The Dataquest Download

Hello, Dataquesters!

Here’s what we have for you in this edition:

Top Read: A clear roadmap into data engineering with the exact skills, order, and portfolio plan to get job-ready. Learn more

Webinar Recording: Build a beginner-friendly Python “Garden Simulator” with OOP, error handling, and randomness. Watch now

From the Community: Heart disease modeling with Plotly, practical R tips for column names, and a convective systems project seeking collaborators. Join the discussion

What We’re Reading: Underrated Python libraries for 2026, a Streamlit goal tracker, and seven pandas performance tricks. Learn more

Data engineering is in demand because companies are producing more data than ever, and they need reliable systems to move, clean, and deliver that data at scale. Roles across the data and analytics space are growing fast, and that growth is powered by the pipelines and infrastructure data engineers build behind the scenes.

If you’ve been curious about breaking into data engineering, this roadmap gives you a clear path forward. You’ll learn which skills to focus on (Python, SQL, cloud platforms, orchestration tools), what order makes the most sense, and a realistic timeline for becoming job-ready, along with how to build a portfolio that actually gets you hired.

Missed the live session? You can now watch the recording of our beginner-friendly Project Lab!

Learn how to build an interactive text-based game in Python from scratch. In this session, Curriculum Director Anna Strahl walks you through using object-oriented programming, error handling, and randomness to create your very own “Garden Simulator.”

Predicting Heart Disease: Kevin added an individual touch to the Dataquest guided project by applying linear regression rather than a brute-force approach to predict cholesterol levels, as well as replacing static plots with interactive visualizations using Plotly.

Using Mapping Functions and Handling Column Header Issues in R: Israel explains when it’s necessary (and when it is not) to use a mapping function for handling dataset column names in R, and how to prevent the first row from being read as column headers.

Collaboration on Convective Systems Algorithm: Femi is planning to work on tracking convective systems in Google Colab and will require human oversight to complement the AI assistance provided by Gemini.

10 Lesser-Known Python Libraries to Try in 2026: A roundup of 10 underused Python libraries that can level up data work, from handling large datasets to validation and time series features.

The 2026 Goal Tracker (Python + Streamlit + Neon): A project walkthrough on building a simple habit tracker using Python, Streamlit, and Neon.

7 pandas Performance Tricks: Practical tips to speed up pandas workflows when working with large, messy datasets—covering faster operations and lower memory use.

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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.