8 fixed misattribution
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People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call this activity "data science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at TwitterMichael Hochster's answer on Quora:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call this activity "data science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at Twitter:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call this activity "data science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Michael Hochster's answer on Quora:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

7 misc
source | link

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call themthis activity "data "science"science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at Twitter:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call them "data "science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at Twitter:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call this activity "data science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at Twitter:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

6 added 58 characters in body
source | link

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call them "data "science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at Twitter:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average. Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at Twitter:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

People define Data Science differently, but I think that the common part is:

  • practical knowledge how to deal with data,
  • practical programming skills.

Contrary to its name, it's rarely "science". That is, in data science the emphasis is on practical results (like in engineering), not proofs, mathematical purity or rigor characteristic to academic science. Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

Statistician is not necessary a programmer (may use pen & paper and a dedicated software). Also, some job calls in data science have nothing to do with statistics. E.g. it's data engineering like processing big data, even if the most advanced maths there may be calculating average (personally I wouldn't call them "data "science", though). Moreover, "data science" is hyped, so tangentially related jobs use this title - to lure the applicants or raise ego of the current workers.

I like the taxonomy from Doing Data Science at Twitter:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

In that sense, Type A Data Scientist is a statistician who can program. But, even for quantitive part, there may be people with background more in computer science (e.g. machine learning) than regular statistics, or ones focusing e.g. on data visualization.

And The Data Science Venn Diagram (here: hacking ~ programming):

The Data Science Venn Diagram

see also alternative Venn diagrams (this and that). Or even a tweet, while humorous, showing a balanced list of typical skills and activities of a data scientist:

a data scientist should be able to

See also this post: Data scientist - statistician, programmer, consultant and visualizer?.

5 more links to venn diagrams
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4 edited body
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