LLM Evaluation, AI Side Projects, User-Friendly Data Tables, and Other October Must-Reads

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

We seem to be in that sweet spot on the calendar between the end of summer and the final rush before things slow down for the holiday season—in other words, it’s the perfect time of year for learning, tinkering, and exploration.

Our most-read articles from October reflect this spirit of focused energy, covering a slew of hands-on topics. From actionable AI project ideas and data science revenue streams to accessible guides on time-series analysis and LLMs, these stories do a great job representing the breadth of our authors’ expertise and the diversity of their (and our readers’) interests. If you haven’t read them yet, what better time than now?

Monthly Highlights

• 5 AI Projects You Can Build This Weekend (with Python). If your sleeves aren’t rolled up just yet, they will be shortly: our most-read post in October, from Shaw Talebi , outlines several compelling project ideas for anyone who’s been thinking about putting their AI knowledge into action. From resume organizers to a multimodal search tool, they offer a smooth entryway into the ever-expanding world of AI-powered product development.
• Who Really Owns the Airbnbs You’re Booking??—?Marketing Perception vs Data Analytics Reality. If you’re looking to sink your teeth into an interesting data-analysis case study, Anna Gordun-Peiro ’s latest article fits the bill. Based on publicly available data, it digs into Airbnb ownership patterns, and shows readers how they can execute a similar investigation for the city of their choice.
• LLM Evaluation Skills Are Easy to Pick Up (Yet Costly to Practice). Creating LLM solutions requires a major investment of time and resources, which makes it crucial for product managers and ML engineers to get a clear and accurate sense of their performance. Thuwarakesh Murallie walks us through the nitty-gritty details of leveraging several evaluation approaches and tools to achieve that often-elusive goal.

• Top 5 Principles for Building User-Friendly Data Tables. “There are numerous times I wonder, ‘What does this column mean?’ ‘Why are there two columns with the same name in table A and table B? Which one should I use?’” Yu Dong introduces five useful rules that will ensure your data tables are accessible, usable, and easily interpretable for teammates and other stakeholders.
• How I Studied LLMs in Two Weeks: A Comprehensive Roadmap. While you might think that LLMs have been inescapable for the past couple of years, many practitioners?—?both new and seasoned?—?are just beginning to tune in to this buzzing topic; for a structured approach to learning all the basics (and then some), head right over to Hesam Sheikh Hassani ’s well-received curriculum.
• Understanding LLMs from Scratch Using Middle School Math. If you could use a more guided method to learn about large language models from the ground up, give Rohit Patel ’s debut TDS contribution a try: it’s a comprehensive, 40-minute explainer on these models’ inner workings—and requires no advanced math or machine learning knowledge.
• 5 Must-Know Techniques for Mastering Time-Series Analysis. From data splitting and cross-validation to feature engineering, Sara Nóbrega ’s recent deep dive zooms in on the fundamental workflows you need to master to conduct effective time-series analysis.
• AI Agents: The Intersection of Tool Calling and Reasoning in Generative AI. Few topics in recent months have generated as much buzz as AI agents; if you’d like to deepen your understanding of their potential (and limitations), don’t miss Tula Masterman ’s lucid overview, which focuses on how agent reasoning is expressed through tool calling, explores some of the challenges agents face with tool use, and covers common ways to evaluate their tool-calling ability.
• My 7 Sources of Income as a Data Scientist. Most (all?) data professionals know about the perks of working full time at a tech giant, but the options for monetizing your skills are much broader than that. Egor Howell provides a candid breakdown of the various revenue streams he’s cultivated in the past few years since becoming a full-time data scientist.

Our latest cohort of new?authors

Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you’re looking for new writers to explore and follow, just browse the work of our latest additions, including David Foutch, Robin von Malottki, Ruth Crasto, Stéphane Derosiaux, Rodrigo Nader, Tezan Sahu, Robson Tigre, Charles Ide, Aamir Mushir Khan, Aneesh Naik, Alex Held, caleb lee, Benjamin Bodner, Vignesh Baskaran, Ingo Nowitzky, Trupti Bavalatti, Sarah Lea, Felix Germaine, Marc Polizzi, Aymeric Floyrac, Bárbara A. Cancino, Hattie Biddlecombe, Carlo Peron, Minda Myers, Marc Linder, Akash Mukherjee, Jake Minns, Leandro Magga, Jack Vanlightly, Rohit Patel, Ben Hagag, Lucas See, Max Shap, Fhilipus Mahendra, Prakhar Ganesh, and Maxime Jabarian.

Thank you for supporting the work of our authors! 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, don’t hesitate to share it with us.

Until the next Variable,

TDS Team