Data Analytics and the Future Impact of Data Analytics

Abstract As data from disparate sources continues to grow exponentially, data analytics are becoming increasingly important in research. This paper looks at the evolution of data analytics. The first part of the paper will detail the difficulties of large data and the significance of data engineering. Following that, we discussed the processes of data conversion, cleansing and modeling. We present a number of data analysis methods including machine learning, predictive modeling, and exploratory data analysis. In conjunction with this, we discussed several programming languages and data analytic tools. We concluded with a discussion on a few of the more recent developments in the area of data analytics for research publications. Some of the developments included big data, cloud computing, and data privacy. The paper aimed to provide a comprehensive perspective on data analytics for research publications. Researchers may find this useful when using data analytics to gain insights from their data.