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This is one of the reasons that I really dislike the term "data scientist." Essentially the job is Analyst, which is something we've had for many years. It's no

This is one of the reasons that I really dislike the term "data scientist." Essentially the job is Analyst, which is something we've had for many years. It's not a bad thing, but there's no reason to embellish it into something it's not. The primary reason I dislike this term, though, is because all scientists are data scientists. Data is how the whole thing works.

yummyfajitas 10y ago
Originally "data scientist" meant a statistician who could write distributed systems to implement their algorithm in production. Now that the term is diluted, I propose we use adopt the banking industry term "quant developer" to refer to that.
- trendia 10y ago
  How about "data monkey"? At least then when the corporations post job ads and bring up buzzwords in their meetings, we can silently laugh at them.
- zintinio5 10y ago
  I'm sorry, but I'm not sure that you're correct that a data scientist is a statistician who can write distributed systems. Problems have been solved for decades by people with quantitative training, from well before distributed systems (or computers) existed. Very roughly, a data scientist is a person with a combination of quantitative training and some programming skills, who is able to effectively analyze (and explain) different kinds of data, be they audio, text, census information, or whatever it might be. What we call data scientists today are the engineers, computer scientists, and mathematicians of yesterday.
- pkaye 10y ago
  I have no experience in Data Science. How much need is there for a distributed system? How many data points would one need to necessitate it? What if we had a billion data points. Would it be sufficient to run on a fast system overnight to crunch data?
  - yummyfajitas 10y ago
    According to me (note zintinio's reply - he disagrees), the etymology of the term is the following. Back in the day, if you had small data you didn't need a data scientist. You needed a statistician. He'd do some shit in SAS/Python/etc, reading one CSV and writing another. Then the developers could run that on a beefy server with cron and push the CSV output someplace else. At some point during the 2000's this stopped working - things became complicated and tangled enough that you couldn't just munge CSVs like this. You needed folks who understood the math well enough to come up with algorithms, and who also understood the computer science well enough to scale out. These folks were termed "data scientists". Banks call them "quant developers". Very often you don't need a distributed system, or anything that isn't trivially parallelizable. Most of the time when I make money it's based on a CSV < 100GB - often it fits in ram. Nowadays I don't think it's misleading to call yourself a "data scientist" if you can only handle such data sets. I recently learned that Facebook calls their business analysts data scientists. I guess it's just sexier.
xapata 10y ago
Science is more than just data. A theoretical physicist is a scientist who does not necessarily care about data collection. Some anthropologists focus on case studies, rather than data, especially if they're just doing descriptive study rather than predictive.
throw_away_777 10y ago
The problem is that industry has coopted the term data analyst to mean something more like data reporter or data descriptor. There often isn't a difference between business intelligence and data analyst. Many data analysts are using tools like Excel, and don't have good programming skills. A data scientist will generally be involved with prediction, and use more sophisticated tools than Excel. This isn't to knock on data analysts (or Excel), they do good and useful work. The proof is in the numbers - a data scientist makes a significantly higher salary than a data analyst. Similarly quantitative analyst got taken over by finance. The term statistician is actually pretty accurate (data science is basically applied statistics), but usually people don't call themselves statisticians unless they have a degree in statistics. With that said, the term data scientist is not at all well-defined, and the field is quite diverse.

yummyfajitas 10y ago

Originally "data scientist" meant a statistician who could write distributed systems to implement their algorithm in production. Now that the term is diluted, I propose we use adopt the banking industry term "quant developer" to refer to that.

trendia 10y ago
How about "data monkey"? At least then when the corporations post job ads and bring up buzzwords in their meetings, we can silently laugh at them.
zintinio5 10y ago
I'm sorry, but I'm not sure that you're correct that a data scientist is a statistician who can write distributed systems. Problems have been solved for decades by people with quantitative training, from well before distributed systems (or computers) existed. Very roughly, a data scientist is a person with a combination of quantitative training and some programming skills, who is able to effectively analyze (and explain) different kinds of data, be they audio, text, census information, or whatever it might be. What we call data scientists today are the engineers, computer scientists, and mathematicians of yesterday.
pkaye 10y ago
I have no experience in Data Science. How much need is there for a distributed system? How many data points would one need to necessitate it? What if we had a billion data points. Would it be sufficient to run on a fast system overnight to crunch data?
- yummyfajitas 10y ago
  According to me (note zintinio's reply - he disagrees), the etymology of the term is the following. Back in the day, if you had small data you didn't need a data scientist. You needed a statistician. He'd do some shit in SAS/Python/etc, reading one CSV and writing another. Then the developers could run that on a beefy server with cron and push the CSV output someplace else. At some point during the 2000's this stopped working - things became complicated and tangled enough that you couldn't just munge CSVs like this. You needed folks who understood the math well enough to come up with algorithms, and who also understood the computer science well enough to scale out. These folks were termed "data scientists". Banks call them "quant developers". Very often you don't need a distributed system, or anything that isn't trivially parallelizable. Most of the time when I make money it's based on a CSV < 100GB - often it fits in ram. Nowadays I don't think it's misleading to call yourself a "data scientist" if you can only handle such data sets. I recently learned that Facebook calls their business analysts data scientists. I guess it's just sexier.

xapata 10y ago

Science is more than just data. A theoretical physicist is a scientist who does not necessarily care about data collection. Some anthropologists focus on case studies, rather than data, especially if they're just doing descriptive study rather than predictive.

throw_away_777 10y ago

The problem is that industry has coopted the term data analyst to mean something more like data reporter or data descriptor. There often isn't a difference between business intelligence and data analyst. Many data analysts are using tools like Excel, and don't have good programming skills. A data scientist will generally be involved with prediction, and use more sophisticated tools than Excel. This isn't to knock on data analysts (or Excel), they do good and useful work. The proof is in the numbers - a data scientist makes a significantly higher salary than a data analyst. Similarly quantitative analyst got taken over by finance. The term statistician is actually pretty accurate (data science is basically applied statistics), but usually people don't call themselves statisticians unless they have a degree in statistics. With that said, the term data scientist is not at all well-defined, and the field is quite diverse.