Data science "uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data" (Wikipedia). It "employs mathematics, statistics, and computer science disciplines, and incorporates techniques like machine learning, cluster analysis, data mining, and visualization" (TechTarget). "Data science, or data-driven science, combines different fields of work in statistics and computation in order to interpret data for the purpose of decision-making" (Investopedia).
We can also define data science as that which a data scientist does:
"Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization" — UC Berkeley School of Information.
Furthermore, data scientists
are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision-makers shift from ad hoc analysis to an ongoing conversation with data. — Harvard Business Review
Data scientists often work in tandem with data engineers.
A definitive guide to data definitions and trends, from the team at Stitch.