I created this blog to help data professionals write better code.

If you’ve ever had to turn research code into something that actually runs reliably in production, you know there’s a gap between “I got it working” and “this is ready to ship.” That gap is software engineering fundamentals, and most data courses don’t teach them.

Pick Your Path

🌱 New to Data/Tech?

Start with the basics before jumping into production topics:

  1. Why Python Should Be Your First Language
  2. 16 Common Linux Commands for Beginners
  3. Setting Up Python Virtual Environments

Next step: Once you’re comfortable with the basics, move to “Writing Better Code” below.

📝 Writing Better Code?

You know pandas and can build models, but want to write more professional code:

  1. Is Your Python Code Giving Junior Developer? 5 Tell-Tale Signs (And How to Fix Them)
  2. Beware of the Mutants: DataFrame Mutation Pitfalls
  3. The Importance of Documentation

Next step: Check out “Ready for Production” for deployment and scaling topics.

🚀 Ready for Production ML/MLOps?

You need to deploy models, build reliable systems, or scale beyond your laptop:

  1. Getting Started with AWS CDK
  2. Serializing Data 101: Apache Avro
  3. Docker Series for Data Practitioners

Next step: Subscribe to my newsletter for more production engineering content and case studies.

💼 Navigating Your Career?

Looking for advice on transitions, job hunting, or leveling up:

  1. Job Search Tips (coming soon!)
  2. My Journey: Data Analyst → MLOps → MLE (coming soon!)
  3. Conference Speaking as a Career Booster (coming soon!)

Not sure where to start? Browse by topic in the navigation menu, or grab my free SWE Checklist for Data Professionals to see which skills you should focus on next.