Data Science is the practice of:
- Asking questions (formulating hypothesis), answers to which solve known problems or unearth unknown solutions that in turn drive business value,
- Defining the data needed or working with an existing data set and employing tools (computer science based) to collect, store and explore such data generally in huge volume & variety (often more than 1 TB and 1000s of dimensions),
- Identifying the type of analysis to be done to get to the answers and performing such analysis by implementing various algorithms/tools (statistics based), often in a distributed and parallel architecture,
- Communicating the insights gathered from the analysis in the form of simple stories/visualizations/dashboards (the Data Product) that a non-data scientist can understand and build conversation out of it. (It should be kept in mind that a product can also be an piece of code that is internal to a company and is used by various departments. The presentation, maintenance, scalability, etc of the code are then the product features, which is often not practiced in many organizations)
- Building a higher level abstraction that does steps 2-3-4 in an autonomous way, analyzing & taking actions on new data as they are fed to the system.
Hope this helps!!
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