What Does It Take To Be a Qualified Data Scientist?

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.

Clustering Time Series Data in R

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?

Making Sense of the Game of Thrones Universe Using Community Detection Algorithms

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

Three words all HR professionals should live by

Failure to follow these words can put patients’ well-being at greater risk, and can open individuals up to ethical and legal challenges about their decision-making. The three words are evidence based practice.

21st Century HR – The three big shifts needed for the future

I am convinced that in the 21st century talent management will be the key differentiator of successful organizations. Within a couple of decades, no matter what measure you use, those with the most advanced approach to the recruiting, development and retention of talent will outperform those who lag behind.

Who You Gonna Call if Your Data Science is Lost in Translation?

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”.