22FIAP MLET Course — Software Engineering for Data Scientists
Role: Course Author / Instructor
Executive summary
Authored an 8-class course teaching data scientists the software-engineering practices — version control, testing, containers, dependency management, code quality and CI/CD — needed to ship reliable, production-ready code.
- Python
- Git
- Docker
- pytest
- Linters & Formatters
- CI/CD
Data-science practitioners in FIAP's ML Engineering program often lack formal software-engineering training, which leads to fragile notebooks and non-reproducible pipelines once a project needs to move toward production.
- Design 8 classes covering software-engineering fundamentals, Git workflows, automated testing/TDD, Docker-based reproducible environments, Python dependency management, code style/linters, CI/CD deployment and monitoring, and refactoring a real data-science project.
- Anchor every class to one evolving hands-on codebase instead of isolated exercises.
- Provide a working credit-scoring API project that students progressively test, containerize, lint and refactor across the eight classes.
I authored each class with a hands-on component built around a small credit-scoring API (`swe4ds-credit-api`) that students progressively test, containerize, lint and refactor as they move through the eight classes — applying each class's practice directly to the same evolving codebase rather than disconnected exercises.
Published as FIAP's course material for the Software Engineering for Data Scientists discipline; used by cohort students to practice each topic hands-on. The expected result is skill acquisition, not a production system.
Anchoring every class to one evolving sample project — instead of disconnected exercises — made the value of each software-engineering practice concrete. Testing, containers and refactoring matter most when applied to the same codebase over time, not in isolation.
Architecture diagram coming soon
Drop an image at assets/img/diagrams/fiap-swe-for-data-scientists.png