Part 1 - The Engineering Mindset

A lot of aspiring data scientists come from non-engineering backgrounds -- this can present some challenges when breaking into the field of data science.

Don't get me wrong, it is great to bring a unique background and perspective to the world of data science, but it's not enough.

Engineering is critical part of being a data scientist.

Developing the right mindset will not only help you break into the field, but it will also make you far more effective when you get there.

Learning the engineering mindset will help you see the big picture, systematically solve underlying problems, and stay focused on results.

Here are my top tips for cultivating the engineering mindset for data scientists:

Build systems to solve problems

It's not about hacking together a one-off solution. It's about building systems to solve problems. Systems that are testable, robust, efficient, scalable, and portable. Systems that you can resuse. Systems that can accomodate...

Getting the attention of recruiters can be tough. Sometimes it seems like every resume you send out goes into a black hole of nothingness and despair, never to be seen by a human again. Or worse yet, you see the dreaded email auto-response telling you that "after careful consideration, we regret to inform you that you have not been selected for this position." Sure, very regretful...

After hours of customizing your resume, you got rejected before you even had a chance to have a 5 minute conversation with a real human. This can be frustrating and you deserve better.

If you feel this way, it's not your fault. It's difficult to get noticed and recruiters are overwhelmed by huge stacks of resumes and countless applications. I know, as a hiring manager I often read through dozens, or even hundreds, of resumes at a time to find candidates that are a fit.

Most resumes only get a 6 second glance before being tossed aside. This is usually enough to determine if a candidate is unqualified,...