Recruiters are getting smarter about how they filter out applicants during the hiring process. Scary stats like the cost of making the wrong hire motivate in-house recruiters to be more careful about whom they extend job offers. The cost of hiring the wrong fit can be up to 2.5x the salary – as much as $240,000, according to one report. 

Summary

Over the weekend, I embarked on a project to create a 3D printed QR code that guests at our house could scan to gain access to our guest wireless network. Why 3D you might ask? Well, that’s how geeks like myself like to impress their guests! Also, let’s be real, I have a 3D printer at home, and I was looking for a fun way to put it to practical use. It turns out that it makes for some nice wall artwork as well.

Introduction to Pandas

So, what is Pandas – practically speaking? In short, it’s the major data analysis library for Python. For scientists, students, and professional developers alike, Pandas represents a central reason for any learning or interaction with Python, as opposed to a statistics-specific language like R, or a proprietary academic package like SPSS or Matlab. (Fun fact – Pandas is named after the term Panel Data, and was originally created for the analysis of financial data tables). I like to think that the final “s” stands for Series or Statistics.

Introduction: balanced and imbalanced datasets

Close your eyes. Now imagine a perfect data world. What do you see? What do you wish to see? Exactly, me too. A flawlessly balanced dataset. A collection of data whose labels form a magnificent 1:1 ratio: 50% of this, 50% of that; not a bit to the left, nor a bit to the right. Just perfectly balanced, as all things should be. Now open your eyes, and come back to the real world.