Data Science in itself is a very diverse field, and hence many types exist in it. Basing on this and the tools utilized the scientists themselves are classified into various types.
- The quantitative data scientists who rely on theories and exploration. They firmly believe that established theories are the best way to analyze data and all of practical implementation relies upon it.
- Operational data scientists come in the next category. Here you have a set of gentlemen who
rely on facts and figures. They wish to classify all data as numbers, and thus carry out further
research. For example a coach would rely on various parameters like pass time, running speed,
dribbling count per minute etc. to analyse the players in his basketball team. After collecting
this raw data he can use tools to sort and order, so special attention can be given to those who
under perform. This is more or less what an operational data scientist does at work, with respect
to various business principles and strategies basing on the stats.
- Product management data scientists, this team composes of people who try to enhance
the product. In short they are trying to tinker the existing modules in order to provide better
interfaces to the user. The example of improving the app store on an android or iOS platform,
just to make the users feel at home is a good example in this type of data science. Thorough
understanding of what the product is set to serve, is key to conquering the market for these
- Marketing data scientists take up the onus of understanding the market well on their
shoulders. It is essentially one of the trickier jobs out there, because the market is always
dynamic and relies on a variety of factors. For example you are analysing the telephone calls of
a particular location over a time period of 1 week, in order to successfully determine what plan
would benefit both user and end service provider. You would run into a situation in which data
would be haphazard, and making order out of chaos would become a daunting task. Hence these
scientists have to work with dedication and adroit skill.
- Research data scientists need to think out of the box and survive on innovation and inspection.
Inspecting how a product is regularly functioning and upgrading it is best possible by the work
of this type of data scientist.
All in all it is the coming together of various forms of data science that helps in gaining overall success
for various businesses.