Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Hence it creates value to the organization.
Data science is a component of analytics, it consists of statistical and operational research techniques, Machine Learning and Deep learning algorithms. Given a problem, the objective of data science component of analytics is to identify a statistical model/Machine learning model that can be used for the business problem.
Life cycle of Data science
What are the subsets of Data Science?
Most of the Artificial intelligence components are the subset of data science or it intersects the components namely Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, Big Data.
Why Data science now?
Massive Data is getting generated by businesses every second, Internet, Hardware (Storage, Processors), Cloud computing, Networks & Community, Funding & investments, Business competitiveness.
Why Organizations keen on using data science?
Data is new oil for the industries and data science is the electricity that powers the industries who wants to have competitive advantage in their business. If data is used properly then it will add real value to the organization by generating useful insights using past data which human can’t handle manually.
Example : Sales predictions, Effective stock maintenance, Leveraging Production capacity, Fraud detection in financial institutions, Customer retention.
Who invented Data science?
The term “data science” has appeared in various contexts over the past thirty years but did not become an established term until recently. In an early usage, it was used as a substitute for computer science by Peter Naur in 1960.