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We seem to be in that sweet spot on the calendar between the end of summer and the final rush before things slow down during the holiday season; In other words, it's the perfect time of year to learn, experiment and explore.
Our most read articles for October reflect this spirit of focused energy and cover a wealth of practical topics. From practical ai project ideas and data science revenue streams to accessible guides on time series analysis and LLM, these stories do a great job of representing the breadth of our authors' experience and the diversity of their interests (and those of our readers). If you haven't read them yet, what better time than now?
Monthly highlights
- 5 ai projects you can create this weekend (with Python)
If you're not already rolling up your sleeves, you will be soon: our most-read post for October, from Shaw Talebioutlines several compelling project ideas for anyone who has been thinking about putting their ai knowledge into practice. From resume organizers to a multi-modal search tool, they offer an easy entry into the ever-expanding world of ai-powered product development. - Who really owns the Airbnbs you are booking? — Marketing perception versus data analysis reality
If you're looking to sink your teeth into an interesting data analytics case study, Anna Gordon PeiróThe last article fits perfectly. Based on publicly available data, it delves into Airbnb ownership patterns and shows readers how they can run similar research for the city of their choice. - LLM assessment skills are easy to acquire (but expensive to practice)
Building LLM solutions requires a large investment of time and resources, making it critical for product managers and ML engineers to get a clear and accurate picture of their performance. Murallie Thuwarakesh guides us through the nitty-gritty details of leveraging various assessment approaches and tools to achieve that often elusive goal.
- Top 5 Principles for Creating User-Friendly Data Tables
“I often ask myself, '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 presents five useful rules that will ensure your data tables are accessible, usable, and easily interpretable by teammates and other interested parties. - How I studied LLM in two weeks: a complete roadmap
While you might think that LLMs have been inescapable for the last few years, many professionals, both new and experienced, are just beginning to tune in to this hot topic; For a structured approach to learning all the basics (and more), head to Hesam SheikhThe well received study plan. - Understanding LLMs from the ground up using high school mathematics
If you could use a more guided approach to learning about large language models from scratch, please provide Rohit PatelTry it with TDS's first contribution – it's a comprehensive 40-minute explanation of the inner workings of these models and doesn't require advanced math or machine learning knowledge. - Five essential techniques to master time series analysis
From data splitting and cross-validation to feature engineering, Sara NobregaThe recent deep dive focuses on the fundamental workflows you need to master to perform 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 want to deepen your understanding of your potential (and limitations), don't miss Tula MasterThis insightful overview, which focuses on how agents' 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. tools. - My 7 sources of income as a data scientist
Most (all?) data professionals know the benefits 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 income streams he has cultivated over 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 new 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, simply explore the work of our latest additions, including David Foutch, Robin von Malottki, Ruth Crasto, Stephane Derosiaux, Rodrigo Nader, Tezan Sahu, Robson Tiger, Carlos Ide, Aamir Mushir Khan, Aneesh Naik, Alex Held, caleb lee, Benjamin Bodner, Vignesh Baskaran, Ingo Nowitzki, Trupti Bavalatti, Sara Lea, Felix Germaine, Marc Polizzi, Aymeric Floyrac, Barbara A. Cancino, Hattie Biddlecombe, Carlo Peron, the mind of myers, Mark Linder, Akash Mukherjee, jake remembers, Leandro Magga, Jack Van Lightly, Rohit Patel, Ben Hagag, luke see, Max Shap, Philip Mahendra, Prakhar Ganeshaand Maxime Jabarian.
Thank you for supporting the work of our authors! We love publishing articles by new authors, so if you have recently written an interesting tour of a project, a tutorial or a theoretical reflection on any of our main topics, do not hesitate to write to us. share it with us.
Until the next variable,
TDS Team
LLM Assessment, ai Side Projects, Easy-To-Use Data Tables, and Other October Must-Reads was originally published on Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
