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We are now at 9 categories after a few updates. Just like there are a few categories of statisticians (biostatisticians, statisticians, econometricians, operations research specialists, actuaries) or business analysts (marketing-oriented, product-oriented, finance-oriented, etc.) we have different categories of data scientists. First, many data scientists have a job title different from data scientist, mine for instance is co-founder. Check the "related articles" section below to discover 400 potential job titles for data scientists.

Categories of data scientists

  • Those strong in statistics: they sometimes develop new statistical theories for big data, that even traditional statisticians are not aware of. They are expert in statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, modeling, predictive modeling and other related techniques.
  • Those strong in mathematics: NSA (national security agency) or defense/military people working on big data, astronomers, and operations research people doing analytic business optimization (inventory management and forecasting, pricing optimization, supply chain, quality control, yield optimization) as they collect, analyse and extract value out of data.
  • Those strong in data engineering, Hadoop, database/memory/file systems optimization and architecture, API's, Analytics as a Service, optimization of data flows, data plumbing.
  • Those strong in machine learning / computer science (algorithms, computational complexity)
  • Those strong in business, ROI optimization, decision sciences, involved in some of the tasks traditionally performed by business analysts in bigger companies (dashboards design, metric mix selection and metric definitions, ROI optimization, high-level database design)
  • Those strong in production code development, software engineering (they know a few programming languages)
  • Those strong in visualization
  • Those strong in GIS, spatial data, data modeled by graphs, graph databases
  • Those strong in a few of the above. After 20 years of experience across many industries, big and small companies (and lots of training), I'm strong both in stats, machine learning, business, mathematics and more than just familiar with visualization and data engineering. This could happen to you as well over time, as you build experience. I mention this because so many people still think that it is not possible to develop a strong knowledge base across multiple domains that are traditionally perceived as separated (the silo mentality). Indeed, that's the very reason why data science was created.

Most of them are familiar or expert in big data. 

There are other ways to categorize data scientists, see for instance our article on Taxonomy of data scientists. A different categorization would be creative versus mundane. The "creative" category has a better future, as mundane can be outsourced (anything published in textbooks or on the web can be automated or outsourced - job security is based on how much you know that no one else know or can easily learn). Along the same lines, we have science users (those using science, that is, practitioners; often they do not have a PhD), innovators (those creating new science, called researchers), and hybrids. Most data scientists, like geologists helping predict earthquakes, or chemists designing new molecules for big pharma, are scientists, and they belong to the user category. 

Implications for other IT professionals

You (engineer, business analyst) probably do already a bit of data science work, and know already some of the stuff that some data scientists do. It might be easier than you think to become a data scientist. Check out our book (listed below in "related articles"), to find out what you already know, what you need to learn, to broaden your career prospects.

Are data scientists a threat to your job/career? Again, check our book (listed below) to find out what data scientists do, if the risk for you is serious (you = the business analyst, data engineer or statistician; risk = being replaced by
a data scientist who does everything) and find out how to mitigate the risk (learn some of the data scientist skills from our book, if you perceive data scientists as competitors)

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Comment by Daren Scot Wilson on January 17, 2014 at 5:25pm

Very interesting.  Coming from physics, electronics, space science, graphics and visual arts, and wanting to move my career into Data Science, clearly data visualization is a good strength.  That was obvious before, though. What this helps me see more clearly is what other strengths based on my past experiences I could rely on for the near future. 

Although, I don't see how much of electronics would carry over.  Data Science isn't much about designing gadgets, is it?  OTOH, I keep seeing good overlap with astronomy and high energy physics - where I first learned about statistics beyond the basics.

Comment by Vincent Granville on January 17, 2014 at 3:09pm

A visual way to show how the different components interact would be by representing it in a graph (as in graph theory or graph data bases) where each node is a domain (computer science, statistics) or subdomain, and edges represent relationships (mother, daughter, sister) between the nodes, with weights indicating the strength of the association, for each edge. Multiple mothers are allowed for each node, so it's definitely more complicated than a tree structure.

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