Big Data and Realistic Mathematics are two relatively independent concepts that differ in the areas and methods of application.

Big data is a collection of data that is very large in volume, complex in type, and difficult to process, analyze, and manage in traditional ways. With the continuous development of modern computer technology, the application fields of big data are constantly expanding, including artificial intelligence, machine learning, cloud computing, the Internet of Things and many other fields. In terms of data processing and analysis, big data uses a variety of modern technical means, such as Hadoop, Spark, NoSQL and so on.

Realistic mathematics, on the other hand, is a branch of applied mathematics aimed at solving practical problems and quantitatively analyzing real data. What makes Realistic Mathematics different from other branches of mathematics is that its purpose is to accurately describe, represent, and analyze real-world problems and to provide mathematical models and algorithms for problem solving. The main methods of Realistic Mathematics include developing appropriate mathematical models, performing model analysis and validation, solving mathematical problems, and implementing computational methods related to data.

Realistic mathematics is instructive for data analysis, processing and modeling, and can help analysts understand and describe data more accurately and design more scientific and efficient analysis methods. At the same time, big data and realistic mathematics have a synergistic relationship due to the continuous development of modern data analysis and computational technologies, which continue to drive innovation and development in the fields of data science and mathematics.

The connection between the two

Big Data and Realistic Mathematics have a certain connection in data processing and analysis. Realistic mathematics can provide effective mathematical models and algorithms for big data analytics, and big data analytics can use methods from realistic mathematics to process data. Realistic mathematics can help data analysts better understand data and design more scientific and efficient data analysis methods.

The huge scale and complexity of big data also make it necessary to incorporate methods of realistic mathematics in data processing and analysis. For example, in the field of data mining, large-scale data sets require the use of methods of real mathematics, such as multivariate statistical analysis, principal component analysis and so on. In addition, branches of real-world mathematics, such as optimization and optimal control, can also be used in big data analysis to improve the accuracy and efficiency of analysis.