6 ms·

Nate Silver: What I need from statisticians

37 points by carlosgg 13y ago

mrcactu5 13y ago
I think data scientist is a sexed up term for a statistician. fuck-yeah
- deleted 13y ago
  [deleted]
chimeracoder 13y ago
> "I think data scientist is a sexed-up term for a statistician." As a statistician-and-engineer who is currently on the job market (my graduate program finishes this spring), I feel this pain. I've been referred to as a "data scientist" multiple times (that's even been my official title at work before), though I do still cringe sometimes when I hear the word, for this exact reason. That said, I don't usually present myself as a statistician, even though my degree is a statistics degree. Most people who hold statistics degrees are fairly lousy engineers[0], and I don't know of any other term that (concisely) expresses that I'm equally competent as a statistician and a (backend) engineer[1]. Of course, this is because many of these programs haven't yet caught up to the fact that computers exist and are still teaching statistics as if we're in a pre-computation era. The perfect solution is to fix this, and thereby fix the connotation of the word "statistician". It's the same reason I dislike the term "growth hacker" - really, that's just the way marketing should be done (ie, based on numbers and verifiable statistics). In a perfect world, all (competent) marketers would be "growth hackers". But many marketers aren't, and so we have to make up another cringe-worthy term for it. Unfortunately, that's a problem that's beyond my means to solve. So I bite my tongue and add the word "data scientist" to my resume anyway. [0] Usually self-proclaimed, too [1] ie, "I could work as a backend engineer if I wanted to/needed to, but I'm looking for work involving both skillsets"
- textminer 13y ago
  I've actually seen "data scientist" used to indicate "probably not that proficient as an actual engineer, but knows what he or she is talking about in terms of theory and can usually get a working model done in R, Matlab, or Python". There seems to be an additional jump in knowledge of systems, data structures, and algorithms in building up performant machine learning stacks (something which can add in practical knowledge and praxis, but steals a little from raw-ideas work). Citation: working in data science and machine learning engineer roles for nearly three years straight out of grad school in math.
casca 13y ago
We need a new term for statistician and data scientist is as good as any. For many years, the terms "statistics" and "statistician" have had negative undertones within the general public and renaming is a great way to overcome that.
- moocowduckquack 13y ago
  We need a new term for statistician and data scientist is as good as any. Do you have any figures to back that up? I think we should run some tests.
- nfoz 13y ago
  huh? what are these "negative undertones"?
  - jlgreco 13y ago
    All I can think of is jokes about statisticians on hunting trips: "A priest, an engineer, and a statistician go on a hunting trip. The priest sees a squirrel, shoots at it, and misses by 10' under. The engineer shoots 10' over it. The statistician yells 'Got it!'" Or perhaps, "76% of all statistics are made up."
  - johnpmayer 13y ago
    Probably the widely-help belief that anybody can come up with statistics for anything.
    - nfoz 13y ago
      Well, that statement is true. You hire a statistician if you want statistics that are correct. Maybe people undervalue the latter...
  - maaku 13y ago
    Lies, damn lies, and statistics.
- djrobstep 13y ago
nfoz 13y ago
Data-scientist is a sexed-up term for statistician without any expectation of mathematical expertise.
- monkeyspaw 13y ago
  What would be a preferable term, data engineer? I don't have a background in statistics, and I do a lot of things that would make PhD level statisticians unhappy... but the things I do are sufficient for my own purposes. Sometimes I don't need a mathematically proven process. I often find a use for something like a Monte Carlo simulation, without rigorous methodology, because it works for me, and for what I'm doing. By analogy, not every website needs a multiple tier, doubly redundant architecture. Sometimes a "dumb" setup with a single webserver and database on a single server gets the job done. In fact, when I worry less about scientific validity and focus more on what is necessary to get the job done... I make a lot more progress. It feels a lot like scientific elitism to me.
  - hharrison 13y ago
    Sounds like you agree with nfoz... you don't always need mathematical expertise ('scientific validity') to play with data, but when you do, you need a statistician. Really, it comes down whether you need to confirm your assumptions and quantify your uncertainty, etc. Often, in applied situations, you don't. In science, you do.
    - monkeyspaw 13y ago
      Yup. I don't plan on publishing anything I do in a peer reviewed journal. If I did, I would have to be way more cautious with a lot of my assumptions. By freeing myself of that level of effort, I can test a lot more stuff and build things a lot faster. In what I do, that's more important than having something be "scientifically accurate". Similarly, when I am coding a prototype, I don't do unit tests. Don't need them, and it takes me longer to develop.
      - kyzyl 13y ago
hharrison 13y ago
