The phrase "analysis of complex data" is correct and usable in written English.
It can be used in contexts where you are discussing the examination or interpretation of intricate datasets, often in fields like research, data science, or statistics.
Example: "The analysis of complex data revealed significant trends that were previously overlooked."
Alternatives: "examination of intricate data" or "evaluation of complicated data".
Atlas.ti is a qualitative analysis software for the analysis of complex data.
This suggests that significant variation in analysis of complex data may be difficult to avoid, even by experts with honest intentions.
Analysis is quantitative and includes novel machine learning technology required for simultaneous analysis of complex data sets and aimed at identifying biomedical phenotypes that improve the mechanistic understanding, diagnosis, and treatment of neuropsychiatric disorders.
This paper outlines opportunities for analysis of complex data sets using examples of multiplexed bead-based assays, drug screens and cell cycle analysis – any of which could become integrated into the clinical environment.
Examples of such methods include in vivo optical observation of brain activity using multiphoton microscopy; detailed quantitative analysis of animal behavior; computational analysis of complex data; viral-assisted gene delivery to manipulate, monitor, and trace neural circuits; the use and refinement of genetically encoded activity sensors; and transgenic organisms.
We rely heavily on the expertise of the Harer group (Dept. of Mathematics, Duke University) for the analysis of complex data, and their understanding of dynamical systems.
However, achieving this vision requires the management, analysis, and delivery of complex data, information, and knowledge.
Thus, the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets was illustrated in this study for groundwater quality assessment.
This study reveals the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and also for identification of pollution sources and better understanding of seasonal variations in water quality for effective coastal water quality management.
This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective surfacewater quality management.
As in any data analysis problem, visual inspection of complex data before statistical analysis is vital.
