The science of studying data, with a focus on extracting meaningful insights for businesses, is what we call data science. It is multidisciplinary, as it combines the principles and practices from the fields of mathematics, statistics, and computer engineering to analyze data to produce data insights – actionable, data-informed conclusions or predictions that you can use to understand and improve your business, investments, your health, and even your lifestyle.
The process of solving a data science problem can be summarized into:
- Understanding the problem statement
- Getting the right data (Data collection)
- Understanding the data (Data processing)
- Modelling
- Communicating the results
The first step is always to frame the problem: you have to understand the business use case and craft a well-defined analytics problem (or problems) out of it. This is followed by acquiring the right data for the problem, and then grappling with the data and the real-world things it describes, so you can extract meaningful features. Finally, these features are plugged into analytical tools that achieve desired results.
Data science has found its application in almost every industry, ranging from Healthcare, Agriculture, Banking and Finance, etc.
