Software Engineering for Data Scientists | OEM Learning Journey
Data Scientists with software engineering skills are in high demand. Writing reproducible, robust, scalable code is key to data science project success. This journey bridges the gap between data science and software engineering.
What will you learn?
- Apply OOP, Functional Programming and Logging like a software engineer
- Master the command line for more efficient developer work
- Use Git and GitHub for version control and teamwork
- Apply Unit Testing and Concurrent Programming for more robust code
- Write Bash scripts for reusable workflow automation
- Advanced Git: branching, merging and deployment
Learning path (90+ hours)
Track 1: Data Science Foundations — 7h courses, 22h labs, 25h IA
Clean, analyse and visualise data with Python, pandas and seaborn.
Track 2: Software Engineering in Python I — 4h labs, 4h IA
OOP, Functional Programming and Logging for production-ready code.
Track 3: Learn the Command Line — 5h labs, 4h IA
Navigate, access and modify files without a mouse.
Track 4: Learn Git: Introduction to Version Control — 1h courses, 4h labs, 2h IA
Git and GitHub for version control and collaborative coding.
Track 5: Software Engineering in Python II — 1h courses, 2h labs, 3h IA
Unit Testing, Concurrent Programming and Deployment.
Track 6: Learn Bash Scripting — 1h labs, 1h IA
Write reusable Bash scripts to automate repetitive tasks.
Track 7: Learn Git II: Git for Deployment — 2h courses, 4h labs, 2h IA
Branching, merging, pull requests and Markdown for team collaboration.
What is included?
- 90+ hours: courses, labs and interactive exercises
- Mentor guidance and assessments
- E-books
Who is this for?
Data scientists who want to write production code and collaborate more effectively with engineering teams.
Get started
Contact us via our contact form or call +31 (0)36 760 10 19.
