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Feeling inspired to write your first TDS post before the end of 2024? We’re always open to contributions from new authors.

And just like that, 2024 is (almost) in the books. It was a year of exciting transitions — both for the TDS team 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.

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.

• 5 AI Projects You Can Build This Weekend (with Python)
  From beginner-friendly to advanced project ideas, Shaw Talebi demonstrates that anyone can get hands-on with AI.
• Understanding LLMs from Scratch Using Middle School Math
  How do LLMs work? Rohit Patel offered one of the most accessible and engaging explainers you’ll ever find on this topic.
• How to Learn AI on Your Own (A Self-Study Guide)
  For the self-starters out there, Thu Vu put together a streamlined roadmap for studying the fundamental building blocks of AI.
• The Math Behind Neural Networks
  To understand neural networks, “the backbone of modern AI,” Cristian Leo guides us deep into their underlying mathematical principles.
• Text Embeddings: Comprehensive Guide
  Embeddings make the magic of LLMs possible, and Mariya Mansurova’s thorough introduction makes it clear how and why they’ve become so important.
• How I Studied LLMs in Two Weeks: A Comprehensive Roadmap
  Another excellent learning resource came to us from Hesam Sheikh, 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.

• Intro to LLM Agents with LangChain: When RAG Is Not Enough
  Back in March—and quite ahead of the curve—Alex Honchar published the definitive beginners’ guide to working with agents.
• Using LangChain ReAct Agents for Answering Multi-Hop Questions in RAG Systems
  Showing us how agents and RAG can complement each other, Dr. Varshita Sher’s tutorial addresses the common need of answering complex queries on internal documents.
• 17 (Advanced) RAG Techniques to Turn Your LLM App Prototype into a Production-Ready Solution
  Building a rudimentary RAG pipeline is one thing; optimizing it so that it can actually work in a business context is another. Dominik Polzer put together a comprehensive guide to the methods you can leverage to achieving that lofty goal.
• 12 RAG Pain Points and Proposed Solutions
  On a similar troubleshooting beat, Wenqi Glantz outlines a dozen streamlined approaches for tackling some of the most common challenges practitioners face when implementing RAG.
• Choosing Between LLM Agent Frameworks
  It can be tough to make informed choices in an ecosystem where both major and emerging players release new tools every day. Aparna Dhinakaran 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).

• What 10 Years at Uber, Meta, and Startups Taught Me About Data Analytics
  From the importance of storytelling and business acumen to the limitations of metrics, Torsten Walbaum generously consolidated lessons based on a decade of work into actionable insights.
• What I Learned in my First 3 Months as a Freelance Data Scientist
  Career switches are always tricky, and moving away from the structure of working at a company to the world of self-employment comes with its own set of challenges—and, as CJ Sullivan shows, with great opportunities for learning and growth.
• How I Became A Data Scientist — No CS Degree, No Bootcamp
  For anyone just taking their first steps in the field, Egor Howell’s candid account of his path into data science is a must-read.
• I Spent $96k To Become a Data Scientist. Here Are 5 Crucial Lessons All Beginners Must Know
  Offering a different perspective on entering the discipline, Khouloud El Alami 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.

• A New Coefficient of Correlation
  “What if you were told there exists a new way to measure the relationship between two variables just like correlation except possibly better”? So starts Tim Sumner’s explainer on a groundbreaking 2020 paper.
• Intro to DSPy: Goodbye Prompting, Hello Programming!
  In another exciting year for open-source tools, one of the standout new arrivals was DSPy, which aims to open up LLMs for programmers and make it easier to build modular AI solutions. Leonie Monigatti’s hands-on introduction is the perfect place to start exploring its possibilities.
• Kolmogorov-Arnold Networks: The Latest Advance in Neural Networks, Simply Explained
  KANs, “promising alternatives of Multi-Layer Perceptrons (MLPs),” made a splashy entrance last spring; Theo Wolf 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 share your work with us.

Until the next Variable, coming your way in the first week of January,

TDS Team

2024 Highlights: The AI and Data Science Articles That Made a Splash was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.