Data-Driven "Definition Hell" Quiz

quiz
data
analyst
engineer
scientist

What’s that?

An attempt to come up with a term describing the confusion arising from the ease with which terms are thrown around in today’s data-related job landscape.

A justified reason, of course, is the need for non-technical recruiters to apply filters to perform their job. I believe this is nothing to poke fun at. What I believe can be poked fun at, however, is the directives that trickle down and shape job postings, conversations and positions as a result of trend hunting.

To emphasize ‘fun’ rather than ‘poke’, I tried to put together a small quiz where everyone can evaluate their knowledge and opinions on the various data-related business and technical roles, against a sample of the collective experience and information found online!

Test your might!

Select a box to see how well you can anticipate the statements documented in top Medium and Towards Data Science member stories search results regarding the most common descriptions of:

Data Scientists
Data Engineers
Data Analysts
ML Engineers

🔎 “A ____ is someone who is better at statistics than any software engineer, and better at software engineering than any statistician.”

  • Data Scientist
  • Data Engineer

🔎 “That is why it’s so important that ____ learn how to excavate insights which can be acted upon, presentable in both visually compelling and digestible formats.”

  • Data Analysts
  • Data Scientists

🔎 “The author mentions ____ as individuals who leverage big data frameworks.”

  • Data Scientists
  • ML Engineer

🔎 “It’s not uncommon to find ____ with only limited programming knowledge. After all, querying a SQL Database , making some visualizations using Python/R are often all the tasks they are ever expected to do.”

  • ML Engineers
  • Data Scientists

🔎 “____’ most basic, universal skill is the ability to write code. This may be less true in five years’ time, when many more people will have the title '____' on their business cards.”

  • Data Analyst(s)
  • Data Scientist(s)
  • Data Engineer(s)

🔎 “As the article suggests, you have less reasons to be a good coder today as a ____.”

  • Data Engineer
  • ML Engineer
  • Data Scientist

🔎 “____: Do you want to analyze big data, design experimentation and A/B test, build simple machine learning and statistical models (e.g. using sklearn) to drive business strategy?”

  • Data Scientist
  • ML Engineer

🔎 “____ have to deal with Big Data where they engage in numerous operations like data cleaning, management, transformation, data deduplication, etc.”

  • Data Analysts
  • Data Engineers

🔎 “Machine learning engineers feed data into models defined by ____.”

  • Data Engineers
  • Data Scientists

🔎 “____ analyze, test, aggregate, optimize the data and present it for the company.”

  • Data Scientists
  • Data Analysts

References

See below for the full quotes:

Any take-aways?

I believe this quiz may illustrate how context-specific these definitions are. It is arguable that, if one knows beforehand, for example, the title of the story containing a quote (i.e. how the author chooses to refer to each of the roles compared) it would be far easier to choose correctly between the available options.

But the fact remains, that different roles are discussed using definitions with near-perfect similarity. And while this confusion will resolve itself significantly as time goes by, nebulous role details can only result in an overall feeling of job insecurity for new or existing employees.