To respond to a bunch of other posters here: There's a fundamental difference between data scientist and statistician, I think. I see statistics as an academic discipline and data science as an applied discipline. More concretely, the statistics approach is: formulate question --> formulate hypothesis --> collect data in a controlled environment under a specific set of assumptions (i.e., perform an experiment) --> determine probability of the data given the hypothesis (and assumptions). While the data science approach is: hey look, we already have all this data --> generate predictions --> collect more data --> refine predictions. Of course, that's an over-generalization. But I think the different emphasis on hypothesis testing vs. machine learning/data mining is fundamental.
srean 13y ago
The way I have made personal peace with this is that I consider myself a better programmer than the median statistician and better in statistics and machine learning than a median programmer. Whether this is a useful spot to be in I have to find out. I can see that depending on the times this can either be an asset or a liability.
chockablock 13y ago
If you're a trained scientist, 'Data Science' sounds distinctly odd. What other kind of science is there? A friend likened it to going to a restaurant to do some 'Food Eating'.
- hharrison 13y ago
  All the more odd that Data Science is meant to be less scientific than Statistics (if the taxonomy I described in another post is correct).
- suchow 13y ago
  In Data Science, the word "Data" isn't meant to modify "Science" in the way that food "Food" modifies "Eating" in your friend's example. It's more like Political Science, where "Science" is used as a fancy way to say "the study and practice of".
- finiteloop 13y ago
  In general, if the subject includes "science" in the name, it probably isn't actually science. Political, data, social, etc. No chemist needs to say they study "chemical science."
mturmon 13y ago
"I think data scientist is a sexed-up term for a statistician." This statement, given by Silver to the annual meeting of the Joint Statistics Meetings (the main cross-organization stats conference), was guaranteed to be a crowd-pleaser for that audience. Unfortunately for them, it's not really true. The problem is that much of conventional academic statistics consists of proving theorems about model classes. This requires a lot of sophisticated analysis, but has turned rather vacuous. And much conventional applied statistics consists of computing diagnostics based on dubious modeling assumptions. Under pressure in the last 20 or so years from computer science, machine learning, computer vision, Moore's law, and the data avalanche, the discipline has changed, but not fast enough. As a result, a lot of what should be taught and researched in statistics departments has been co-opted by these other disciplines. And many people with a real problem would rather work with a "machine learning" person than a "statistics" person. The best summary of this state of affairs is Leo Breiman's essay (http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?handle=euclid.ss/1009213726&view=body&content-type=pdf_1 http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?ha...). The abstract of this essay is brutal: "There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large, complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools." Breiman was mathematically sophisticated, so it's not that he wasn't able to follow the theory he critiques, it's that he wasn't snowed by detail and could see its lack of relevance to real problems.
- bane 13y ago
  But I think data scientist, the way it's used today in the job market, is definitely a term for a person who works with real data using software tools and computers through a statistical lens (or a statistician who's comfortable munging data for their analysis). There's simply no term for someone who works with lots and lots of data (not necessarily "big data", I don't like that work) through a more general kind of lens that may or may not involve a statistical approach. When the term "data scientist" first really started to float around I got excited thinking it was describing the latter, when it just ended up describing the former. i.e. A data scientist role is more likely to answer a question like "What percent of people in the White House visitor logs show up in the past 24 months of Reuters News reporting?" vs. "Get me a list of people on the White House visitor logs who've shown up in the past 24 months of Reuters News reporting and build a relationship graph from both the news reports and the visitor logs and merge them." Both require the data munging part of grabbing the visitor log, grabbing the news reports and finding the names in the reports (controlling for various data quality issues). But the "data scientist" approach is to then jump from that onto a statistical answer, while the second approach is to build a social network that might be used for other purposes. These are very different kinds of approaches, with different kinds of outcomes. It's frustrating that a person in the second kind of approach doesn't have a name, "data scientist" would have been perfect, but the field seems to have gone a different direction with the term.
rohunati 13y ago
It could be argued that we shouldn't abandon the term "statistics." Data science is to statistics what mathematics is to physics, but we don't (nor do physicists) call it "number science."

