Show Notes

Data science sits at the intersection of Computer Science and Statistics, so it comes as no surprise that many of the best data scientists have a computer science or software development background. And those that don’t? Well, there’s a lot they can learn from software developers.

In this episode, I’m joined by Ethan Garofolo to discuss techniques from software engineering and software development that you can use to become a better data scientist.

Guest Bio

Ethan Garofolo is a software developer and software architect, specialising in microservice-based projects and using Lean and DevOps principles to make software development teams more effective. He is the author of Practical Microservices: Build Event-Driven Architectures with Event Sourcing and CQRS and runs the Utah Microservices Meetup group.

Talking Points

  • What is the difference between a software engineer, software developer and software architect?
  • The impact of team structure and communications on software design.
  • How Lean and DevOps principles can be used to make technical teams run more effectively.
  • The benefits of pair programming and mob programming.
  • What is test-driven development and how can it be used to enhance the quality of data science outputs?
  • Using ChatGPT/AI to enhance developer capabilities.


Value Driven Data Science
Value Driven Data Science
Episode 22: Software Engineering for Data Science