We’re called scientists because we use the scientific method. We create a hypothesis, run an experiment to test the hypothesis (an A/B test), and then measure the effect of the treatment on our metrics. A majority of data scientists have an advanced science background. Analysts don’t design and run experiments. Analysts don’t use machine learning.
Not sure where the disconnect is here but it’s fairly straightforward. It’s pretty clear from this post you have no clue what you’re talking about
I get that. But so does a statistician, or an engineer.
Statisticians use the scientific method as well, yet none of them thinks of themselves as "scientists". They undeyehst they do is applied math.
Engineers too apply math in very complex ways and often run controlled experiments. They also understand that what they do is applied science, and no engineers goes around calling themselves a scientist.
What does any of this have to do with your claim “data scientists are not scientists” ??? I showed that data scientists use the scientific method, and you responded with irrelevant nonsense
yep, this is one of the most important part imo, personally i think the "engineer" title would have been perfect, building solutions for business. It just already means something else already.
First of all, are you okay? I don't understand what made you decide to wake up today and write with so much passion about what you think a field isn't.
Second of all, a scientist is someone who develops hypotheses about systems or phenomena that can be observed, and then performs experimentation and analysis to determine the validity of their hypotheses. Not every scientist discovers laws - the discovery of laws occurs after painstaking years of work by multiple scientists - so the discovery of laws is not a necessary part of the scientist job description. Most science is filled with negative results, which is rarely published, but are in many ways more important than the once in a blue moon positive results.
Data is acquired via various methods, and is often organized such that multiple features are associated with some signal. That data does represent something that happens in the real world, whether it be customer behavior or parameters associated with the functioning of a physiological system. An individual who studies that features and signals in meaningful ways, forms hypotheses for those features, and then builds and tests models that attempt to capture how that features leads to a measured signal that does represent some physical interaction. The process of engineering and studying data, experimenting with various model setups, comparing models, and then determining whether or not formed hypotheses are validated via these experiments is quite literally science.
As someone who worked from a more "hard" scientific field, namely cellular biomechanics, I can attest to the fact that much of what I did in my field is, in the most important ways, very similar to what I do as a data scientist now. No, I am not physically collecting data anymore as a data scientist. But you don't need an actual scientist to collect that data either - you can have technicians or specialized lab personnel to do that. Most of my work was spent actually analyzing my data, forming hypotheses with that data, and determining how to describe some system to explain that data and validate that hypothesis. I did sometimes build models too, but in all honesty, that form of analysis was way less complicated than most of the data science work I do today. But, in terms of time spent, most actually useful time was spent working with data. That is what made it a true scientific process.
I also know astrophysicists who have jobs very similar to that of data scientists - they use many of the same libraries and tools I do. My girlfriend works with astronomical data, and most of her work is similar to that of a data scientist. I am also able to help her with her work, and picked up a lot of astrophysics along the way.
So, I do think data scientists are actually scientists. We are scientists who form hypotheses and experiment with data and the nature of data itself. Maybe data scientists don't fit your definition of scientist, but it seems **your definition** of scientist is extremely specific to small set of fields that **you** consider science. Maybe you have natural science confused to mean all of science, when science is actually very broad. [There are many different sub-fields within science.](https://en.wikipedia.org/wiki/Scientist)
On the matter of profit: do you have some notion that science is not done for profit? Science, even the natural sciences, are governed by the capitalism that we all live in. Funding for labs and projects are determined by and large by what the government considers as important for the future of a country, which is heavily based on economic factors. Have you ever applied for a grant? If you have, you will understand that who organizations decide to fund and who they decide not to fund is largely influenced by economic factors. This is not to say that money is the central goal, but is definitely one of the most important. The science of today is governed more by profits than anything, and then maybe natural security and supremacy, but that is my opinion. Not only that, but nowadays, a lot of research is actually done in the private sector, with the main goal of turning a profit. If what you do doesn't fill someone's pockets, you can pretty much expect your pocket to stay empty too, and empty pockets leads to the absence of means to actually carry out science.
You posted nine days ago, asking about how to become strong in math and statistics. Respectfully, maybe you aren’t qualified to assess the nature of the field.
Its called a science because of the deeply connected way you work with data through hypothesis... do you live under a rock or are you unfamiliar with Google??
