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.
Links
- Connect with Genevieve on LinkedIn
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