Statistics and Probability in Data Science: Combining Statistical Methods with Machine Learning Algorithms for the Analysis of Large and Complex Data

Data science, as a highly important and rapidly growing field in today's information-driven world, plays a significant role in data analysis and utilization. This paper delves into the examination of the role of statistics and probability in data science, the analysis of large and complex data, machine learning algorithms in data science, complex data analysis, and the application of statistics and probability in business decision-making. The fundamental role of statistics and probability in data analysis and event prediction is emphasized, and these concepts are introduced as fundamental tools in this field. As this paper is a systematic review, it first elaborates on the concepts of statistics and probability in detail and examines the role of machine learning algorithms in data analysis. Furthermore, the importance of analyzing large and complex data and the applications of these concepts in data science are presented. The results of using statistics and probability in business decision-making are also provided. Mathematical examples and formulas related to statistics and probability concepts are presented, providing a framework for evaluating any research in this field. In summary, the paper underscores the significance of statistics and probability in data analysis and business decision-making. The results demonstrate that in today's information-centric world, possessing knowledge and skills in the field of statistics and probability is vital for the success of organizations and decision-makers.

Keywords:

Data Science , Statistics and Probability , Data Analysis , Machine Learning , Big Data , Business Decision-Making