37 points by carlosgg 13y ago

mrcactu5 13y ago

I think data scientist is a sexed up term for a statistician. fuck-yeah

deleted 13y ago
[deleted]

chimeracoder 13y ago

> "I think data scientist is a sexed-up term for a statistician." As a statistician-and-engineer who is currently on the job market (my graduate program finishes this spring), I feel this pain. I've been referred to as a "data scientist" multiple times (that's even been my official title at work before), though I do still cringe sometimes when I hear the word, for this exact reason. That said, I don't usually present myself as a statistician, even though my degree is a statistics degree. Most people who hold statistics degrees are fairly lousy engineers[0], and I don't know of any other term that (concisely) expresses that I'm equally competent as a statistician and a (backend) engineer[1]. Of course, this is because many of these programs haven't yet caught up to the fact that computers exist and are still teaching statistics as if we're in a pre-computation era. The perfect solution is to fix this, and thereby fix the connotation of the word "statistician". It's the same reason I dislike the term "growth hacker" - really, that's just the way marketing should be done (ie, based on numbers and verifiable statistics). In a perfect world, all (competent) marketers would be "growth hackers". But many marketers aren't, and so we have to make up another cringe-worthy term for it. Unfortunately, that's a problem that's beyond my means to solve. So I bite my tongue and add the word "data scientist" to my resume anyway. [0] Usually self-proclaimed, too [1] ie, "I could work as a backend engineer if I wanted to/needed to, but I'm looking for work involving both skillsets"

textminer 13y ago
I've actually seen "data scientist" used to indicate "probably not that proficient as an actual engineer, but knows what he or she is talking about in terms of theory and can usually get a working model done in R, Matlab, or Python". There seems to be an additional jump in knowledge of systems, data structures, and algorithms in building up performant machine learning stacks (something which can add in practical knowledge and praxis, but steals a little from raw-ideas work). Citation: working in data science and machine learning engineer roles for nearly three years straight out of grad school in math.

casca 13y ago

We need a new term for statistician and data scientist is as good as any. For many years, the terms "statistics" and "statistician" have had negative undertones within the general public and renaming is a great way to overcome that.

moocowduckquack 13y ago
We need a new term for statistician and data scientist is as good as any. Do you have any figures to back that up? I think we should run some tests.
nfoz 13y ago
huh? what are these "negative undertones"?
- jlgreco 13y ago
  All I can think of is jokes about statisticians on hunting trips: "A priest, an engineer, and a statistician go on a hunting trip. The priest sees a squirrel, shoots at it, and misses by 10' under. The engineer shoots 10' over it. The statistician yells 'Got it!'" Or perhaps, "76% of all statistics are made up."
- johnpmayer 13y ago
  Probably the widely-help belief that anybody can come up with statistics for anything.
  - nfoz 13y ago
    Well, that statement is true. You hire a statistician if you want statistics that are correct. Maybe people undervalue the latter...
- maaku 13y ago
  Lies, damn lies, and statistics.
djrobstep 13y ago

nfoz 13y ago

Data-scientist is a sexed-up term for statistician without any expectation of mathematical expertise.

monkeyspaw 13y ago
What would be a preferable term, data engineer? I don't have a background in statistics, and I do a lot of things that would make PhD level statisticians unhappy... but the things I do are sufficient for my own purposes. Sometimes I don't need a mathematically proven process. I often find a use for something like a Monte Carlo simulation, without rigorous methodology, because it works for me, and for what I'm doing. By analogy, not every website needs a multiple tier, doubly redundant architecture. Sometimes a "dumb" setup with a single webserver and database on a single server gets the job done. In fact, when I worry less about scientific validity and focus more on what is necessary to get the job done... I make a lot more progress. It feels a lot like scientific elitism to me.
- hharrison 13y ago
  Sounds like you agree with nfoz... you don't always need mathematical expertise ('scientific validity') to play with data, but when you do, you need a statistician. Really, it comes down whether you need to confirm your assumptions and quantify your uncertainty, etc. Often, in applied situations, you don't. In science, you do.
  - monkeyspaw 13y ago
    Yup. I don't plan on publishing anything I do in a peer reviewed journal. If I did, I would have to be way more cautious with a lot of my assumptions. By freeing myself of that level of effort, I can test a lot more stuff and build things a lot faster. In what I do, that's more important than having something be "scientifically accurate". Similarly, when I am coding a prototype, I don't do unit tests. Don't need them, and it takes me longer to develop.
    - kyzyl 13y ago

