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A Perspective on “Working with Data” Curriculum Development

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Data Management and Analysis

Part of the book series: Studies in Big Data ((SBD,volume 65))

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Abstract

In this paper, I am reporting a perspective on how to adapt current traditional courses in undergraduate engineering curricula to develop a curriculum for Data Science Specialization for Engineers at the undergraduate level. For engineers, to be able to handle data science related problems/projects at the undergrad level, their education needs to expose them more toward project-based learning scheme that covers all aspects of the data analytics lifecycle. However, given the robust and well-developed undergraduate engineering curricula and the limited resources, it would be beneficial to modify some of the courses offered at the undergraduate level to address the different aspects of data analytics lifecycle. I conclude this paper with a list of suggested/modified courses, their descriptions and objectives, tools and development platforms, challenges, project ideas, and teaching methodology. The list represents a seed for a curriculum proposal and a pilot project is needed to measure the effectiveness of the proposed curriculum.

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References

  1. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2013). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 21, 20–31.

    Google Scholar 

  2. Stanton, J. M., et al. (2011). Education for eScience professionals: Job analysis, curriculum guidance, and program considerations. Journal of Education for Library and Information Science, 52(2), 79–94.

    Google Scholar 

  3. Macías, J., et al. Enhancing project-based learning in software engineering lab teaching through an E-portfolio approach. IEEE Transactions on Education, 55(4), 502–507.

    Article  Google Scholar 

  4. Hadim, H. A., & Esche, S. K. (2002). Enhancing the engineering curriculum through project-based learning. In Frontiers in education, 2002. FIE 2002. 32nd Annual (Vol. 2, p. F3F). IEEE.

    Google Scholar 

  5. García, S., Luengo, J., & Herrera, F. (2016). Data preprocessing in data mining. New York: Springer.

    Google Scholar 

  6. Abdulrahman, S. M., Brazdil, P., van Rijn, J. N., & Vanschoren, J. (2018). Speeding up algorithm selection using average ranking and active testing by introducing runtime. Machine Learning, 107(1), 79–108.

    Article  MathSciNet  Google Scholar 

  7. White, T. (2012). Hadoop: The definitive guide. Sebastopol, CA: O'Reilly Media.

    Google Scholar 

  8. Shanahan, J. G., & Dai, L. (2015). Large scale distributed data science using apache spark. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 2323–2324). New York: ACM.

    Chapter  Google Scholar 

  9. H2O.ai. Retrieved from https://www.h2o.ai/

  10. Welcome to Python.org. Retrieved from https://www.python.org/

  11. R: The R Project for Statistical Computing. Retrieved from https://www.r-project.org/

  12. Dataiku | Collaborative Data Science Platform. Retrieved from https://www.dataiku.com/

  13. Qlik: Data Analytics for Modern Business Intelligence. Retrieved from https://www.qlik.com/us/

  14. @tableau. (2018). Tableau Reader.

    Google Scholar 

  15. Microsoft Azure Cloud Computing Platform & Services. Retrieved from https://azure.microsoft.com/en-ca/

  16. IBM Cloud. Retrieved from https://www.ibm.com/cloud

  17. @dominodatalab. What is Domino? Domino Data Lab. Retrieved from https://www.dominodatalab.com/

  18. Canonical. (2018). Juju | Cloud | Ubuntu.

    Google Scholar 

  19. Raspberry PI board. Retrieved from https://www.raspberrypi.org/

  20. Data Scientist | Job Post | LinkedIn. Retrieved from https://www.linkedin.com/jobs/view/592290132/

  21. @redblobgames. (2018). Introduction to A∗. Retrieved from https://www.redblobgames.com/pathfinding/a-star/introduction.html

  22. Kamber, M., Han, J., & Pei, J. (2012). Data mining: Concepts and techniques. Amsterdam: Elsevier.

    MATH  Google Scholar 

  23. Competitions | Kaggle. Retrieved from https://www.kaggle.com/competitions

  24. UCI Machine Learning Repository. Retrieved from https://archive.ics.uci.edu/ml/index.php

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Correspondence to Emad A. Mohammed .

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Mohammed, E.A. (2020). A Perspective on “Working with Data” Curriculum Development. In: Alhajj, R., Moshirpour, M., Far, B. (eds) Data Management and Analysis. Studies in Big Data, vol 65. Springer, Cham. https://doi.org/10.1007/978-3-030-32587-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-32587-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32586-2

  • Online ISBN: 978-3-030-32587-9

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