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: Data science vs data engineering—day-to-day work, required skills, salaries, and a simple framework to choose your path. Learn more
From the Community: Glucose prediction in R, why domain logic beats AI for storm tracking, help interpreting RMSE vs R², and command line nano tips. Join the discussion
What We’re Reading: 10 data and AI trends for 2026, how AI is used at work today, and a clear guide to database normalization. Learn more
Data science and data engineering often get lumped together, but they’re fundamentally different careers. Data scientists focus on modeling, insights, and prediction. Data engineers build the pipelines and infrastructure that make those insights possible.
This guide breaks down what each role actually looks like day-to-day, the skills you’ll need, salary and job market outlook, and a clear framework to help you choose the path that fits your strengths and interests. If you’re deciding between these two careers, this is the clarity you’ve been looking for.
From the Community
Glucose Experiment R Project: Israel used his R skills to predict glucose concentrations in a test sample by building a linear model and summarizing the results with a clear and effective plot, demonstrating how programming and statistical modeling can support real-world data analysis.
Tracking Convective Systems—AI vs. Domain Knowledge: Sarah provides valuable observations on why tracking convective systems is primarily an algorithm design challenge, emphasizing the importance of clearly defined tracking logic and domain rules rather than reliance on AI tools.
Experimenting with Different Machine Learning Models: Salem seeks advice on interpreting model performance metrics after encountering high RMSE values alongside high R² scores across several machine learning models.
Issues with Nano Environment: Michelle is learning Git basics in the command line as essential tooling for Python users and is facing persistent challenges when working in the nano environment. Your help will be very appreciated.
What We're Reading
10 Data & AI Trends Shaping 2026: A quick scan of the biggest shifts in how people are building with AI right now, from automation to open-source momentum and what’s gaining traction next.
Where AI Is Actually Being Used at Work: Anthropic’s Economic Index uses real Claude usage data to map how AI is showing up across roles, tasks, and countries. A grounded look at AI’s real economic impact today.
Database Normalization Explained: A clear guide to one of the most important concepts in database design. Learn how normalization reduces redundancy and keeps data consistent as systems grow.
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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.