We designed a curriculum to prepare you for your new career, now and going forward.
Here's how we did it.
Back end: Behind the scenes, back end technologies like Python and SQL search through and collect data to then send to the front end. Even though users don't see it, this is where we'll write most of our code.
Cloud computing: To share our application with others, we'll store our data and our code on the cloud. By the cloud we just mean a company's computer that we can rent on demand. For this, we'll use Amazon Web Services and Docker.
Percentage of all *all* Indeed.com job postings, listing the above skills.
After teaching the fundamentals of front end, back end and cloud computing, we'll learn how to navigate large codebases that others wrote.
Here, the main skill is learning to navigate, comprehend, and contribute to an unfamiliar codebase.
And this is the real skill required of a professional developer. By developing this skillset, you'll learn the procedures to work with various codebases, regardless of the particular languages or tools.
Vicki Boykis, Machine Learning Engineer, Tumblr
Just a personal anecdote, but in the past 2 years, % of any given project:
That involves ML: 15%
That involves moving, monitoring, and counting data to feed ML:
Source: Data science is different now.
To round out the skills for a data engineer, we still have to learn data pipelines.
It's worth it. Not only are we learning an in demand skillset, but we're also learning the foundations for a career in data science or machine learning.
In fact, if you want to become a data scientist or machine learning engineer in the future, it's critical to first have the required data engineering skills.