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It’s been exciting to see so many TDS authors dive headfirst into Fall, sharing hands-on, actionable insights on topics ranging from cutting-edge (agents, MCP…) to evergreen (Python skills and machine learning engineering, to name a few).

Equally thrilling? To see our September articles resonate with so many readers, who then share them far and wide. Join us as we celebrate our most-read stories of the past month — they cover a broad spectrum of data science, ML, and AI themes, so you’re bound to discover something relevant to your current interests.

How to Become a Machine Learning Engineer (Step-by-Step)

Egor Howell has made it his specialty to create guides for aspiring data and ML practitioners — and their success proves how eager job seekers are for honest, pragmatic, and detailed advice. His latest, on the (increasingly popular) machine learning engineer career path, is another resource that should go straight to your bookmarks.

Implementing the Coffee Machine in Python

Programming basics explained with an engaging twist? Yes, please! Mahnoor Javed’s latest Python tutorial covers conditional statements, loops, and dictionaries.

Python Can Now Call Mojo

A second Python post zoomed all the way to the top of our best-performing articles this month: Thomas Reid’s comprehensive, example-filled guide to boosting your runtime with a healthy dose of Mojo code.

Author Spotlights

TDS contributors are embedded across industries, disciplines, and organization types, so chatting with them gives us — and you — an unfiltered view of life at the forefront of data science and AI. Here are two recent Q&As you shouldn’t miss.

Other September Highlights

Don’t miss our other top reads from September, tackling some of the most buzz-generating tools, concepts, and workflows of the moment.

• Using LangGraph and MCP Servers to Create My Own Voice Assistant, by Benjamin Lee

• The End-to-End Data Scientist’s Prompt Playbook, by Sara Nobrega

• Creating and Deploying an MCP Server from Scratch, by Vyacheslav Efimov

• Building Research Agents for Tech Insights, by Ida Silfverskiöld

• My Experiments with NotebookLM for Teaching, by Parul Pandey

• Why Context Is the New Currency in AI: From RAG to Context Engineering, by Sudheer Singamsetty

Meet Our New Authors

The latest cohort of TDS contributors has done a fantastic job translating innovative work into engaging and accessible articles. 

• Iva Pezo explores AI’s potential to make the resource-intensive process of fact-checking faster, scalable, and more reliable.
• Sruly Rosenblat and coauthors Ilan Strauss, Isobel Moure, and Tim O’Reilly take a close look at the emerging AI developer ecosystem and the rise of MCP.
• Karol Struniawski (along with Antoni Olbrysz and Tomasz Wierzbicki) presents an image recognition project at the intersection of computer vision, ecology, and biotechnology.

We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?

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