Statistics, Machine Learning, and Data Science: A Historical Review and a Look to the Future

We are witnessing the beginning of a data driven era with the explosion of data, impact of data on our everyday lives, and advances of data processing methodology and technology. At this juncture, data science has emerged as an interdisciplinary field to deal with data for which statistics and machine learning are two key enablers. Originally, statistics and machine learning appear to have been developed in the different contexts of mathematics and computer science, respectively. Data science has brought them together in which statistics focuses on mathematical foundations and methods, while machine learning is more on algorithms and automated data processing. First, this talk looks back on a timeline of the emergence of statistics, machine learning, data science, and related fields. Second, it reviews a chronology of the invention and context of some important statistical and machine learning methods. Third, it discusses relationships between statistics, machine learning, and data science. Finally, it addresses some challenges and overcoming ways of learning from big data.