Towards Data Science

The Machine Learning “Advent Calendar” Day 15: SVM in Excel

angela shi — Mon, 15 Dec 2025 19:41:01 +0000

Instead of starting with margins and geometry, this article builds the Support Vector Machine step by step from familiar models. By changing the loss function and reusing regularization, SVM appears naturally as a linear classifier trained by optimization. This perspective unifies logistic regression, SVM, and other linear models into a single, coherent framework.

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6 Technical Skills That Make You a Senior Data Scientist

Piero Paialunga — Mon, 15 Dec 2025 15:43:00 +0000

Beyond writing code, these are the design-level decisions, trade-offs, and habits that quietly separate senior data scientists from everyone else.

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Geospatial exploratory data analysis with GeoPandas and DuckDB

Thomas Reid — Mon, 15 Dec 2025 13:17:00 +0000

In this article, I’ll show you how to use two popular Python libraries to carry out some geospatial analysis of traffic accident data within the UK. I was a relatively early adopter of DuckDB, the fast OLAP database, after it became available, but only recently realised that, through an extension, it offered a large number […]

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Lessons Learned from Upgrading to LangChain 1.0 in Production

Clara Chong — Mon, 15 Dec 2025 10:30:00 +0000

What worked, what broke, and why I did it

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The Machine Learning “Advent Calendar” Day 14: Softmax Regression in Excel

angela shi — Sun, 14 Dec 2025 18:12:00 +0000

Softmax Regression is simply Logistic Regression extended to multiple classes.

By computing one linear score per class and normalizing them with Softmax, we obtain multiclass probabilities without changing the core logic.

The loss, the gradients, and the optimization remain the same.
Only the number of parallel scores increases.

Implemented in Excel, the model becomes transparent: you can see the scores, the probabilities, and how the coefficients evolve over time.

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The Skills That Bridge Technical Work and Business Impact

TDS Editors — Sun, 14 Dec 2025 14:30:29 +0000

In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Maria Mouschoutzi. Maria is a Data Analyst and Project Manager with a strong background in Operations Research, Mechanical […]

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Stop Writing Spaghetti if-else Chains: Parsing JSON with Python’s match-case

Eirik Berge — Sun, 14 Dec 2025 10:24:00 +0000

Introduction If you work in data science, data engineering, or as as a frontend/backend developer, you deal with JSON. For professionals, its basically only death, taxes, and JSON-parsing that is inevitable. The issue is that parsing JSON is often a serious pain. Whether you are pulling data from a REST API, parsing logs, or reading […]

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The Machine Learning “Advent Calendar” Day 13: LASSO and Ridge Regression in Excel

angela shi — Sat, 13 Dec 2025 16:56:00 +0000

Ridge and Lasso regression are often perceived as more complex versions of linear regression. In reality, the prediction model remains exactly the same. What changes is the training objective. By adding a penalty on the coefficients, regularization forces the model to choose more stable solutions, especially when features are correlated. Implementing Ridge and Lasso step by step in Excel makes this idea explicit: regularization does not add complexity, it adds preference.

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How to Increase Coding Iteration Speed

Eivind Kjosbakken — Sat, 13 Dec 2025 13:30:00 +0000

Learn how to become a more efficient programmer with local testing

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NeurIPS 2025 Best Paper Review: Qwen’s Systematic Exploration of Attention Gating

Sean Moran — Sat, 13 Dec 2025 10:16:00 +0000

This one little trick can bring about enhanced training stability, the use of larger learning rates and improved scaling properties

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The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel

angela shi — Fri, 12 Dec 2025 17:15:00 +0000

In this article, we rebuild Logistic Regression step by step directly in Excel.
Starting from a binary dataset, we explore why linear regression struggles as a classifier, how the logistic function fixes these issues, and how log-loss naturally appears from the likelihood.
With a transparent gradient-descent table, you can watch the model learn at each iteration—making the whole process intuitive, visual, and surprisingly satisfying.

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Decentralized Computation: The Hidden Principle Behind Deep Learning

Xiaocong Yang — Fri, 12 Dec 2025 15:47:00 +0000

Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep-learning-based AI models succeed because many simple units interact locally […]

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EDA in Public (Part 1): Cleaning and Exploring Sales Data with Pandas

Ibrahim Salami — Fri, 12 Dec 2025 13:20:00 +0000

Hey everyone! Welcome to the start of a major data journey that I’m calling “EDA in Public.” For those who know me, I believe the best way to learn anything is to tackle a real-world problem and share the entire messy process — including mistakes, victories, and everything in between. If you’ve been looking to level up […]

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Spectral Community Detection in Clinical Knowledge Graphs

Silvia Onofrei — Fri, 12 Dec 2025 10:30:00 +0000

Introduction How do we identify latent groups of patients in a large cohort? How can we find similarities among patients that go beyond the well-known comorbidity clusters associated with specific diseases? And more importantly, how can we extract quantitative signals that can be analyzed, compared, and reused across different clinical scenarios? The information associated to […]

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The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel

angela shi — Thu, 11 Dec 2025 16:31:00 +0000

Linear Regression looks simple, but it introduces the core ideas of modern machine learning: loss functions, optimization, gradients, scaling, and interpretation.
In this article, we rebuild Linear Regression in Excel, compare the closed-form solution with Gradient Descent, and see how the coefficients evolve step by step.
This foundation naturally leads to regularization, kernels, classification, and the dual view.
Linear Regression is not just a straight line, but the starting point for many models we will explore next in the Advent Calendar.

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Drawing Shapes with the Python Turtle Module

Mahnoor Javed — Thu, 11 Dec 2025 15:00:00 +0000

A step-by-step tutorial that explores the Python Turtle Module

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7 Pandas Performance Tricks Every Data Scientist Should Know

Benjamin Nweke — Thu, 11 Dec 2025 13:30:00 +0000

What I've learned about making Pandas faster after too many slow notebooks and frozen sessions

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How Agent Handoffs Work in Multi-Agent Systems

Kenneth Leung — Thu, 11 Dec 2025 12:00:00 +0000

Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph

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The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel

angela shi — Wed, 10 Dec 2025 16:30:00 +0000

DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point.
It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel.
But because it relies on one fixed radius, HDBSCAN is needed to make the method robust on real data.

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How to Maximize Agentic Memory for Continual Learning

Eivind Kjosbakken — Wed, 10 Dec 2025 15:00:00 +0000

Learn how to become an effective engineer with continual learning LLMs

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