Things I Wish I Knew When I First Started Learning Python for Data Stuff

Industrial Engineering graduates get a great foundation to thrive in most applied math-ish jobs (Data Science, ML Engineer, Applied Math etc.). They are taught all three pillars of what I’d call the ‘full-stack data-driven decision making’: statistics, optimization and simulation. Their skills are relevant for many successful tech companies emerged in the 21st century such as Stitch Fix, Lyft or DoorDash.

A must to work in these companies to have good coding skills (e.g. in Python). However, there is a misconception in IE departments that they can’t code or it’s not their job to code! This is why students usually shy away from such jobs or they feel less competent in comparison to, say, a Computer Science student.

I was invited to present an entry level workshop about Python in this conference. The aim is to address this gap a bit. Here’s a glimpse of concepts I talk about:

• Why Jupyter Notebooks should not be used until a certain level of proficiency in the language
• Testing
• Profiling
• Top-down coding/problem solving
• Documenting your code a.k.a. Docstrings
• Writing reproducible code

If you want to see the slides, check the PDF button above!