Analytical concepts of causal inference and intensification of data prioritizing meta-heuristic approach

In general data science is the prioritization on large size data provisioning multidisciplinary approach towards extraction of meaningful insights in general as well as business applications. In this context, general purpose programming languages have already been thought of in which the basic importance has been provisioned towards code readability with transparency. As the direct involvement may be on multiple programming paradigms, the interpreters associated with the conceptualized programming environment can be provisioned with many operating systems. Somehow due to structured programming approach, the linkages can be associated towards functional programming as well as aspect oriented and logic programming. In many situations, it has been observed that the dynamic implementation can be initiated provisioning specified sequenced references in memory management. Accordingly, the programming languages associated with the features of binding methods can be properly sequenced during program execution. As per maximum extensibilities of some specific programming languages, the programmable interfaces towards existing applications can enhance the feasibilities with the existing applications. In practical situation, analysis of the huge amount of heterogeneous data implementing learning principles is possible to focus on meaningful intelligent applications to maximize the outcomes from each and every stage of application. As it is understood that modeling data with predictive analysis is quite feasible using intelligent agents as well as meta-heuristic approaches, accordingly in this work, the meta-heuristic technique has been prioritized to focus on causal inference with intensification data.