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: 40+ real data analyst interview questions with code and model answers across SQL, Python, stats, business thinking, and behavior. Learn more
From the Community: Northwind analysis with window functions, No-Lose Lottery winner, cleaning NaNs in app data, and tips to add narrative to Power BI. Join the discussion
What We’re Reading: Prep for ML roles in 2026, building a crime-trend ETL with Postgres and Metabase, and better answers to the salary question. Learn more
Interviews can trigger a spiral of doubt. Am I ready? What if I freeze? What if they ask something I don’t know?
If you’re preparing for a data analyst interview, this guide turns that uncertainty into clarity. You’ll work through 40+ real interview questions across SQL, Python, statistics, business thinking, and behavioral topics—the exact areas hiring managers evaluate. Each question explains why it’s asked, what a strong answer looks like, and includes practical code examples where relevant.
By the end, you won’t just have memorized answers. You’ll understand the patterns interviewers look for and walk in confident about how to approach whatever they ask.
From the Community
Northwind Traders Analysis Using SQL Window Functions: Tomaz delivered valuable insights into Northwind Traders’ overall performance and proposed effective optimization strategies to further increase profitability.
Community No-Lose Lottery Winner: Find out who claimed the prize in the latest Community No-Lose Lottery and see how you can join the next round for your chance to win.
App Profile Problem Set Question: Hema is working on a practice problem related to cleaning Android and iOS data and has a question about how to handle NaN entries—your assistance would be greatly appreciated.
Enhancing Power BI Projects: Alla offers thoughtful recommendations on how to improve Power BI data projects by adding context and narrative to strengthen visual storytelling—and explains why it matters.
What We're Reading
How to Prepare for Machine Learning Jobs in 2026: A practical guide on what actually matters for landing ML roles in 2026—strong fundamentals, real projects, and the mindset hiring managers look for (not trend-chasing).
Creating a Data Pipeline to Monitor Local Crime Trend: A step-by-step ETL project that pulls police log data, loads it into PostgreSQL, and visualizes it in Metabase—plus validation and scheduling with Prefect.
How Not to Answer the Salary Question: Talking salary too early in an interview can hurt your negotiating power. This piece explains why you should first understand the full role and benefits, and how to respond strategically without undervaluing yourself.
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