The Dataquest Download
Level up your data and AI skills, one newsletter at a time.
Hello, Dataquesters!
Here’s what we have in store for you in this edition:
Top Read: Learn how to use Docker Compose to automate your full ETL setup, spinning up databases and scripts with a single command for a smoother, error-free workflow. Read the blog
Community Highlights: From a well-documented lottery probability app to a powerful heart disease classifier, check out standout projects and career opportunities shared by learners like you. Join the discussion
New Project: Visualize what’s really causing traffic on I-94. This guided project teaches you how to uncover patterns with Python, pandas, and Seaborn that stats alone can’t reveal. Try this project
As your data projects grow, they often involve more than one piece, like a database and a script. Running everything manually gets tedious and error-prone. One service starts too early, an environment variable is missing, and suddenly nothing works.
Docker Compose solves this. In this tutorial, you’ll define a full ETL workflow with a PostgreSQL container and a Python script, all managed in one file. Run the entire setup locally with a single command and skip the manual setup for good.
From the Community
6/49 Lottery Probability App: Yana’s project clearly explains the problem, outlines key questions, and presents well-documented code with clean outputs and helpful insights.
Classifying Heart Disease: Steve’s project features deep EDA, strong visuals, and a critical evaluation of the model’s performance and real-world impact.
Senior AI/LLM Developer Opening: A US-based company is hiring a remote, part-time senior AI/LLM developer. Check out the opportunity if you’re qualified.
Add a Table of Contents to Your Notebook: Linky shares a quick markdown trick to help readers navigate your project more easily.
DQ Resources
Finding Heavy Traffic Indicators on I-94: Learn how to build multiple visualizations that tell a comprehensive story about traffic patterns, demonstrating how exploratory data analysis can reveal insights that summary statistics alone might miss. Learn more
Analyzing Startup Fundraising Deals from Crunchbase: Learn how to process large CSV files efficiently by chunking, optimizing memory, and using encoding strategies, then turn the data into a fast, searchable SQLite database. Learn more
Answering Business Questions Using SQL: Learn how to analyze a digital music store’s data using SQL. This hands-on project with the Chinook database walks through real-world SQL tasks like tracking sales, evaluating employees, and spotting growth opportunities using CTEs, subqueries, and more. Learn more
What We're Reading
2048, Iterators and Iterables: While coding a version of the 2048 game in Python, Ned explores a subtle but critical distinction – iterator vs iterable. It’s a great practical example of debugging Python logic and deepening your understanding of iteration.
The SQL Concept Most Analysts Miss: This article spotlights a common SQL blind spot: writing queries that work but scale poorly. Learn how to shift from “querying for results” to “engineering for performance”—a must-read for anyone collaborating on large datasets.
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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.