<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.</p></abstract>

Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

Big Data and Data Science: Opportunities and Challenges of iSchools

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Big Data and Data Science: Opportunities and Challenges of iSchools"}

Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening...

Step	Sub-steps
1. Business understanding	What is the question to solve and what metrics are to be evaluated?
	Generate hypothesis; Assess resources (people, data, and tools).
2. Data understanding	Identify data resources, data reuse and integration plan, datatification, and decision on tools.
3. Data preparation	Acquire data; Perform data profiling, cleanse, and transform; Explore data and verify quality.
4. Model planning	Determine the methods, techniques, and workflow;
	Select key variables and determine correlation between them.
5. Model building	Build models; Perform analysis and iterate.
6. Evaluation	Perform evaluation against metrics; Communicate results and recommendations.
7. Deployment	Integrate analytics procedures into management dashboards and operational systems.
8. Review and monitoring	Monitor performance; Identify parts that need to be improved.

Big Data and Data Science: Opportunities and Challenges of iSchools

Article Category: Perspective

Publié en ligne: 22 août 2017

Pages: 1 - 18

Reçu: 25 mars 2017

Accepté: 18 avr. 2017

DOI: https://doi.org/10.1515/jdis-2017-0011

Mots clés
Big data, Data science, Information computing, The fourth Industrial Revolution, iSchool, Computational thinking, Data-driven paradigm, Data science lifecycle

© 2017 Il-Yeol Song & Yongjun Zhu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Eight steps of a data science lifecycle.

Big Data and Data Science: Opportunities and Challenges of iSchools

Article Category: Perspective

Publié en ligne: 22 août 2017

Pages: 1 - 18

Reçu: 25 mars 2017

Accepté: 18 avr. 2017

DOI: https://doi.org/10.1515/jdis-2017-0011

Mots clésBig data, Data science, Information computing, The fourth Industrial Revolution, iSchool, Computational thinking, Data-driven paradigm, Data science lifecycle

© 2017 Il-Yeol Song & Yongjun Zhu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Eight steps of a data science lifecycle.

Mots clés
Big data, Data science, Information computing, The fourth Industrial Revolution, iSchool, Computational thinking, Data-driven paradigm, Data science lifecycle