... according to O'Reilly, yes, it is.
Data Scientist (DS) - a very popular label that seems to be associated with people kind of like us-OR-people these days (just like "analytics" has been for the last few years), but no one is completely sure exactly what it is. As a result, many of us are reluctant to call ourselves a data scientist, or don't know how to make the transition to be called one (see my next post on where to start). There is the Venn diagram, and examples from famous DS people like Nate Silver and Hilary Mason (who are identified more as statisticians than anything else), but confusions are still bountiful.
OR has always had a bit of an identity crisis - how many jobs have you seen with the words "operations research" in the title or description? Is "Data Science" here to help?
O'Reilly published a book, titled "Analyzing the Analyzers", which discusses the results and implications for people in these related fields, based on a survey they ran in mid-2012, with whom they consider as data scientists, and "how they viewed their skills, careers, and experiences with prospective employers". Their goal, best summarised in their own words, are, "in the broad Analytics / Data Science / Big Data / Applied Stats / Machine Learning space, ...to define these new fields better, and we hope the results will help people such as yourself talk about how your skills and your work fit in with everyone else's."
The main result was summarised into a 5X4 matrix (credit: O'Reilly), showing where the survey respondents are in terms of skills / expertise and the label they associate themselves with.
The list of skills they grouped under "Math / OR" are: Optimisation, Math, Graphical Models, Bayesian / Monte Carlo Statistics, Algorithms, Simulation. Sounds familiar indeed.
Hooray for the mention of OR as a Data Science skillset!
I recommend reading the full report for more details. Here is a summary to give you a taste:
Have you got what it takes to call yourself a Data Scientist? OR folks, see my next post on how to upgrade yourself (umm, didn't mean to make you sound like machines).
This is perhaps the clearest I've seen anyone link OR to Data Science. Or perhaps, depending on how you read it...it shows that some Data Scientists are OR people. OR is a subset of Data Science skills.
Data Scientist (DS) - a very popular label that seems to be associated with people kind of like us-OR-people these days (just like "analytics" has been for the last few years), but no one is completely sure exactly what it is. As a result, many of us are reluctant to call ourselves a data scientist, or don't know how to make the transition to be called one (see my next post on where to start). There is the Venn diagram, and examples from famous DS people like Nate Silver and Hilary Mason (who are identified more as statisticians than anything else), but confusions are still bountiful.
OR has always had a bit of an identity crisis - how many jobs have you seen with the words "operations research" in the title or description? Is "Data Science" here to help?
O'Reilly published a book, titled "Analyzing the Analyzers", which discusses the results and implications for people in these related fields, based on a survey they ran in mid-2012, with whom they consider as data scientists, and "how they viewed their skills, careers, and experiences with prospective employers". Their goal, best summarised in their own words, are, "in the broad Analytics / Data Science / Big Data / Applied Stats / Machine Learning space, ...to define these new fields better, and we hope the results will help people such as yourself talk about how your skills and your work fit in with everyone else's."
The main result was summarised into a 5X4 matrix (credit: O'Reilly), showing where the survey respondents are in terms of skills / expertise and the label they associate themselves with.
The list of skills they grouped under "Math / OR" are: Optimisation, Math, Graphical Models, Bayesian / Monte Carlo Statistics, Algorithms, Simulation. Sounds familiar indeed.
Hooray for the mention of OR as a Data Science skillset!
I recommend reading the full report for more details. Here is a summary to give you a taste:
(The last point above: it wasn't too comprehensive, so don't expect too much. More of a taster.)
- Four data scientist clusters
- Cases in miscommunication between data scientists and organisations looking to hire
- Why "T-shaped" data scientists have an advantage in breadth and depth of skills
- How organisations can apply the survey results to identify, train, integrate, team up, and promote data scientists
Have you got what it takes to call yourself a Data Scientist? OR folks, see my next post on how to upgrade yourself (umm, didn't mean to make you sound like machines).
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