iterative.

linear.

it communicates the outputs from it in such a way that colleagues with a nontechnical background can understand them and have confidence in them.

it justifies the extensive investment required for an ML project.

it helps to hide the uncertainties surrounding analytic results, if the data is not reliable.

must be removed from the data set(s) before any analytics operation.

is not of interest to analytics because it is not the captured data that holds the core meaning.

can be used to reveal hidden patterns and insight into situations.

we want to automate behavior patterns or operative processes and we do not yet know how to do that manually.

we face a problem that humans cannot solve, but we have access to data sources that might be of help.

there is the need to safe costs related to the use of information systems.

we have a large number of data examples and when the patterns are too complex for humans to discover and extract manually.

a real-world, domain-specific problem and the need to design a data-driven solution to this problem.

a hard calculatory challenge that we cannot solve using statistical means but for which sufficient data is available.

a new programming technique that we can test on interesting practice problems.

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.