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Data Science in action In 2022, Pymble Ladies’ College WHAT DATA SCIENCE MEANS launched UCLA’s Introduction to
TO PYMBLE Teaching Data Science is part of
Data Science course for students our College-wide focus on building in Years 9, 10 and 11. We believe responsible and sustainable Digital we are the first school in the Southern Hemisphere to offer a Data Science course. The take-up has been impressive, given students had no prior knowledge of the curriculum, and reflects our learning community’s understanding that STEMrelated fields such as Data Science represent the future of work for our students.
Intelligence, and addressing the chronic under-representation of women working in STEM fields. It creates a life-changing opportunity for students to engage meaningfully with data and be inspired to go on to use their learnings and acquired skills to make the world a better place. • We use data sets to develop self-efficacy and knowledge. Self-efficacy and data literacy are essential to participate actively in society as an employable and valuable worker and an informed citizen. Data Science builds the set of skills and thinking habits that will empower students to infer knowledge from a wide range of data sets, and guide them to make decisions, take actions and form opinions.
STUDENT TAKE-UP IN 2022 Year 9: 40 Year 10: 31 Year 11: 6 TOTAL: 77
• We teach R to analyse data sets. Data Science is an opportunity to teach R coding language to analyse data sets relevant to real-world complexities. It opens a new pedagogical landscape where teachers can transform their practice to offer diverse, differentiated and collaborative learning opportunities that will help our students structure their ideas and become more confident thinkers. • We foster investigative mindsets. Through designing and conducting their own scientific research, initially using small data sets, students deepen and build upon their understanding of analysing and interpreting data as they progress through each course. Students gather, examine, model and critically assess evidence by posing questions, considering relations among variables, generating hypotheses, and evaluating shortcomings and strengths in the data and the data collection process.