Language is dynamic, fluid, and evolving especially in fields of study that are going through significant transformation. This is especially true for data analysis. There also are fads and fashions in fields of study and this translates into changes in language and emphasis in study. The evolution of techniques and language means that terms and descriptors that were useful in the past do not have the same meaning or have been de-emphasized in importance. These changes in focus and language means that what was current a decade ago seems dated today.
I have listed some of the language and focus changes that have occurred since I was a graduate school. These represent the topics or phasing of terms in the current market for data analysis. These new terms have deeper meaning than what we have used as contrast. In many cases, they are better descriptors. Nevertheless, these changes may give you pause and place a humorous spin on the current changes in data analytics.
Nobody studies statistics. We are all involved in data science. Using the term management science is dated. We are involved with business analytics. Forget about rules, we only follow algos. If you are using data, it better be "big data". Regression is for those with grey hair, everyone should be involved with supervised learning. Get with the language right and you will be cutting-edge.
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