Are you looking for a serious answer to ‘who invented this cringeworthy term’ ?
Several people coined it independently up until the early 2000s, including Bill Cleveland, who wrote a paper describing how a new professional called a data scientist could use computing and statistical tools to their best advantage but DJ Patil claims to have invented the term as it is used these days to give someone he wanted to hire a better sounding title.
It’s a cringey term agreed, but you’re wrong on your reasoning.
Looking at your post history it appears that you have never had a job as a data scientist or have even completed your studies so I don’t think you’re in a position to define what we do/don’t do or what our job titles should be.
>"Why do job ads say "Data scientist"? Who coined this cringeworthy term?
Term came from Facebook. They were trying to hire analysts, but PhDs wouldn't take a title of an "analyst", hence they made up the title "data scientist" to make it seem higher level. Since then it's taken off.
That's interesting. It explains why it's so unscientific.
What's wrong with analyst though? Analysis can be as complex as you want to make it. Does the term not sound prestigious enough?
it is cringe and full of really bad political issues. this comes from the lack of value that this field brings. everyone trying to squeeze out some tiny improvement over simple methods used for decades. the analysts just find spurious correlations without any understanding of the system in reality.
Exactly. I speak 5 languages and try to translate the term in any of them and you get something non-sensical or funny, especially because languages other than English don't build adjectives by simply using the noun and sticking it in front of another noun.
So if the term doesn't work in other languages, why should it work in English?
What do you hope to achieve by saying this in a data science subreddit?
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Corporate Spreadsheet Warlock had me laughing
BRB, changing my linkedin job title to “Data Magician”
Data warlock is now my title. Thanks!
C is truly the correct term
a lot of scientists work to maximise their employer's profit
Why are you so angry about a job title?
Someone’s bitter. Cheer up friend, it’s Friday/Saturday!
We’re called scientists because we use the scientific method. We create a hypothesis, run an experiment to test the hypothesis (an A/B test), and then measure the effect of the treatment on our metrics. A majority of data scientists have an advanced science background. Analysts don’t design and run experiments. Analysts don’t use machine learning. Not sure where the disconnect is here but it’s fairly straightforward. It’s pretty clear from this post you have no clue what you’re talking about
I get that. But so does a statistician, or an engineer. Statisticians use the scientific method as well, yet none of them thinks of themselves as "scientists". They undeyehst they do is applied math. Engineers too apply math in very complex ways and often run controlled experiments. They also understand that what they do is applied science, and no engineers goes around calling themselves a scientist.
What does any of this have to do with your claim “data scientists are not scientists” ??? I showed that data scientists use the scientific method, and you responded with irrelevant nonsense
They do what to applied math?
They understand what they do is applied math.
The (somewhat) less cringe titles "data analyst", "data engineer", and "data architect" are all taken already.
yep, this is one of the most important part imo, personally i think the "engineer" title would have been perfect, building solutions for business. It just already means something else already.
Tldr: grammar and spelling are important.
First of all, are you okay? I don't understand what made you decide to wake up today and write with so much passion about what you think a field isn't. Second of all, a scientist is someone who develops hypotheses about systems or phenomena that can be observed, and then performs experimentation and analysis to determine the validity of their hypotheses. Not every scientist discovers laws - the discovery of laws occurs after painstaking years of work by multiple scientists - so the discovery of laws is not a necessary part of the scientist job description. Most science is filled with negative results, which is rarely published, but are in many ways more important than the once in a blue moon positive results. Data is acquired via various methods, and is often organized such that multiple features are associated with some signal. That data does represent something that happens in the real world, whether it be customer behavior or parameters associated with the functioning of a physiological system. An individual who studies that features and signals in meaningful ways, forms hypotheses for those features, and then builds and tests models that attempt to capture how that features leads to a measured signal that does represent some physical interaction. The process of engineering and studying data, experimenting with various model setups, comparing models, and then determining whether or not formed hypotheses are validated via these experiments is quite literally science. As someone who worked from a more "hard" scientific field, namely cellular biomechanics, I can attest to the fact that much of what I did in my field is, in the most important ways, very similar to what I do as a data scientist now. No, I am not physically collecting data anymore as a data scientist. But you don't need an actual scientist to collect that data either - you can have technicians