摘要
近年来,越来越多的高校开始开设数据科学与大数据技术专业,作为一个多学科交叉的新兴热门宽口径专业,其课程体系仍在进一步完善中。文中运用复杂网络方法对从互联网上收集到的106所高校的课程数据进行了分析和可视化,分别构建了课程共现网络和开设院校关系网络。对于耦合度较高的课程共现网络,提出了一种基于边权的壳层分解算法,对课程重要性进行逐层分析,并将所得结果与词频统计和由Apriori算法获取的频繁项集结果进行了对比分析。考虑到该专业可授予理学或工学学位,又将数据集划分为理学和工学两部分进行了分析和可视化。本研究的开展能够给即将开设或者已经开设数据科学与大数据技术专业的院校提供一定的参考,同时也为高耦合网络的分析提供一种有效的算法。
In recent years,more and more universities have begun to offer majors in data science and big data technology.As an emerging and popular multi-disciplinary major with wide caliber,its curriculum system is still being furthered improved.In this paper,we use complex network methods to analyze and visualize the course data set of 106 universities collected from the Internet.The course co-occurrence network and college relationship network are constructed respectively.For the highly coupled course co-occurrence network,a shell decomposition algorithm based on edge weights is proposed.The results are compared with the word frequency statistics and the frequent items obtained by the Apriori algorithm.Considering that this speciality can award a degree in science or engineering,the data set is divided into two sections science and engineering to analyze and visualize.This research can provide a certain reference to universities which are establishing or have established data science and big data technology speciality,and also provide an effective algorithm for the analysis of highly coupled networks.
作者
杨波
李远彪
YANG Bo;LI Yuan-biao(Data Science Research Center,Kunming University of Science and Technology,Kunming 650500,China;Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S01期680-685,807,共7页
Computer Science
基金
国家自然科学基金(11947041)。
关键词
复杂网络
课程共现网络
壳层分解算法
频繁项集
Complex network
Course co-occurrence network
Shell decomposition algorithm
Frequent items