hharrison 13y ago

To respond to a bunch of other posters here: There's a fundamental difference between data scientist and statistician, I think. I see statistics as an academic discipline and data science as an applied discipline. More concretely, the statistics approach is: formulate question --> formulate hypothesis --> collect data in a controlled environment under a specific set of assumptions (i.e., perform an experiment) --> determine probability of the data given the hypothesis (and assumptions). While the data science approach is: hey look, we already have all this data --> generate predictions --> collect more data --> refine predictions. Of course, that's an over-generalization. But I think the different emphasis on hypothesis testing vs. machine learning/data mining is fundamental.

srean 13y ago

The way I have made personal peace with this is that I consider myself a better programmer than the median statistician and better in statistics and machine learning than a median programmer. Whether this is a useful spot to be in I have to find out. I can see that depending on the times this can either be an asset or a liability.

chockablock 13y ago

If you're a trained scientist, 'Data Science' sounds distinctly odd. What other kind of science is there? A friend likened it to going to a restaurant to do some 'Food Eating'.

hharrison 13y ago
All the more odd that Data Science is meant to be less scientific than Statistics (if the taxonomy I described in another post is correct).
suchow 13y ago
In Data Science, the word "Data" isn't meant to modify "Science" in the way that food "Food" modifies "Eating" in your friend's example. It's more like Political Science, where "Science" is used as a fancy way to say "the study and practice of".
finiteloop 13y ago
In general, if the subject includes "science" in the name, it probably isn't actually science. Political, data, social, etc. No chemist needs to say they study "chemical science."

mturmon 13y ago

"I think data scientist is a sexed-up term for a statistician." This statement, given by Silver to the annual meeting of the Joint Statistics Meetings (the main cross-organization stats conference), was guaranteed to be a crowd-pleaser for that audience. Unfortunately for them, it's not really true. The problem is that much of conventional academic statistics consists of proving theorems about model classes. This requires a lot of sophisticated analysis, but has turned rather vacuous. And much conventional applied statistics consists of computing diagnostics based on dubious modeling assumptions. Under pressure in the last 20 or so years from computer science, machine learning, computer vision, Moore's law, and the data avalanche, the discipline has changed, but not fast enough. As a result, a lot of what should be taught and researched in statistics departments has been co-opted by these other disciplines. And many people with a real problem would rather work with a "machine learning" person than a "statistics" person. The best summary of this state of affairs is Leo Breiman's essay (http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?handle=euclid.ss/1009213726&view=body&content-type=pdf_1 http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?ha...). The abstract of this essay is brutal: "There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large, complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools." Breiman was mathematically sophisticated, so it's not that he wasn't able to follow the theory he critiques, it's that he wasn't snowed by detail and could see its lack of relevance to real problems.

bane 13y ago
But I think data scientist, the way it's used today in the job market, is definitely a term for a person who works with real data using software tools and computers through a statistical lens (or a statistician who's comfortable munging data for their analysis). There's simply no term for someone who works with lots and lots of data (not necessarily "big data", I don't like that work) through a more general kind of lens that may or may not involve a statistical approach. When the term "data scientist" first really started to float around I got excited thinking it was describing the latter, when it just ended up describing the former. i.e. A data scientist role is more likely to answer a question like "What percent of people in the White House visitor logs show up in the past 24 months of Reuters News reporting?" vs. "Get me a list of people on the White House visitor logs who've shown up in the past 24 months of Reuters News reporting and build a relationship graph from both the news reports and the visitor logs and merge them." Both require the data munging part of grabbing the visitor log, grabbing the news reports and finding the names in the reports (controlling for various data quality issues). But the "data scientist" approach is to then jump from that onto a statistical answer, while the second approach is to build a social network that might be used for other purposes. These are very different kinds of approaches, with different kinds of outcomes. It's frustrating that a person in the second kind of approach doesn't have a name, "data scientist" would have been perfect, but the field seems to have gone a different direction with the term.

rohunati 13y ago

It could be argued that we shouldn't abandon the term "statistics." Data science is to statistics what mathematics is to physics, but we don't (nor do physicists) call it "number science."