Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more.
With the end of the year just a few weeks away, neither our authors nor our readers are showing any signs of slowing down.
We’re thrilled to have published some of our strongest articles of the year in the past month: practical guides on LLM workflows and resources on career growth, Python-focused tutorials, and deep dives on recently launched tools, among other standout topics. Read on to catch up with (or revisit) November’s most-read stories.
Graph RAG vs SQL RAG
Which database paradigm delivers more accurate and insightful results? Reinhard Sellmair sets out to assess the performance of two types of RAG systems by pitting GraphRAG and SQL RAG against each other, using the same dataset and questions.
LLM-Powered Time-Series Analysis
In the second part of Sara Nobrega’s popular series, we learn about the prompts we need for advanced model development (think ARIMA and LSTM).
How to Build Machine Learning Projects That Help You Get Hired
Not all ML portfolios are created equal. Egor Howell shares time-tested insights on what works — and what doesn’t.
Other November Highlights
Don’t miss our other top reads from the past month, tackling NumPy, Multimodal RAG, marimo notebooks, and many other topics — both evergreen and cutting edge.
NumPy for Absolute Beginners: A Project-Based Approach to Data Analysis, by Ibrahim Salami
Understanding Convolutional Neural Networks (CNNs) Through Excel, by Angela Shi
Run Python Up to 150× Faster with C, by Thomas Reid
How to Build an Over-Engineered Retrieval System, by Ida Silfverskiöld
Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources, by Partha Sarkar
Why I’m Making the Switch to marimo Notebooks, by Parul Pandey
Your Next ‘Large’ Language Model Might Not Be Large After All, by Moulik Gupta
In Case You Missed It: Our Latest Author Q&As
We love sharing our authors’ expertise, career insights, and views on the recent developments in the world of data science and AI. Here are our most recent Author Spotlights.
- “Systems thinking helps me put the big picture front and center”
Shuai Guo on deep research agents, analytical AI vs LLM-based agents, and systems thinking.
- “The success of an AI product depends on how intuitively users can interact with its capabilities”
Janna Lipenkova on AI strategy, AI products, and how domain knowledge can change the entire shape of an AI solution.
Meet Our New Authors
We hope you take the time to explore the excellent work from the latest cohort of TDS contributors:
- Jure Leskovec, a Stanford professor of computer science and entrepreneur, explains why LLMs aren’t a one-size-fits-all solution for companies.
- Sherin Sunny, a senior engineer at Walmart, walked us through the creation of a computer vision project aimed at detecting leaves.
- Manuel Franco de la Peña introduced us to ShaTS, a novel Shapley-based explainability method specifically designed for time-series models, which he co-created.
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?
We’d Love Your Feedback, Authors!
Are you an existing TDS author? We invite you to fill out a 5-minute survey so we can improve the publishing process for all contributors.
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