What is Data Science?
What is analytics? What is a data scientist?
“We have heaps of data – now what to do?”
(How can we find hidden value from our data?)
Value of your Business
This feature of data science is all about revealing findings from data. Diving in at a rough level to mine and understand complex behaviours, trends, and inferences. It’s about surfacing hidden insight that can help allow companies to make smarter business decisions. For example:
How do data scientists mine the insight of data? It starts with data investigation. When given a difficult issue, data scientists become detectives. They explore leads and try to understand pattern or features within the data. This requires a big amount of analytical methods.
Then as needed, data scientists may apply some technique like quantitative technique in order to get a level deeper – some examples are inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The aim is to scientifically bit together a forensic view of what the data is really saying.
This data-driven insight is core to providing strategic guidance. In this logic data scientists act as consultants, guiding business owners or decision makers on how to act on findings.
We can say data product is a technical talent that: (1) utilizes data as input, and (2) processes that data to return algorithmically-generated results. The classic example of a data product is a recommendation engine, which ingests user data, and makes custom-made recommendations based on that data. Let us see some examples of data products:
This is different from the “data insights” section mentioned above; where the result to that is to perhaps provide advice to an executive to make a smarter decision. Alternatively, a data product is technical functionality that encapsulates an algorithm, and is designed to integrate directly into core applications. Individual examples of applications that have as a feature data product behind the scenes: Amazon’s homepage, Gmail’s inbox, and autonomous driving software.
Data scientists play a core role in developing data product. Data scientists are building out algorithms, as well as testing, refinement, and technical use into production systems. In this logic, data scientists serve as technical developers, building assets that can be leveraged at wide scale.