Application of Correlation Analysis and Cluster Analysis in Teaching Reform for Big Data Course
- DOI
- 10.2991/978-94-6463-034-3_17How to use a DOI?
- Keywords
- Correlation analysis; cluster analysis; application of IBM SPSS; precision teaching; teaching reform of big data course
- Abstract
According to the needs of big data course teaching reform, Using IBM SPSS as the tool, BNUZ has carried out the correlation analysis and the cluster analysis on the data samples of the big data courses in recent three years. The results of qualitative and quantitative analysis are conducive to correction and implementation of specific teaching contents. Cluster analysis of data samples is conducive to improvement and planning of the overall teaching reform scheme. This paper emphasizes that applying advanced mathematical statistical analysis methods to teaching reform is a scientific process that must implement in teaching research, which is quite necessary. This paper also explains these algorithms used and application, such as the correlation analysis, the cluster analysis, K-means, k-medoids and so on, conducive to other disciplines’ teaching research and convenient to learn from this example.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jianhua Zhang PY - 2022 DA - 2022/12/23 TI - Application of Correlation Analysis and Cluster Analysis in Teaching Reform for Big Data Course BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 155 EP - 164 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_17 DO - 10.2991/978-94-6463-034-3_17 ID - Zhang2022 ER -