Data analytics is a complex process of extracting useful information from raw data in order to use that information to make better decisions in planning and optimising operations in various fields. Effort on data analysis over the recent decades has seen many techniques of data analytics become part of many automated systems, particularly in the financial world, social networks, and tourism, retail, telecommunication, and hospitality industries. Data analytics on customer data can provide timely information on consumer behaviours, emerging needs, and dissatisfaction of service that are critical for business success, due to the short-term periodicity of business operations and a relatively high portion of uncertainty of customer behaviours in the service industries.

In science and engineering, however, the periodicity of planning for new projects or major upgrading of existing projects is relatively longer and the routine operations are highly stable in normal circumstances due to a high level of certainty as designed and tested originally. Hence, data analytics on the raw data collected from some of these disciplines seems less urgent as such studies do not generate immediate benefits to the existing systems. Given the mega-scale of some industrial operations, such as better scheduling for heavy haul railway networks, an adoption of a small adjustment in an existing system guided by the conclusion from data analytics may save operation costs or increase revenue in a scale of tens to hundreds of millions of dollars annually.

The aim of this Special Issue is to reflect on the latest development in incorporating mathematical and intelligent techniques for data analytics in science and engineering. In normal circumstances, mathematical and statistical techniques are the first choice in analysing and/or modelling scientific and engineering datasets. For the abnormal periods occurred unexpectedly during operations with a high level of uncertainty, intelligent techniques may offer more help in extracting useful information from such datasets to help in diagnosing the cause of and/or resolving the problem encountered. Therefore, it is a logical approach to incorporate mathematical and intelligent techniques for data analytics in science and engineering.

Potential topics include but are not limited to the following:

• New algorithms and analysis tools
• New system design, implementation, and evaluation
• New strategies for optimising the performance of existing systems
• New applications in all areas of science, engineering, and technology
• Comparative studies on selected popular incorporative systems
• Structural review of current incorporative systems and applications