Everyone is now calling themselves a Data Scientist. No matter what position I am hiring for, that term is on over 80% of the resumes I look at. It has actually made me start to ignore the term because it is not a differentiator of talent any more.
Increasingly, there is a desire to cluster observations based on how they change over time. Do they increase, decrease, stay the same? Are they consistently high, consistently low, or do they go up and down? Are some more complex in their changes than others?
The rate at which data is being generated by business and the world at large is rapidly exceeding the speed at which leaders are equipping themselves to work with data.
The more coding I do, the more sensitive I become to inefficiency. Here are ten hacks I use regularly to try to minimize distractions and keep up my production pace.
I was interested in finding out what it was about someone during that short interaction that determined whether or not someone viewed them as a match. This is a great opportunity to practice simple logistic regression if you’ve never done it before.
Community detection can help identify a structure in a set of interactions, which has applications to organizational design, but can also be useful in other fields such as digital communications and crime investigation
The reticulate package allows R and Python to work together — here’s a tutorial
There was once a business executive who kept coming home troubled and distracted. His partner asked him one evening, “What’s the matter? Something’s worrying you.”. “There’s so much data”, he replied, “and I can’t understand it or get my head around it.” “You could try hiring a data scientist”, suggested his partner. So he did just that. Six months later he was still coming home troubled, and when his partner inquired again what was up, he replied: “There’s so much data, and I can’t understand what my data scientist is telling me about it”.
I struggled to keep it to ten last time, so here’s ten more for you