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: Master prompt engineering by crafting clear, effective prompts to get better results from AI tools. Learn more
From the Community: Confused about when to use GitHub Repositories vs. Gists? Discover the key differences, strengths, and use cases to help you choose the right tool for your next project. Learn more
New Resource: Build a machine learning model to predict heart disease risk—covering data cleaning, feature selection, and model building. Learn more
Introduction to Prompt Engineering for Data Professionals
AI tools like ChatGPT can supercharge your data work but you need to know how to communicate with them. In this tutorial, you’ll discover how to craft precise, strategic prompts that turn vague AI outputs into clear, actionable insights. Learn techniques to save time, boost accuracy, and make AI a reliable partner in your data workflow.
From the Community
Heart Disease Prediction Kaggle Competition Winners: Pastor, the competition’s organizer, has announced the 3 lucky winners. Find out their names and be ready for the next competition!
Hacker News Pipeline: Ramesh has created a reproducible pipeline to identify the top 100 keywords on HackerNews in 2014. The project is brief and straight to the point because it relies on the use of @pipeline decorator, which allowed Ramesh to split complicated work into easily adjustable chunks.
Python Variable Naming Convention: Raisa gives an exhaustive explanation of variable naming best practices in Python and why they’re important.
Introduction to Python Dictionaries: Raisa provides a detailed, comprehensive, and well-exemplified introduction to Python dictionaries, illustrating the theory with a use case.
GitHub repositories vs. Gists: Anna provides a quick but helpful comparison of GitHub repositories and Gists outlining the strong and weak points of both.
DQ Resources
Build a Machine Learning Model: Learn to analyze patient data and build a machine learning model that predicts heart disease risk. This project covers data cleaning, exploratory analysis, feature selection, and model building—essential skills for aspiring data professionals. Learn more
Build A Python Word Guessing Game: Learn to create a Wordle-style word guessing game in Python. A fun way to practice programming fundamentals like loops, logic, and object-oriented design. Great for beginners looking to build interactive projects. Learn more
Analyzing Kaggle Data Science Survey: Practice core Python skills like lists, loops, and conditionals while exploring trends in programming languages and compensation among data scientists. Learn more
What We're Reading
Dealing with Highly Skewed Data: A Practical Guide: Learn how to identify and transform skewed data for better analysis in your data science projects.
AI Agents Try Running a Company—Here’s What Happened: An experiment conducted at Carnegie Mellon University tested AI agents managing a company, leading to missed deadlines, strange hacks, and a revealing look at AI’s limitations.
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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.