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Journal of Data and Information Science  2017, Vol. 2 Issue (3): 1-18    DOI: 10.1515/jdis-2017-0011
Perspective     
Big Data and Data Science: Opportunities and Challenges of iSchools
Il-Yeol Song (),Yongjun Zhu
College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USA
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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.



Key wordsBig data      Data science      Information computing      The fourth Industrial Revolution      iSchool      Computational thinking      Data-driven paradigm      Data science lifecycle     
Published: 07 November 2009
Corresponding Authors: Song Il-Yeol     E-mail: song@drexel.edu
Cite this article:

Il-Yeol Song , Yongjun Zhu. Big Data and Data Science: Opportunities and Challenges of iSchools. Journal of Data and Information Science, 2017, 2(3): 1-18.

URL:

http://manu47.magtech.com.cn/Jwk3_jdis/10.1515/jdis-2017-0011     OR     http://manu47.magtech.com.cn/Jwk3_jdis/Y2017/V2/I3/1

Figure 1. iSchools educate data scientists to prepare for the fourth Industrial Revolution.
Figure 2. Three foci of information computing for iSchools.
Figure 3. The proposed Data Science Education Framework (DSEF) for iSchools.
Song & Zhu, 2016).">
Figure 4. A data science lifecycle proposed in our previous study (Song & Zhu, 2016).
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.
Table 1 Eight steps of a data science lifecycle.
[1]   Baweja B., Donovan P., Haefele M., Siddiqi L., & Smiles S. (2016). Extreme automation and connectivity: The global, regional, and investment implications of the Fourth Industrial Revolution. UBS White Paper for the World Economic Forum Annual Meeting 2016. Retrieved on October, 1, 2016, from
[2]   Bundy,A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 67-69.
[3]   Davenport,T.H., & Patil,D.J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70-76.
doi: 10.1007/s11213-012-9233-0 pmid: 23074866
[4]   Dhar,V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.
doi: 10.1145/2500499
[5]   Gartner,Inc. (2016). Organizing for big data through better process and governance. Retrieved on September 15, 2016, from
[6]   Kagermann H., Helbig J., Hellinger A., & Wahlster W. (2013. Recommendations for implementing the strategic initiative Industrie 4.0: Securing the future of the German manufacturing industry; final report of the Industrie 4.0 Working Group.Retrieved on September 15, 2016, from .
[7]   Lasi H., Fettke P., Kemper H., Feld T., & Hoffmann M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242.
[8]   Mayer-Schonberger,V., & Cukier,K.(2013). Big data: A revolution that will transform how we live, work, and think. Boston, MA:Houghton Mifflin Harcourt.
doi: 10.1080/1369118X.2014.923482 pmid: 24714727
[9]   Provost,F., & Fawcett,T.(2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.
doi: 10.1089/big.2013.1508 pmid: 27447038
[10]   Schwab,K. (2016). The fourth industrial revolution: What it means, how to respond. World Economic Forum. Retrieved on October 20, 2016, from
[11]   Stanton,J. (2012. An introduction to data science .Retrieved on September 15, 2016, from.
[12]   Song,I.-Y., & Zhu,Y. (2016). Big data and data science: What should we teach? Expert Systems, 33(4), 364-373.
doi: 10.1111/exsy.12130
[13]   Storey,V., & Song,I.-Y.(2017). Big data technologies and management: What conceptual modeling can do? Data & Knowledge Engineering, 108, 50-67.
doi: 10.1016/j.datak.2017.01.001
[14]   Wing,J.M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
[15]   Wing,J.M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical,Physical and Engineering Sciences, 366(1881), 3717-3725.
doi: 10.1098/rsta.2008.0118 pmid: 18672462
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