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Could it really be the end of another year? We’ve been publishing annual recaps for a long time, and this final stretch still somehow sneaks up on us.
Every year feels hectic, of course. But we’re pretty sure that we’ve never been busier — in the best possible ways! — than in 2025, whether it’s rebuilding TDS as an independent publication, iterating on our Author Payment Program, or staying as determined as ever to publish the best articles by data science, ML, and AI experts.
At the end of such a monumental year, we’re thrilled to highlight the stories that stood out among the hundreds we’ve published, and our celebration of 2025 must-reads reflects the diversity of topics and experiences our authors cover.
Every year, a fresh, exciting set of tools inevitably captures practitioners’ imagination; after LLMs in 2023 and RAG in 2024, this has been, without a question, the Year of the Agent (and complementary frameworks like MCP and contextual engineering).
Beyond agentic AI, Python continued its long reign as an essential programming language, and our authors and readers alike zoomed in on keeping their skills up-to-date in a competitive job market.
Before we explore the articles that resonated the most with our readers this year, we’d like to take a moment to thank you for trusting us with your curiosity, and our authors for joining us in our new home.
Agents, Agents, and More Agents
While agentic AI wasn’t quite new this year — it already popped up as a trending topic in our previous annual recap! — its reach and mainstream status grew exponentially. Here are the top stories on the signature technology of 2025.
How to Design My First AI Agent
In 2025, everybody working in, around, or with AI wanted to know what agents are — and how to leverage their power. Fabiana Clemente’s blockbuster tutorial responded to this need with clear, actionable guidelines that can be customized for specific contexts and use cases.
A Developer’s Guide to Building Scalable AI: Workflows vs Agents
A masterful deep dive from Hailey Quach, who unpacks the tradeoffs between autonomous agents and orchestrated workflows and the situations in which agents are called for.
LangGraph 101: Let’s Build A Deep Research Agent
As Shuai Guo explains, “Building LLM agents that actually work in practice is not an easy task” — which explains why an accessible guide like this one resonated with so many readers.
Agentic AI: Single vs Multi-Agent Systems
AI agents come in various flavors, and we can use them for a wide range of tasks. Ida Silfverskiöld focuses on one key distinction to be mindful of.
AI Agents from Scratch: Single Agents
Relying solely on Python and Ollama, Mauro Di Pietro shared one of last year’s standout hands-on tutorials, showing us how to build a functional agent.
Must-Have Skills, Career Growth, and Other Trending Topics
From the latest buzz-generating concepts to the evergreen areas that keep data and ML professionals competitive in a challenging job landscape, our top authors covered an impressive range of topics and questions.
How to Become a Machine Learning Engineer (Step-by-Step), by Egor Howell
Our most-read article of 2025 presents a one-stop guide to becoming a machine learning engineer.
I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy, by Claudia Ng
Complete guide to feature selection, threshold optimization, and neural network architecture for ML competitions.
Advanced Prompt Engineering for Data Science Projects, by Sara Nobrega
In part 2 of Sara’s popular series, we learn all about prompt engineering for features, modeling, and evaluation.
Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI, by Steve Hedden
How retrieval-augmented generation is evolving from static pipelines to governed, context-aware systems that make AI more explainable, trustworthy, and scalable.
Model Context Protocol (MCP) Tutorial: Build Your First MCP Server in 6 Steps, by Destin Gong
A step-by-step guide to develop a custom code-to-diagram MCP server.
Python, Forever
Trends and buzzwords come and go, but the ability to code in Python is as relevant as ever. Here are our top Python-focused reads of the past year.
Building A Modern Dashboard with Python and Taipy, by Thomas Reid
From an author who’s published many a viral Python article on TDS (be sure to revisit some of Tom’s other stories!) comes a guide to building a front-end data application.
How We Reduced LLM Costs by 90% with 5 Lines of Code, by Uri Peled
When clean code hides inefficiencies: what we learned from fixing a few lines of code and saving 90% in LLM cost.
Implementing the Coffee Machine in Python, by Mahnoor Javed
A beginner-friendly, step-by-step guide to coding a coffee maker (and learning about conditional statements, loops, and Python dictionaries along the way).
Contribute to TDS
Thinking about drafting a new article over the holidays? We’d love to read it. Don’t hesitate to send it our way.