The Dataquest Download

Hello, Dataquesters!

Here’s what we have for you in this edition:

Top Read: 60 SQL interview questions with direct answers and code examples across beginner, intermediate, advanced, and data engineering topics. Learn more

Webinar Recording: Build an interactive food ordering app in Python using dictionaries, loops, and functions. Watch now

Latest DQ Resources: Build a job-ready portfolio with 30 data science projects, 20 guided data analyst projects, and a full RAG system tutorial. Learn more

From the Community: Real stories behind guided projects, why ML engineers need data engineering skills, clustering project best practices, and a new collaboration opportunity. Join the discussion

What We’re Reading: How to run Claude Code for free with Ollama, plus 15 power features to help you get more out of it. Learn more

SQL shows up in almost every data interview, yet it’s one of the easiest places to get caught off guard. This post gives you 60 real SQL interview questions across beginner, intermediate, and advanced levels, including a dedicated section for data engineering roles.

Each answer is short, direct, and paired with code examples so you can quickly understand what interviewers expect. If you’re preparing for a data role, this is a focused way to sharpen your SQL skills before the interview.

Missed the live session? Watch the recording

Learn how to build an interactive food ordering app using Python. In this step-by-step walkthrough, Anna Strahl shows how to use dictionaries, loops, and functions to create a seamless user experience from menu to checkout.

Build Your Portfolio with Real Projects: 30 data science projects with datasets, code, and step-by-step guidance. Start finishing projects that show what you can actually do.

20 Data Analyst Projects for Beginners (2026): Learn Python, SQL, Excel, Tableau, and Power BI through step-by-step projects designed to help you build real experience from scratch.

Build a RAG System Without Frameworks: See how a RAG system was built using just Python, a vector database, and APIs. Learn what worked, what broke, and how to improve your own setup.

DETE and TAFE Employee Exit Surveys: Constance shared a short story related to the guided project on Clean and Analyze Employee Exit Surveys about her friend from Queensland, Australia, who actually worked for TAFE and was laid off in 2009—a real-life story behind just one row of the dataset.

Why Every ML Engineer Needs Data Engineering Skills: Pastor explains, from his professional experience, why learning data engineering is essential for every machine learning engineer to understand the entire data lifecycle, handle real-world tasks efficiently, and build a successful career.

Software Engineer Partner Needed: Yoni is seeking a fluent English-speaking software development specialist with strong communication skills for a long-term, remote, performance-based collaboration; more details on responsibilities and requirements are in the post.

Best Practices for Clustering Projects: Casandra illustrates the importance of defining a clear business goal and context for clustering projects, and the value of highlighting key cluster characteristics directly in the final graph to improve the interpretability of the results.

Run Claude Code for Free with Ollama: This guide shows how to run Claude Code using Ollama with local or free-tier models. A practical way to experiment and prototype without worrying about costs.

Claude Code’s Hidden Power Features: The creator of Claude Code shares 15 features he uses daily, along with how to start using them yourself. A quick way to get more out of the tool.

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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.