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SQL (Structured Query Language) is a language for interacting with data stored in a relational database. A collection of articles on querying and managing relational data.
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Excel is a very popular tool but many of its functions are often unknown to the average user. Articles to consolidate knowledge on advanced Excel topics.
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Game theory applied to streaming platform competition (Netflix vs Infinity TV): Salop circle model, targeted advertising, privacy costs, and market share equilibrium.
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Economic systems, Pareto efficiency, Walrasian equilibrium, welfare theorems, market failures, financial crises (1929 vs 2008), and policy instruments.
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From antiquity to modern economics: Mercantilism, Physiocracy, Adam Smith, Ricardo, Marx, Marginalist Revolution, Keynes, Monetarism, and modern synthesis.
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The ILVA steelworks case study: history of Italy's largest steel plant, environmental crisis, constitutional court balancing of rights (work vs health), and the re-engineering plan.
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