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: Learn when to lean on AI, when to push through on your own, and how to make sure you’re not just copying answers. Read the post
Webinar Recording: Predict employee productivity with decision trees and random forests in scikit-learn. Watch the recording
Community Picks: From app market analysis to creative machine learning projects and personal websites. Join the discussion
What We’re Reading: Python for AI, PyApp for packaging apps, and a beginner-friendly Python project building a coffee machine. Read more
Ever worried that leaning on AI tools while learning Python, SQL, or data science would make you less of a “real” learner? Many beginners feel that way. But real engineers and hiring managers say the opposite: AI can accelerate your learning—if you use it right.
In this guide, we share the pain points new learners struggle with, the warnings pros give, and a clear step-by-step roadmap to build data skills responsibly with AI. You’ll learn when to lean on AI, when to push through on your own, and how to make sure you’re not just copying answers but actually mastering the skills.
We’ll also show you how to do this with Chandra, Dataquest’s AI learning assistant, designed to help you ask better questions, test your thinking, and learn faster, without cutting corners.
Whether you’re starting out or adding projects to your portfolio, this post will give you the mindset and workflow to use AI as your coach, not your crutch.
Webinar Recording
Learn how to use Python and scikit-learn to predict employee productivity using real-world garment factory data. In under an hour, you’ll walk through the full workflow—from cleaning and modeling to presenting insights with decision trees and random forests.
Great for anyone looking to level up their machine learning portfolio.
From the Community
Profitable App Profiles for the App Store and Google Play Markets: Rohan applied the Don’t Repeat Yourself (DRY) principle by creating several functions to clean and analyze app store datasets. They produced a professional, easy-to-read report that offers recommendations for apps to be developed in the competitive landscape of mobile app development.
Data Cleaning and Analysis of Used Car Sales: Elena demonstrated strong skills in data cleaning and exploration and wrote an engaging story backed by concrete numbers to discover the factors that have the largest impact on car prices.
Helpful Resources for Data Scientists and Programmers: Artur shared an extensive selection of various advanced resources that are a must-check for data science and programming professionals and enthusiasts. Among them are his impressive GitHub project portfolio and some highlighted repositories. Check the posts [1] and [2] to learn more.
Alternative Way of Tuple Unpacking: Israel shared a different and more efficient approach to solving a problem from a Dataquest lesson—unpacking a Python tuple.
Creating Websites with Python and HTML: Hamza shared tips and tricks to make building websites with Python and HTML smoother, easier, and faster.
Importance of Data Literacy in Generative AI World: Pranta raised a crucial question about the role of data literacy in ensuring the accuracy and meaningfulness of generative AI outputs.
Personal Machine Learning Engineer Website: Pastor showcased his exemplary personal website, highlighting his skills and experiences as a Machine Learning Engineer, along with articles and insights about Data Engineering, Machine Learning, and AI technologies.
What We're Reading
Build a Coffee Machine in Python: Beginner-friendly project where you build a coffee maker program that takes orders, checks stock, and accepts payment—great practice for loops, conditionals, and dictionaries.
How Python Powers AI: Python’s simplicity and rich libraries like TensorFlow, PyTorch, and pandas make it the top choice for AI and ML, speeding up prototyping, training, and deployment across industries.
PyApp – Package Python Apps Easily: PyApp, built with Rust, turns Python projects into standalone executables so they run anywhere without requiring Python installed.
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