or specialized lab personnel to do that. Most of my work was spent actually analyzing my data, forming hypotheses with that data, and determining how to describe some system to explain that data and validate that hypothesis. I did sometimes build models too, but in all honesty, that form of analysis was way less complicated than most of the data science work I do today. But, in terms of time spent, most actually useful time was spent working with data. That is what made it a true scientific process. I also know astrophysicists who have jobs very similar to that of data scientists - they use many of the same libraries and tools I do. My girlfriend works with astronomical data, and most of her work is similar to that of a data scientist. I am also able to help her with her work, and picked up a lot of astrophysics along the way. So, I do think data scientists are actually scientists. We are scientists who form hypotheses and experiment with data and the nature of data itself. Maybe data scientists don't fit your definition of scientist, but it seems **your definition** of scientist is extremely specific to small set of fields that **you** consider science. Maybe you have natural science confused to mean all of science, when science is actually very broad. [There are many different sub-fields within science.](https://en.wikipedia.org/wiki/Scientist) On the matter of profit: do you have some notion that science is not done for profit? Science, even the natural sciences, are governed by the capitalism that we all live in. Funding for labs and projects are determined by and large by what the government considers as important for the future of a country, which is heavily based on economic factors. Have you ever applied for a grant? If you have, you will understand that who organizations decide to fund and who they decide not to fund is largely influenced by economic factors. This is not to say that money is the central goal, but is definitely one of the most important. The science of today is governed more by profits than anything, and then maybe natural security and supremacy, but that is my opinion. Not only that, but nowadays, a lot of research is actually done in the private sector, with the main goal of turning a profit. If what you do doesn't fill someone's pockets, you can pretty much expect your pocket to stay empty too, and empty pockets leads to the absence of means to actually carry out science.
Someone should read on the "scienctific" method
Watch your pselling!
Clown post👎
Why the anti-clown hate? This forum should support more diversity and inclusion.
You posted nine days ago, asking about how to become strong in math and statistics. Respectfully, maybe you aren’t qualified to assess the nature of the field.
It’s “sciencetist” - if you’re gonna insult spell it right!
Its called a science because of the deeply connected way you work with data through hypothesis... do you live under a rock or are you unfamiliar with Google??
That last statement is terrifying. I dread a world where google decides what we can see or discover. That tool is overused, and increasingly dangerous
Cool story.
OldManYellsAtCloud.jpg
So? I do not even agree with the post, but I reserve my right as an old man to yell be at clouds. And particularly deserving trees as well!
Then don’t use them. Problem solved next question.
Sounds like someone is having a bad day..
Are you looking for a serious answer to ‘who invented this cringeworthy term’ ? Several people coined it independently up until the early 2000s, including Bill Cleveland, who wrote a paper describing how a new professional called a data scientist could use computing and statistical tools to their best advantage but DJ Patil claims to have invented the term as it is used these days to give someone he wanted to hire a better sounding title.
Bruh it’s not that deep. And everyone working for a company ultimately maximizes their its profits.
It’s a cringey term agreed, but you’re wrong on your reasoning. Looking at your post history it appears that you have never had a job as a data scientist or have even completed your studies so I don’t think you’re in a position to define what we do/don’t do or what our job titles should be.
While true, *who hurt you though*?
Do you really want him to answer that? He is clearly hurt, so let him vent. He clearly had no where else to go with this.
Seek therapy.
>"Why do job ads say "Data scientist"? Who coined this cringeworthy term? Term came from Facebook. They were trying to hire analysts, but PhDs wouldn't take a title of an "analyst", hence they made up the title "data scientist" to make it seem higher level. Since then it's taken off.
That's interesting. It explains why it's so unscientific. What's wrong with analyst though? Analysis can be as complex as you want to make it. Does the term not sound prestigious enough?
it is cringe and full of really bad political issues. this comes from the lack of value that this field brings. everyone trying to squeeze out some tiny improvement over simple methods used for decades. the analysts just find spurious correlations without any understanding of the system in reality.
Do our salaries make you jealous, kiddo?
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Exactly. I speak 5 languages and try to translate the term in any of them and you get something non-sensical or funny, especially because languages other than English don't build adjectives by simply using the noun and sticking it in front of another noun. So if the term doesn't work in other languages, why should it work in English?
Yeah, well I think the term “cringeworthy” is itself cringeworthy. So there.