The Dataquest Download

Build data and AI skills – one newsletter at a time

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Here’s what’s inside:

• Top Read: Build a real Power BI dashboard from raw data to a finished cloud-published report, complete with modeling, refresh schedules, and alerts. Learn more

• Upcoming Webinar: Join us live to analyze NYC high school data in Python, clean and merge datasets, visualize trends, and explore how SAT scores relate to income, race, and school safety. Register now

• DQ Resources: Learn Python’s ternary operator, compare the best AI bootcamps in 2026, and find the Python course that fits your goals. Learn more

• From the Community: A Web3 collaboration opportunity, a smart Quarto workflow for PDF tables, and a Shiny app splash page question from a fellow learner. Join the discussion

• What We’re Reading: Why AI coding assistants need memory, and a practical BERT project for anime genre prediction on Kaggle. Learn more

Top Read

Most Power BI tutorials stop after a few charts. This one takes you through the full workflow—from a raw dataset to a finished dashboard you can publish and access in the cloud.

You’ll learn how to load and clean data, build a proper data model, create interactive visuals, configure refresh schedules, and even set alerts. By the end, you’ll have built a real business dashboard step by step, using the same project-based approach Dataquest uses in its Power BI learning path.

Upcoming Webinar

Date: May 14, 12:30 PM (ET)

Host: Anna Strahl, Director of Curriculum

Project: Analyzing NYC High School Data (Python)

Join us live to explore real-world NYC high school data and uncover how SAT scores relate to income, race, and school safety factors. You’ll clean and merge multiple datasets, visualize trends, and practice exploratory data analysis in a hands-on Jupyter Notebook session.

DQ Resources

• Python Ternary Operator—A Beginner’s Guide: Learn how Python’s ternary operator lets you handle simple if-else logic in a single readable line. A small feature you’ll use more often than you think.

• Best AI Bootcamps in 2026: Not all AI bootcamps teach the same skills. This guide breaks down the difference between building AI systems and using AI tools so you can pick the right path.

• Best Python Courses in 2026: This guide compares Python courses by learning style, goals, cost, and depth so you can stop researching and start learning.

From the Community

• Looking for a Blockchain Developer for a Web3 Ecosystem Project: Aline is seeking a blockchain developer to collaborate on building a Web3 platform that combines staking, gaming, sports betting, and blockchain education. More details are available in the thread.

• Using Quarto to Automatically Generate Tables for PDF Reports: Artur recommends a helpful tool for automatically generating tables in PDF files directly from code, rather than inserting them as images.

• Creating a Splash Page for a Shiny App in R: Salem is working on a splash page for a Shiny app and is experiencing issues displaying an image in R Markdown—your assistance would be very welcome.

What We're Reading

• Why AI Coding Assistants Need Memory: Coding assistants can write impressive code, but they lose context between sessions. This article explores why adding memory could make tools like Claude and Copilot far more useful for real projects.

• Anime Genre Prediction with BERT (Kaggle): This project fine-tunes BERT to predict anime genres from text descriptions, offering a practical look at multi-label NLP classification in action.

Learning with Friends

Bring an accountability partner along on your learning journey. Refer a friend, and they’ll enjoy an extra 20% off when they subscribe, while you earn $20. It’s a win-win for everyone. Learn together, stay motivated, and use your bonuses for digital gift cards, prepaid cards or charity donations!