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 the mindset that sets senior data scientists apart. This 4-step framework will help you break down complex problems and approach your data projects with clarity and confidence. Read the blog
Community Highlights: Explore app profitability with data-driven storytelling, check a well-documented data visualization project on traffic patterns, and learn how AI is transforming the world of football. Join the discussion
The Mindset That Separates Junior Analysts from Senior Data Scientists
Complex data problems can feel overwhelming. But experienced data scientists rely on recursive thinking and that’s breaking problems into smaller, repeatable steps. In this guide, you’ll learn a 4-step framework to tackle messy datasets, unclear business questions, and large-scale analysis without the stress. Apply it directly to your data projects and see how powerful this mindset can be.
From the Community
Profitable App Profiles for App Store and Google Play Markets: Oladunni’s project stands out for its engaging intro, clear subheadings, clean code, and thoughtful, data-driven conclusions.
Metro Traffic Volume Analysis: Zahabia’s first data viz project features an excellent README that outlines goals, data sources, and tools used.
How AI Engineering is Transforming Football: James explores the impact of AI in football, from tactical decision-making to player wellbeing and fan engagement.
Uploading a Project to GitHub: Alla shares a simple, step-by-step guide to help you upload your project files to GitHub with confidence.
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
When to create classes in Python: This reflective post illustrates when object-oriented design pays off. The author shows how converting a tangle of functions into a well-structured class made their Python project easier to manage and extend.
How AI like ChatGPT really works: This explainer breaks down how generative AI models learn from data and produce content, while also tackling hot-button issues like misinformation, job automation, and environmental impact. A balanced, beginner-friendly read.
Unless ChatGPT-5 gets these upgrades, I’m sticking with Claude — here’s why: A candid, critical opinion piece comparing Claude and ChatGPT. The writer highlights where Claude currently excels, especially in memory, tone, and comprehension, and lays out the must-fix flaws OpenAI should address in GPT-5.
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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.