Lex L- Profile

Data science emerged as a term over the past few years. Amazon describes data science as such:

_{^{Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.}}

Wikipedia defines data science as such:

^{_{Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, scientific visualization, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.}}

One thing that stands out from both definitions is the use of the word insights. Meaning, that data scientists are students of data, studying it carefully, observing its patterns, and then putting forth

The Cambridge dictionary defines insights as such:

_{to have a clear, deep, and sometimes sudden understanding of a complicated problem or situation.}

_{Example sentence: It was an interesting book, full of fascinating insights into human relationships.}

Insights, in short, means understanding. Understanding is that ‘aha’ moment whereby we know the why and how, and the why behind the why, where we dig deeper into things.

These 2 definitions offered then by AWS and Wikipedia then for the term data science, are problematic. The term data science is a misnomer, one that is not apt for the description of the work. Why is that so?

We need to look at the definition of the word scientist. The Cambridge Dictionary defines scientist as such:

_{an expert who studies or works in one of the sciences
Examples:
a forensic scientist
a team of scientists from the University of Miami
A couple of decades ago scientists noticed Panama’s climate was slowly growing drier.
There are scientists who say that the results of the research are flawed.}

First of all, data is not a science.

Secondly, data scientists apparently, according to AWS and Wikipedia, don’t study data.

Lastly, the definition by AWS and Wikipedia for data scientists is conspicuously missing the term ‘expert.

To better fit their actual definition, we can try doing the reverse, trying to find a suitable job title or role based on AWS and Wikipedia’s definition, or rather, job scope. I plugged this definition into Claude AI. Claude is a better option than Chat GPT in terms of accuracy, and its founded by the former Chief Scientist of Open AI, Chat GPT’s product.

_{1st Question:}

_{propose a term for the field of studying data to extract insights}

_Answer:

_{Data Analyst}

_{Data Analytics
This term encompasses the field dedicated to studying data to extract insights.}

_{2nd Question:}

_{Propose a job title for someone who studies data to extract insights}

_Answer:

_{Data Analyst}

_{This title reflects the role of someone who examines data to uncover insights. I can provide more details about this position if you’re interested.}

In other words, it is preferable to ascribe the term Data Analyst to the description of the term Data Scientist, and the term Data Analytics to the field of Data Science.

Intuitively, to me, analysis is about insights, thinking critically, examination, and finally, understanding.

It’s timely for Wikipedia and the rest of the technology companies to be truthful about the term Data Scientist, and revise it to Data Analyst.