I first heard of Learning Through Play when I sent my kids to pre-school, but now I realize it’s how all Data Scientists should learn
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.
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.
Odds and probability are different, and too many people make decisions without knowing that.
An aspect of mathematics that I believe to be sorely lacking in the business world, and which can lead to seriously erroneous decisions being made on a regular basis: the concept of significance when studying data.
This is the fourth in a series of articles explaining the principles of networks for those who may use them in a data science context.
Decision makers in the year 2020 will be facing many more data driven documents and charts than they did 10 or 20 years prior. But are those decision makers any better equipped to make accurate decisions in such a data rich environment?
This is the third in a series of articles explaining the principles of networks for those who may use them in a data science context.
I strongly believe that the field of data science could benefit greatly from a more frequent use of analogies in communication of key concepts.
This is the second in a series of articles explaining the principles of graph theory for those who may use it in a data science context.