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Customisable Data Science Educational Environment: From Competences Management and Curriculum Design to Virtual Labs On-Demand

机译:可定制的数据科学教育环境:从能力管理和课程设计到虚拟实验室按需

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Data Science is an emerging field of science, which requires a multi-disciplinary approach and is based on the Big Data and data intensive technologies that both provide a basis for effective use of the data driven research and economy models. Modern data driven research and industry require new types of specialists that are capable to support all stages of the data lifecycle from data production and input to data processing and actionable results delivery, visualisation and reporting, which can be jointly defined as the Data Science professions family. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects all multi-disciplinary knowledge and competences that are required from the Data Science practitioners in modern, data driven research and the digital economy. The educational model and approach should also solve different aspects of the future professionals that includes both theoretical knowledge and practical skills that must be supported by corresponding education infrastructure and educational labs environment. In modern conditions with the fast technology change and strong skills demand, the Data Science education and training should be customizable and delivered in multiple form, also providing sufficient data labs facilities for practical training. This paper discussed both aspects: building customizable Data Science curriculum for different types of learners and proposing a hybrid model for virtual labs that can combine local university facility and use cloud based Big Data and Data analytics facilities and services on demand. The proposed approach is based on using the EDISON Data Science Framework (EDSF) developed in the EU funded Project EDISON and CYCLONE cloud automation systems being developed in another EU funded project CYCLONE.
机译:数据科学是一种新兴科学领域,需要一种多学科方法,并且基于大数据和数据密集型技术,两者都为有效利用数据驱动研究和经济模型提供了基础。现代数据驱动的研究和行业需要新型专家,该专家能够支持数据生命周期的所有阶段,从数据生产和输入到数据处理和可操作的结果传递,可视化和报告,可以共同定义为数据科学专业家庭。数据科学家的教育和培训目前缺乏一个常见的统一教学模式,反映了现代,数据驱动研究和数字经济中的数据科学从业者所需的所有多学科知识和能力。教育模式和方法还应解决未来专业人士的不同方面,包括相应的教育基础设施和教育实验室环境必须支持的理论知识和实践技能。在现代技术变化快速变化和技能需求强劲,数据科学教育和培训应定制并以多种形式提供,还提供足够的数据实验室设施进行实际培训。本文讨论了各个方面:为不同类型的学习者构建可定制的数据科学课程,并为虚拟实验室提出一个可以结合当地大学设施的混合模型,并根据需求使用云的大数据和数据分析设施和服务。该方法是基于在欧盟资助的项目爱迪生和Cyclone云自动化系统中开发的Edison数据科学框架(EDSF)基于在另一个欧盟资助的项目Cyclone中开发的。

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