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: Stuck in the “no experience” loop? Here are 20 beginner-friendly data analyst projects across Python, SQL, Excel, and BI to help you build a hireable portfolio. Learn more
Project Lab recordings: Watch 18+ full walkthroughs of real projects being built from scratch so you can see exactly how to clean data and structure your analysis. Watch recordings
From the community: Tips for maximizing the business impact of your data science projects, plus a guide to managing your forum notifications. Join the discussion
What we’re reading: How to build an AI agent for time-series anomaly detection, and a complete EDA on 46 years of Coca-Cola’s stock data. Learn more
Top Read
Trying to break into data analytics but stuck in the “no experience” loop? This guide shares 20 beginner-friendly data analyst projects across Python, SQL, Excel, Tableau, and Power BI—each with step-by-step instructions.
You’ll also get a recommended starting order and tips for turning 3–5 strong projects into a portfolio that gets you interviews.
Project Lab Recordings
If the 20 projects above gave you a roadmap, Project Lab gives you execution.
Inside our Project Lab recordings, you’ll find our collection of full walkthroughs where we build real, portfolio-ready projects from scratch. No fluff. No vague instructions. Just clear, practical guidance you can follow and replicate.
You’ll see exactly how to:
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Clean messy, real-world datasets
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Structure an analysis from question to recommendation
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Combine tools like Python and SQL in one workflow
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Build dashboards and publishable apps
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Handle larger datasets without crashing your notebook
These recordings are perfect if you learn best by watching someone think through the process, make decisions, debug issues, and explain trade-offs along the way.
From the Community
Business Impact of Data Science Projects: Alla discusses the importance of including key insights and final business recommendations to strengthen the analytical and business impact of data science projects—check out here and here.
Notification Levels for Community Topics: Learn about the four types of notification levels for individual topics or groups of topics on our forum, when to use each one, and how to easily adjust and manage them.
What We're Reading
Building an AI agent for anomaly detection: Spotting unexpected spikes in time-series data is a classic pain point. This hands-on guide walks through building an AI agent to detect and handle those anomalies automatically.
Coca-Cola’s complete EDA: Warren Buffett has held KO stock since 1988. This Kaggle notebook uses 46 years of data to walk through a complete exploratory data analysis, covering price history, returns, and volatility.
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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.