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Is "data science" a new hipper name for "statistics"?

neilsharma 12y ago
Data Science can mean machine learning, statistics, "data engineering" (the IT side of Big Data), SQL Monkey, data cleaning, data mining, data analytics, data visualization, etc. So its like statistics, except there may not be math or statistics. Depends heavily on the role.
- brianliou91 12y ago
  I would add "domain expertise" but other than that I totally agree!
kiyoto 12y ago
Not quite. I used to be a 'data scientist' in finance. Their jargon for this position is "quantitative analyst". The two definitely have intersections. Here is what's in DataScience \cap Statistics 1. A solid understanding of basic inferential statistics. 2. A healthy dose of skepticism about drawing conclusions from data. 3. The ability to model the problem and communicate how data fits into the model. Here are the skills that most data scientists I know do NOT have but statisticians do. 1. A solid mathematical foundation of statistics: I am guessing because this stuff is only taught in graduate-level statistics classes, which most data scientists have never taken. 2. The research frontier of statistics, or the "what's new" of the statistics academia. 3. Coming up with new statistical methodologies (or discovering anything new and contributing to the foundation of statistics). This is not entirely true, but most data scientists I know are users of existing techniques/ideas. Here are the skills that most statisticians I know do NOT have but data scientists do. 1. "Vocational" programming skills: most statisticians know R/Matlab, but many of them lack the experience of acquiring and cleaning data before doing analysis. I don't think this is a matter of ability but experience and lack of exposure. 2. Communicating statistical conclusions to non-statisticians: some of my statistician friends have a knack for this, but it is a skill that's hugely important for data scientists. People who want to be data-driven and ask for statistical conclusions far outnumber people who can actually understand statistical subtleties, and almost by survivors' bias, all data scientists I know are excellent at getting their conclusions across the organization even if others do not fully understand the full ramificaiton.
kxr 12y ago
Rob Hyndman is a Professor of Statistics and was recently wondering the same thing [0]. [0] "Am I data scientist?" http://robjhyndman.com/hyndsight/am-i-a-data-scientist/ http://robjhyndman.com/hyndsight/am-i-a-data-scientist/

Data Science can mean machine learning, statistics, "data engineering" (the IT side of Big Data), SQL Monkey, data cleaning, data mining, data analytics, data visualization, etc. So its like statistics, except there may not be math or statistics. Depends heavily on the role.

brianliou91 12y ago
I would add "domain expertise" but other than that I totally agree!

kiyoto 12y ago

Not quite. I used to be a 'data scientist' in finance. Their jargon for this position is "quantitative analyst". The two definitely have intersections. Here is what's in DataScience \cap Statistics 1. A solid understanding of basic inferential statistics. 2. A healthy dose of skepticism about drawing conclusions from data. 3. The ability to model the problem and communicate how data fits into the model. Here are the skills that most data scientists I know do NOT have but statisticians do. 1. A solid mathematical foundation of statistics: I am guessing because this stuff is only taught in graduate-level statistics classes, which most data scientists have never taken. 2. The research frontier of statistics, or the "what's new" of the statistics academia. 3. Coming up with new statistical methodologies (or discovering anything new and contributing to the foundation of statistics). This is not entirely true, but most data scientists I know are users of existing techniques/ideas. Here are the skills that most statisticians I know do NOT have but data scientists do. 1. "Vocational" programming skills: most statisticians know R/Matlab, but many of them lack the experience of acquiring and cleaning data before doing analysis. I don't think this is a matter of ability but experience and lack of exposure. 2. Communicating statistical conclusions to non-statisticians: some of my statistician friends have a knack for this, but it is a skill that's hugely important for data scientists. People who want to be data-driven and ask for statistical conclusions far outnumber people who can actually understand statistical subtleties, and almost by survivors' bias, all data scientists I know are excellent at getting their conclusions across the organization even if others do not fully understand the full ramificaiton.

kxr 12y ago

Rob Hyndman is a Professor of Statistics and was recently wondering the same thing [0]. [0] "Am I data scientist?" http://robjhyndman.com/hyndsight/am-i-a-data-scientist/ http://robjhyndman.com/hyndsight/am-i-a-data-scientist/