12-04, 11:00–11:30 (UTC), Data/ Data Science Track
This proposal aims to develop a Python curriculum for data science for multidisciplinary studies in university education. Data Science is nowadays a trending topic in any area like social science, finance, natural science and so many others. Therefore, every student in the university education is keen to learn data science using computer languages rather than using SPSS or other traditional data analysis tools especially related to research. So, this aims to develop a new curriculum for any student studying from any discipline in higher education to learn data science using trending techniques and tools. Python is the core programming language here because it is very widely used and related to data science field. Plus, it has many advantages like easy to learn and use, platform independence used, large and active community support. Utilizing Bloom’s Taxonomy as the guiding framework has developed a new curriculum for four-year degree programs to succeed in data driven world considering multidisciplinary approach. In this curriculum, students can start from Python basic programming concepts to progress to advanced analyzing techniques using libraries like Pandas, NumPy, and Seaborn, and platforms such as Anaconda and Google Colab and finally build own projects in that students related discipline. Ultimately this curriculum will leverage success in Data-centric society in domain specific applications.
Keywords: Bloom’s, curriculum, multidisciplinary, python, science, taxonomy
As we are living in a rapidly evolving, data-driven world and now most professionals open an eye to ways to learn data analytics and how to apply it in their domains. So, Data science-related specialists need to address any field to how to learn proficiency in programming and data analysis. Because traditional academic studies not to success in a knowledge-driven economy. Therefore, this proposal aims to develop a new curriculum to focus on multidisciplinary studies in higher education to fill the gap between traditional academic studies and new practical skills related to data science.
This developed Python curriculum for data science for multidisciplinary studies in university education fulfils the responsibility to society step by step to get ladder of data science and be succusses in any domain. Here compass three main things when developing this curriculum. They are Bloom's Taxonomy, Knowledge Discovery in Databases (KDD) process in data science and Python language and related techniques and tools.
Bloom’s Taxonomy is a framework originally developed by Benjamin Bloom and a team of educators in the 1950s. This has been widely used in the education domain because it guides setup design and development in the curriculum, learning outcomes and assessment methods. Bloom’s Taxonomy is structured in a hierarchical manner and here used the revised version consisting of six stages. They are from bottom to top order remembering, understanding, Applying, Analyzing, Evaluating and creating.
Moreover, the new curriculum covers year by year what aspects related to the KDD process. (Refer Image: PYDATA Bloom Framework: Left-hand corner) So year by year the plan is to gradually increase student knowledge. Because especially when considering multidisciplinary studies, students could come from different backgrounds. So, this is not another burden to them.
Then Python is the core programming language here because it is very widely used and related to the data science field. Also, it has many advantages like easy to learn and use, platform independence used, large and active community support, Integration with other technologies and so many. This new PYDATA Bloom Framework has added python related activates student has to complete tear by year using libraries like Pandas, NumPy, and Seaborn, and platforms such as Anaconda and Google Colab. Additionally, cloud platforms like Snowflake are introduced to familiarize students with modern data warehousing solutions. (Refer image: PYDATA Bloom Framework: right hand corner).
The presentation PYDATA: Multidisciplinary Python Curriculum for Data Science (Second image in right hand corner), it represented activities that need to provide align with learning out comes in year by year. In this proposed curriculum it provides the opportunity to any student in higher education to make own path stating from basic python syntax to creating a project in a relevant study field with the hands-on, practical experience. So, it not only emphasizes technical skills but also promotes critical thinking, problem-solving, and interdisciplinary collaboration. Students will gain the opportunity to work on real work problems through the knowledge they get from projects and case studies and peer evaluations.
As a further development, this proposal is seeking to engage with the open-source community and it’s also inviting collaboration to feedback from data science experts. On the other hand, it also aims to make available all the learning resources and materials online that provide support to cultivate a global community of learns in various domains who are interesting in data science field.
No previous knowledge expected
Hansila Sudasinghe is a dedicated data science professional and educator with a robust background in computing and information systems. She completed her Master of Data Analytics at the Faculty of Graduate Studies, University of Kelaniya, following a Post-Graduate Diploma in Information Technology from the University of Colombo School of Computing. Hansila holds a B.Sc. (special) in Computing & Information Systems from Sabaragamuwa University of Sri Lanka, where she graduated.
With extensive teaching experience across various academic institutions, Hansila currently working as a Lecturer in Computer Science at the Edith Cowan University – Sri Lanka Campus. She has completed a Certificate Course in Teaching in Higher Education and earned badges for her contributions to professional development. Hansila is actively involved in curriculum development and research, contributing to projects that integrate technology and data science with practical applications.
Her expertise spans multiple programming languages and tools, including Python, SQL, Java, and Power BI, with a focus on data analysis, web development, and business intelligence. Recent projects include analyzing social media sentiments on global economic issues and developing data-driven solutions for the apparel industry. She has also co-authored research on topics ranging from obesity prediction models to mobile banking adoption.
An advocate for continuous learning and collaboration, Hansila has actively participated in workshops, technical committees, and community events. She aims to bridge the gap between academia and industry by equipping students and professionals with the skills needed to thrive in a data-driven world.