Engineers to Data-Scientists with Python
| Start time | 11:00 |
|---|---|
| End time | 11:25 |
| Countdown link | Open timer |
With data-science teams increasingly requiring domain knowledge, transforming a group of domain specific engineers into a functioning data-science team can be a challenge. In this talk we will explore how a team of engineers moved from a tangled MATLAB codebase to a functioning Python data-analysis pipeline, and some of the lessons learned along the way.
This talk centers around an early stage start-up reaching the scale-up phase of growth and having to move from an ad-hoc collection of MATLAB scripts to a functioning Python analysis pipeline. All while transforming a team of mechanical engineers into a functioning software engineering and data-science team.
I will paint a picture of the work we do, and how the product was first created - including the wild selection of technologies, before talking about some of the challenges faced in migrating a large monolithic product to a modular codebase. I’ll discuss some the advantages and pitfalls of using Python, and how we trained non-software engineers to produce quality code.
The talk has something for everyone, whether you’re a tech-lead responsible for training & developing your team, a business leader in a scaling startup, or a junior engineer thinking about how to improve.
An Engineer and software developer, James is a senior analytics engineer and the tech-lead of the Data Processing team at Resolution Systems. He has spent the past two years building out and transitioning the team from a MATLAB monolith to a Python analysis pipeline.