Data science is the process of using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision making.
Today’s data science teams are expected to answer many questions. Business demands better prediction and optimization based on real-time insights backed by tools like these.
The data science lifecycle starts with gathering data from relevant sources, cleaning it and putting it in formats that machines can understand. In the next phase, statistical methods and other algorithms are used to find patterns and trends. Then models are programmed and built to predict and forecast; finally, results are interpreted.
Advances in AI, machine learning and automation have raised the standards of data science tools for business. The result is the formation of data science teams — expert data scientists, citizen data scientists, programmers, engineers and business analysts — that extend across business units.
The opportunity here is massive. The automation of tedious data science tasks such as data preparation, and the empowerment of analysts without coding experience (00:21) to build models, keeps business agile and innovative. Automating the data science lifecycle frees expert data scientists to address the more interesting and innovative aspects of the field. Human intelligence — combined with data science technology and automation — helps a business extract greater value from data.