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It's just a name. "Data science" still requires statistics and anyone who calls themselves a data scientist were (hopefully) trained in statistics at one point

It's just a name. "Data science" still requires statistics and anyone who calls themselves a data scientist were (hopefully) trained in statistics at one point or another, whether formally or not. For those who don't use statistics properly, well, they aren't doing data science properly either. It's all statistics.

theseatoms 11y ago
> For those who don't use statistics properly, well, they aren't doing data science properly either. It's all statistics. I agree wholeheartedly. It's not just data science, but all science that requires good statistics. EDIT: IANADS, but data science seems tightly intertwined with statistics, to such a degree that I've had to double-check the difference multiple times. (Seems to be mostly terminology-based, tbh.)
christopheraden 11y ago
Definitely agree. Part of the problem is that data science doesn't have nearly the same formalism in its definition that statistics does. What's the difference between BI's, Data Miners, Data Analysts, Data Scientists, etc? The tools used to arrive at conclusions (R vs. Python vs. SAS vs. Tableau/Excel/SPSS) doesn't seem like a good way of differentiating the roles. A more useful discriminator would be the application of statistics (BI vs. Biostatistician, for instance), the depth and complexity of the statistical algorithms used, and whether the main use is stat inference or prediction (machine learning doesn't seem to focus on inference a whole lot, for example).
wfo 11y ago
I think the name means something, data science to me means you're more focused on the tools and less on the theory. And that a VERY dangerous attitude to take in statistics. It's really, really easy to apply tools and get a model that looks quantitative and explanatory and good but is actually trash. It's really hard to tell the difference between good work and bad work. Statistics is very, very complicated and finicky in theory and practice and I think we'll need a better way to tell the charlatans from the experts. Maybe the title will work as a general rule of thumb as people self-select -- data scientist = knows how to load a data set into R and type 'lm', statistician = actual expert
- washedup 11y ago
  People who attempt data science without validating the statistics should eventually end up with a pretty bad track record and leave the "data scientist" job market. My perception of what data scientist currently means is a statistician who knows how to program machine learning algorithms and manage large data sets. Based on what you are saying, there are a lot of impostors out there, which is most likely true. However, every time I see a job posting for a data scientist, it almost always requires some formal math or statistics background.

theseatoms 11y ago

> For those who don't use statistics properly, well, they aren't doing data science properly either. It's all statistics. I agree wholeheartedly. It's not just data science, but all science that requires good statistics. EDIT: IANADS, but data science seems tightly intertwined with statistics, to such a degree that I've had to double-check the difference multiple times. (Seems to be mostly terminology-based, tbh.)

christopheraden 11y ago

Definitely agree. Part of the problem is that data science doesn't have nearly the same formalism in its definition that statistics does. What's the difference between BI's, Data Miners, Data Analysts, Data Scientists, etc? The tools used to arrive at conclusions (R vs. Python vs. SAS vs. Tableau/Excel/SPSS) doesn't seem like a good way of differentiating the roles. A more useful discriminator would be the application of statistics (BI vs. Biostatistician, for instance), the depth and complexity of the statistical algorithms used, and whether the main use is stat inference or prediction (machine learning doesn't seem to focus on inference a whole lot, for example).

wfo 11y ago

I think the name means something, data science to me means you're more focused on the tools and less on the theory. And that a VERY dangerous attitude to take in statistics. It's really, really easy to apply tools and get a model that looks quantitative and explanatory and good but is actually trash. It's really hard to tell the difference between good work and bad work. Statistics is very, very complicated and finicky in theory and practice and I think we'll need a better way to tell the charlatans from the experts. Maybe the title will work as a general rule of thumb as people self-select -- data scientist = knows how to load a data set into R and type 'lm', statistician = actual expert

washedup 11y ago
People who attempt data science without validating the statistics should eventually end up with a pretty bad track record and leave the "data scientist" job market. My perception of what data scientist currently means is a statistician who knows how to program machine learning algorithms and manage large data sets. Based on what you are saying, there are a lot of impostors out there, which is most likely true. However, every time I see a job posting for a data scientist, it almost always requires some formal math or statistics background.