FALL TERM DATES:
SEPTEMBER 18TH-NOV 9TH
Whether it means being the tip of the spear or the newly elected sheriff cleaning up the town, a dev ops engineer is the tech industry’s pinnacle of “order from chaos.” Dev ops consists of the multitude of technologies and concerns that create the baseline from which teams build software. This course covers the principles and practices that go into occupying this high-impact role.
WHO SHOULD TAKE THIS COURSE?
The ideal student for this course embraces chaos as an opportunity. Patience is a must as trial and error is a core staple of being a dev ops engineer, and this course will emulate that. If you’re the kind of person that loves the opportunity to create order and structure that can benefit others, then you might be a good fit for dev ops.
HOW DO I KNOW I’M READY?
A strong familiarity with the entire SDLC, and ideally have created or been a part of the creation of full applications. You’re committed to your journey into the software development industry, and would here to carve out your place in it. You should be very comfortable with the command line (bash in particular) and concepts of server structure.
In this class you'll
- Learn to see the world from the perspective of a machine
- Recognize machine learning in your daily life in Portland
- Find ways to apply machine learning to Hack Oregon projects
- Appreciate the kinds of algorithms that work for different problems and kinds of data ("model selection")
- Assemble the pieces of a machine learning pipeline using python packages ("feature engineering")
- Assess the "quality" of a machine learning model or pipeline ("cross validation")
- Recognize the "scaling" challenges of machine learning ("scaling")
- How to build a production machine learning pipeline ("online learning")
- Recognize when human intuition leads you astray, and how a machine can do better ("logical fallacies")
WHO SHOULD TAKE THIS COURSE
If you like discovering patterns and trends in data or the way the world works, you enjoy this class. And you'll be excited to know that it's possible to teach machines to recognize and react to those patterns while you sit back and just feed them data. You'll learn how in this class.
HOW DO I KNOW I’M READY?
Do you know how to write a python function? Can you instantiate and manipulate a python object (like a `dict` or array)? Can you plot a table of data in Spreadsheet or in python? Do you know what "linear regression" is? If so, you're well-prepared to get the most out of this class.