Episode 22: Software Engineering for Data Science
About this episode
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, Ethan Garofolo joins Dr Genevieve Hayes 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
- Ethan’s Website
- Connect with Ethan on LinkedIn
- Follow Ethan on Twitter
- Follow Ethan on Twitch
- Connect with Genevieve on LinkedIn
- Be among the first to hear about the release of each new podcast episode by signing up HERE
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