The Dataquest Download
Level up your data and AI skills, one newsletter at a time.
Hello, Dataquesters!
Here’s what we have for you in this edition:
Top Read: Speed up real data work with Claude Code. Install it, refactor multi-file projects, generate tests and docs, and learn when it helps and when to rely on your own judgment. Read the blog
From the Community: Join a live thread on roles and hiring signals, commit to finishing a course in a week, talk through choosing the right ML algorithm, share quarterly takeaways, and get guided support on building your first end-to-end project. Join the discussion
What We’re Reading: A clear primer on Retrieval-Augmented Generation, a Python TUI toolkit with Asciimatics, and a look at AI “false memories” and what they reveal about model behavior. Learn more
Tired of writing the same data validation code or untangling messy pandas scripts? Claude Code is a terminal-side coding assistant that understands your project, generates boilerplate, refactors multi-file workflows, writes tests and docs, and helps debug tricky errors. In this tutorial, you’ll learn how to install it, where it shines, where it doesn’t, and how to use it to speed up real data work while you keep control of the analytical decisions.
From the Community
Roles, Signals & Job Search in Data Careers: Neha is hosting a discussion for anyone navigating their data career journey. Career changers, upskillers, or beginners. Join the thread to ask questions about job roles, hiring signals, and strategies to land a data job.
Picking the Right Machine Learning Algorithm: Join Suheb and Neha in a deep-dive discussion on how to choose the right machine learning algorithm when working on a specific task with a new dataset.
Community Quarterly Highlights: Tarun invites Community members to reflect on the past quarter. Share your top takeaways, impactful discussions, and favorite resources that helped you grow.
Building and Sharing Your First Project: Pastor and Artur guide you through creating your first end-to-end data science project and explain why sharing it in the Community is an important step in growing your portfolio and visibility.
What We're Reading
Introduction to Retrieval-Augmented Generation (RAG): Large language models can hallucinate or provide outdated responses. This article breaks down how Retrieval-Augmented Generation (RAG) works, explaining how it integrates external data sources to deliver more accurate and current answers.
Intro to Asciimatics: Another Python TUI Package—Step beyond GUIs and discover TUIs (Text User Interfaces). Learn how to use Asciimatics, a Python library for creating retro, text-based apps with animations and interactivity straight from the DOS era.
The “Seahorse Emoji” and AI False Memories: AI models can “remember” things that never existed, like a seahorse emoji. This piece explores how false memories in large language models reveal insights into their internal reasoning and training patterns
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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.