is just another term for Data Science.

generally deals with the analysis of structured data and often implies an emphasis on commercial applications.

is the process to identify, analyze and use patterns in unstructured, qualitative data.

the improvement of machines in order to achieve cost reductions in operations.

recognizing correlations in customer data for improvement of product and service development and customer retention.

the design and evaluation of algorithms for extracting patterns from data.

Data science is the computation of complex solutions from differential equations.

Data Science is the application of the most recent Information and Computing Technology for business problems.

Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious and useful patterns from large data sets.

achieving customer transparency in order to innovate in products and services.

achieving competitive advantage through high-tech applications in business.

improving decision-making through the analysis of data.

in case sensitive data would be shared.

in case the data transfer would take place over the Internet.

because they might conduct erroneous computations.

the accuracy of calculations achieved by the analytic algorithm.

the readability of analytic results.

the accuracy and trustworthiness of the

generated data.

the economic benefit achievable when selling the data to third parties for further analysis.

the data's capacity to provide valuable insights from analysis.

the economic benefit derived from the implementation of the analytics project.

Statistics are analytic results that leave no space for interpretation.

Statistics are artificially visualized data.

Statistics

are the result of data analysis and interpretation.

Statistics are data in context.