Category: Data Science

• Sketulate: Sketchable Functions & Densities for Data Science

  

  I simulate a lot of data to test my ideas, particularly the more complex ones. With non-standard stuff, it can be pretty time consuming to find the right function and/or parameters to do what I want. If the shape you want isn’t a common function or distribution, you can spend too much time searching for…

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  August 27, 2025

  Data Science

  Data Science, experimentation, Machine Learning, Statistics

• Calibration Plots with Generalized Additive Models (GAMs)

  

  Calibration of probabilistic predictions is a crucial task in many machine learning applications, especially when the outputs are used downstream for decision-making. It’s actually a simple concept, with calibration just ensuring that the predicted probabilities align with the actual outcomes. Despite that, I’ve noticed many people neglect the step in their modelling process! Typically, when…

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  July 2, 2025

  Data Science

  Data Science, Machine Learning

• XGBoost Can’t Extrapolate

  

  A common pattern that I observe among inexperienced Data Scientists is the following – they often default to XGBoost or a similar Gradient Boosted Model for their problem with any thought as to whether its the right choice for the job. Given how powerful these methods are, this isn’t the most egregious mistake one can…

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  April 21, 2025

  Data Science, Machine Learning

  Data Science, Machine Learning

• Learn How to Containerize Your R Notebooks with Docker

  

  In this article I show how to containerize your R Notebooks with Docker, for reproducible sharing, using renv for dependency management.

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  March 4, 2024

  Data Engineering, Data Science

  Data Engineering, Data Science, deployment, reproducibility

• Small Data: Creativity, Explainability & Precision

  

  This post discusses the significance of small data in the context of data science and modeling, detailing tips and tricks for working with small data sets.

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  February 8, 2024

  Data Science

  Data Science, Machine Learning

• Bayesian Time Series Interpolation

  

  The third article in the series, I explore Bayesian modeling for multivariate time series interpolation with hierarchical models with Numpyro and Bambi.

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  January 14, 2024

  Data Science, Machine Learning, Statistics, Time Series

  bambi, bayesian, numpyro, Time Series

• Genetic Algorithms with PyGAD and PyTorch

  

  Deep dive into Genetic Algorithms (GAs), an optimization algorithm inspired by the concept of natural evolution, including using a GA to train a Pytorch model with the Pygad library.

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  December 16, 2023

  Data Science, Machine Learning

  Data Science, Machine Learning, Statistics

• How to Choose a Distribution for your Regression Model

  

  This article is all about distributions! In it, I explore the most common distributions including Gaussian, Uniform, Student-T, Gamma, and others. I also discuss their applications and when to choose them for regression modelling.

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  December 4, 2023

  Data Science, Statistics

  Data Science, Statistics

• Heteroscedastic Data – Part 2: Inference

  

  In this article, I deal with complex heteroscedastic problems for data analysis and inference. With a simulated dataset to demonstrate, I tackle issues such as strong trends, non-linearity, non-normal target distributions, multiple seasonalities, and changing variance. I use the advanced techniques of Bayesian Spline Modelling and Bayesian Additive Regression Trees (BART), emphasizing their efficacy in…

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  March 27, 2023

  Data Science, Statistics

• Heteroscedastic Regression

  

  In this article, I deal with complex heteroscedastic problems for data analysis and inference. With a simulated dataset to demonstrate, I tackle issues such as strong trends, non-linearity, non-normal target distributions, multiple seasonalities, and changing variance. I use the advanced techniques of Bayesian Spline Modelling and Bayesian Additive Regression Trees (BART), emphasizing their efficacy in…

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  March 27, 2023

  Data Science, Statistics

• Mutual Information: a powerful alternative to correlation

  

  While Pearson Correlation is effective in identifying relationships during exploratory data analysis, Mutual Information (MI) offers a powerful alternative, capable of uncovering non-linear relationships between variables. Despite being harder to interpret, its implementation in Scikitlearn is worth exploring.

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  March 9, 2023

  Data Science, Statistics

• Mutual Information

  

  While Pearson Correlation is effective in identifying relationships during exploratory data analysis, Mutual Information (MI) offers a powerful alternative, capable of uncovering non-linear relationships between variables. Despite being harder to interpret, its implementation in Scikitlearn is worth exploring.

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  March 9, 2023

  Data Science, Statistics

• Working with Heteroscedastic Data

  

  Heteroscedasticity, variance inconsistency across a related variable’s range, is often overlooked in data analysis. The two-part series explores handling heteroscedastic data through transformations, linear models, and model-based solutions, using LinkedIn engagements for instance. Techniques include Quantile Regression, Conditional Variance model, and common transformations like power or square root transformations.

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  January 25, 2023

  Data Science

  Data Science