The study of Coursera’s data science specialization

L. Panchenko
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Abstract

Objective: To identify the characteristics of the specialization form of the massive open online courses. Research object: a learning process of the massive open online courses. Research subject: Data science specialization of Coursera. Research objectives: to participate as a student in the several online courses in “data science specialization”, to find the structure of this specialization, to determine its characteristics. Methods: participant observation, content analysis. Results: Data science specialization of Cousera project is a series of 9 courses covering concepts and tools of data analysis, from the research questions formulation to results publication. The implementation of a special Capstone Project has completed this sequence of courses. Сourses are repeated once a month during a year. The courses in the specialization are related with a hard and a soft dependences. Course structure consists of syllabus, short video lectures, tests, peer assessment, course projects, forum. The software R, RStudio, Git, GitHub are used for programming assignment. Conclusions and recommendations: there are next ways to aggregate this form in the traditional educational process of Ukrainian universities: developing training and methodological support of disciplines, the students work organization with course materials, including the topics in qualification works, using new data analysis tools and techniques in the post graduate and post doctoral studies.
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对Coursera数据科学专业的研究
目的:探讨大规模网络公开课程专业化形式的特点。研究对象:大规模网络开放课程的学习过程。研究课题:Coursera数据科学专业。研究目标:以学生的身份参与“数据科学专业”的几门在线课程,发现该专业的结构,确定其特点。方法:参与观察、内容分析。结果:Cousera项目的数据科学专业是一系列9门课程,涵盖了数据分析的概念和工具,从研究问题的制定到结果的发表。一个特殊的顶点项目的实施完成了这一系列的课程。Сourses在一年中每个月重复一次。本专业的课程有软硬关系。课程结构包括教学大纲、短视频讲座、测试、同侪评估、课程专题、论坛。软件R, RStudio, Git, GitHub用于编程作业。结论和建议:在乌克兰大学的传统教育过程中,有以下几种方式可以将这种形式整合起来:发展学科培训和方法支持,学生工作组织课程材料,包括资格工作中的主题,在研究生和博士后研究中使用新的数据分析工具和技术。
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