Research on the Teaching Reform of “Big Data Analysis and Visualization” Course for College Students

Abstract

Under the background of the big data era, the education of big data majors is undergoing a profound teaching reform and innovation. With the increasing role of big data technology in analysis and decision-making, updating and expanding the teaching content of big data majors has become particularly important. In the era of big data, modern enterprises have put forward new and higher demands for big data talents, which not only include traditional data analysis skills but also knowledge of data visualization and information technology. To address these challenges, big data education needs to reform and innovate in the development and utilization of teaching content, methods, and resources. This paper proposes teaching models and reform methods for big data majors and analyzes corresponding teaching reforms and innovations to meet the requirements of the new development of big data majors. The traditional classroom teaching method is no longer sufficient to meet the learning needs of students, and more dynamic and interactive teaching methods, such as case studies, flipped classrooms, and project-based learning, are becoming increasingly essential. These innovative teaching methods can more effectively cultivate students’ practical operation skills and independent thinking while allowing them to better learn advanced knowledge in a real big-data environment. In addition, the paper also discusses the construction of big data processing and analysis platforms, as well as innovative teaching management and evaluation systems to improve teaching quality.