The following information has been put together after reading a couple of web pages related to this matter:
Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems.
1.Data science – discovery of data insight
This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviours, trends, and inferences.
When given a challenging question, data scientists become detectives. They investigate leads and try to understand pattern or characteristics within the data. This requires a big dose of analytical creativity.
Then as needed, data scientists may apply quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.
2.Data science – development of data product
A "data product" is a technical asset 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 personalized recommendations based on that data.
This is different from the "data insights" section above, where the outcome to that is to perhaps provide advice to an executive to make a smarter business decision. In contrast, a data product is technical functionality that encapsulates an algorithm, and is designed to integrate directly into core applications.
Key Capabilities of a data scientist
- You have to understand that data has meaning
- You have to understand the problem that you need to solve, and how the data relates to that
- The third capability is about understanding and delivering the infrastructure required to perform any analysis.
- Mathematics Expertise
At the heart of mining data insight and building data product is the ability to view the data through a quantitative lens.
- Technology and Hacking
Referring to the tech programmer subculture meaning of hacking – i.e., creativity and ingenuity in using technical skills to build things and find clever solutions to problems.
- Strong Business Acumen
It is important for a data scientist to be a tactical business consultant. Working so closely with data, data scientists are positioned to learn from data in ways no one else can.