• Sidita Duli University “Luigj Gurakuqi”, Shkoder
Keywords: Data Science, Natural Sciences, Python programming


The study of Natural Sciences often leads to Data Science, a trending topic among skilled professionals and organizations. Data Science is the newest field that promises to revolutionize industries, businesses, government, health care, and academia. A data scientist should own mathematical expertise, strong business and technology skills.
With the development of higher education, teaching methods, courses content, the technology used in the learning process, and the curriculum system should reform to adapt to the needs of society. Some data scientists have studied Computer Science. Meanwhile, many others come from Statistics, Mathematics, Physics, or Biology backgrounds and don’t have programming skills. This article shows the role of programming in Natural Sciences study programs in Universities and suggests the programming course content.
Many data scientists and software developers use Python to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights. Python provides many functions to Data visualize and data analysis. Python syntax is easy to understand and write, which makes it very popular.
With the advantages of Python, the Natural Sciences students create the main skills in data science, mathematical computations libraries, and data analysis.
The methodology used in this research is the SciPy, the main Python library that includes linear algebra, integration, optimization, and statistics modules. Another library is NumPy. It is a perfect tool for scientific computing and performing basic and advanced array operations. Matplotlib is a standard data science library that generates data visualizations, such as two-dimensional diagrams and graphs (histograms, scatterplots, non-Cartesian coordinates graphs).
This article aims to emphasize the role of the Python programming language in all the study programs in Natural Sciences. It discusses the course content of Python Programming, the main ideas to cover to create the basic knowledge of Data Science. It suggests that Natural Sciences students should master these three libraries: NumPy, SciPy, and matplotlib. It describes each of these libraries with main functionalities.


Arif, T.M. (2020). Introduction to Deep Learning for Engineers: Using Python and Google Cloud Platform.

Liu, X., & Xu, H. (2019). School-Enterprise Cooperation on Python Data Analysis Teaching. IEEE Xplore.

Lee, Y., & Cho, J. (2017). The Influence of Python Programming Education for Raising Computational Thinking. International Journal of U- and E-Service, Science and Technology, 10(8), 59–72.

Fagan, B. J., & Payne, B. (2017). Learning to Program in Python – by Teaching It! Proceedings of the Interdisciplinary STEM Teaching and Learning Conference, 1(1).

Sotomayor-Beltran, C., Segura, G. W. Z., & Roman-Gonzalez, A. (2018). Why should Python be a compulsory introductory programming course in Lima (Peru) universities? 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA).

Kui, X., Liu, W., Xia, J., & Du, H. (2017). Research on the improvement of Python language programming course teaching methods based on visualization. IEEE Xplore.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33.

Brennan, K., & Resnick, M. (2021). New frameworks for studying and assessing the development of computational thinking”, In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada, pp. 1-25.

Kantaria, M., Basilaia, G., Dgebuadze, M., & Chokhonelidze, G. (2020). Applying a new teaching methodology to university programming language courses.

Lee, W.-M. (2019). Python machine learning. Wiley.

Pajankar, A. (2021). Practical Python Data Visualization: A Fast Track Approach To Learning Data Visualization With Python.

How to Cite
Duli, S. (2021). THE ROLE OF PYTHON PROGRAMMING COURSE IN NATURAL SCIENCES. Knowledge International Journal, 47(3), 469 - 472. Retrieved from