Feeling inspired to write your first TDS post before the end of 2024? and, in many meaningful ways, for the data science, machine learning, and AI communities at large. We’d like to thank all of you—readers, authors, and followers—for your support, and for keeping us busy and engaged with your excellent contributions and comments.
Unlike in 2023, when a single event (ChatGPT’s launch just weeks before the beginning of the year) stopped everyone in their tracks and shaped conversations for months on end, this year we experienced a more cumulative and fragmented sense of transformation. Practitioners across industry and academia experimented with new tools and worked hard to find innovative ways to benefit from the rapid rise of LLMs; at the same time, they also had to navigate a challenging job market and a world where AI’s footprint inches ever closer to their own everyday workflows.
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To help you make sense of these developments, we published more than 3,500 articles this past year, including hundreds from first-time contributors. Our authors have an incredible knack for injecting their unique perspective into any topic they cover—from big questions and timely topics to more focused technical challenges—and we’re proud of every post we published in 2024.
Within this massive creative output, some articles manage to resonate particularly well with our readers, and we’re dedicating our final Variable edition to these: our most-read, -discussed, and -shared posts of the year. As you might expect, they cover a lot of ground, so we’ve decided to arrange them following the major themes we’ve detected this year: learning and building from scratch, RAG and AI agents, career growth, and breakthroughs and innovation.
We hope you enjoy exploring our 2024 highlights, and we wish you a relaxing end of the year — see you in January!
Learning and Building from Scratch
The most reliably popular type of TDS post is the one that teaches readers how to do or study something interesting and productive on their own, and with minimal prerequisites. This year is no exception—our three most-read articles of 2024 fall under this category.
demonstrates that anyone can get hands-on with AI.
offered one of the most accessible and engaging explainers you’ll ever find on this topic.
put together a streamlined roadmap for studying the fundamental building blocks of AI.
guides us deep into their underlying mathematical principles.
’s thorough introduction makes it clear how and why they’ve become so important.
, who walked us through an intensive—but accessible— curriculum to master the basics (and then some) of large language models.
RAG and AI Agents
Once the initial excitement surrounding LLMs settled (a bit), data and ML professionals realized that these powerful models aren’t all that useful out of the box. Retrieval-augmented generation and agentic AI rose to prominence in the past year as the two leading approaches that bridge the gap between the models’ potential and real-world value; they also ended up being our most covered technical topics in recent months.
published the definitive beginners’ guide to working with agents.
’s tutorial addresses the common need of answering complex queries on internal documents.
put together a comprehensive guide to the methods you can leverage to achieving that lofty goal.
outlines a dozen streamlined approaches for tackling some of the most common challenges practitioners face when implementing RAG.
is here to help with sharp insights on the tradeoffs to keep in mind.
Career Growth
Data science and machine learning career paths continue to evolve, and the need to adapt to this changing terrain can generate nontrivial amounts of stress for many professionals, whether they’re deep into their career or are just starting out. We love publishing personal reflections on this topic when they also offer readers pragmatic advice—here are four that stood out to us (and to our readers).
generously consolidated lessons based on a decade of work into actionable insights.
shows, with great opportunities for learning and growth.
’s candid account of his path into data science is a must-read.
offers practical tips on managing your data science education so that you set yourself on the right path.
Breakthroughs and Innovation
Staying up-to-date with cutting-edge research and new tools can feel overwhelming at times, which is why we have a particular soft spot for top-notch paper walkthroughs and primers on emerging libraries and models. Here are three such articles that particularly resonated with our audience.
’s explainer on a groundbreaking 2020 paper.
’s hands-on introduction is the perfect place to start exploring its possibilities.
made their ramifications and potential benefits for ML practitioners evident with this accessible primer.
Thank you for supporting the work of our authors in 2024! If writing for TDS is one of your goals for 2025, why not get started now? Don’t hesitate to was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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