Journals / Turkish Journal of Mathematics and Computer Science / 2019 / Cilt: 11 - Sayı: 1

Teaching an Introductory Programming Course to Non-Computer Science Majors Using SageMath

Pages
24–28
Publication Date
2022-11-05
DOI
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⚡ TL;DR(Too Long; Didn't Read)

We also include some course evaluation results from the first iterationof the course.

📌 3 Key Takeaways

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Objective & Hypothesis:In this paper we propose an introduction to programming course using SageMath for non-ComputerScience majors.
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Key Findings & Data:A course outline on developing and designing the course is briefly presented.
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Conclusion & Impact:We also include some course evaluation results from the first iterationof the course.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper we propose an introduction to programming course using SageMath for non-ComputerScience majors. A course outline on developing and designing the course is briefly presented. Given the largenumber of packages available in SageMath, such a course could easily be tweaked to match the need of a diversestudent population, whether it is dominated by students majoring in Mathematics, Data Science, Computer Science,Information Technology, or a mix of these. We also include some course evaluation results from the first iterationof the course.

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Cite This Article

Meze, R. A. (2022). Teaching an Introductory Programming Course to Non-Computer Science Majors Using SageMath. Turkish Journal of Mathematics and Computer Science, Cilt: 11 - Sayı: 1, 24–28. https://ajindex.com/articles/609139

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References (14)

  1. Bard, G.V., Sage for undergraduates (online version). http://www.gregorybard.com/sage.html, retrieved Dec. 2018
  2. Cass, S., The 2018 top programming languages. https://spectrum.ieee.org, 2017
  3. Davis, J., 5 top languages for machine learning, data science. https://www.informationweek.com, retrieved Dec. 2018
  4. Gaddis, T., Starting Out with Python (4th Edition), 2017
  5. Guo, P., Python is now the most popular introductory teaching language at top U.S. universities. https://cacm.acm.org, retrieved Dec. 2018
  6. Jochen, M., Introduction to computer programming course syllabus - fall 2012. http://quantum.esu.edu/ mjochen/Teaching/CPSC130/12f/, 2012
  7. Lutz, M., Learning Python, 5th Edition, O’Reilly Media, 2013
  8. Puget, J.F., The most popular language for machine learning is. https://www.ibm.com, retrieved Dec. 2018
  9. Rufinus, J. and Kortsarts, Y., Teaching an introductory programming course for non-majors using Python., In Proc ISECON, v22 (ColumbusOH), 2005
  10. Stein, W.A. et al., Sage quickstart for statistics. http://doc.sagemath.org, retrieved Dec. 2018
  11. Stein, W.A. et al., SageMath - open-source mathematical software system. http://www.sagemath.org, retrieved Dec. 2018
  12. Voskoglou, C., What is the best programming language for machine learning? https://towardsdatascience.com, retrieved Dec. 2018
  13. Zelle, J., Python Programming: An Introduction to Computer Science, 3rd Ed., Franklin, Beedle & Associates, 2016
  14. Zimmermann, P. et al., Computational mathematics with SageMath. http://www.sagemath.org, retrieved Dec. 2018.