For a recent University of San Francisco MBA class, I wanted to put my students in a challenging situation where they would be forced to make difficult data science trade...
It is hardly possible in real life to develop a good machine learning model in a single pass. ML modeling is an iterative process and it is extremely important to keep tr...
I was wondering how to approach this blog when I decided to toast some raisin-bread for breakfast. Shortly after I started eating it, I began coughing. I have shared ...
You’ve perfected your CV, got great experience under your belt, maybe a PhD and can wrangle data amongst the finest but just how prepared are you for your next intervie...
Instead of well-run experiments and real evidence, many supposed rules are based on opinion, aesthetic judgments, and incomplete or oversimplified studies. In this Data S...
Summary: Dealing with imbalanced datasets is an everyday problem. SMOTE, Synthetic Minority Oversampling TEchnique and its variants are techniques for solving this pr...
Once dubbed as the sexiest job of the 21st century by The Harvard Business Review, data scientists take pride in having adept technical skills in providing solutions to p...
Do you often go with gut feeling rather than data and insights? Is your data stored in separate databases, in different formats with different values? We all have bad ha...
Companies are always looking for ways to improve the way they work with data. The ability to build out a workflow, automate the data blending and preparation, and then an...
Been trying to pull together a taxonomy of 3D data viz. Biggest difference is I think between allocentric (data moves) and egocentric (you move) viewpoints. The differenc...