In computer science and computer programming, a data type or simply type is a classification of data which tells the compiler or interpreter how the programmer intends to use the data. Most programming languages support various types of data, for example: real, integer or Boolean. A data type provides a set of values from which an expression
(i.e. variable, function...) may take its values. The type defines the
operations that can be done on the data, the meaning of the data, and
the way values of that type can be stored.a type of value from which an
expression may take its valueCategories of data scientists
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.
PLEASE SHARE IT!!
- 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.
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.
PLEASE SHARE IT!!
No comments:
Post a Comment