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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.
LEANING INTO OUR DATA SCIENCE COMMUNITY Real-world learning is a key component to learning at Pymble – particularly in rapidly emerging and changing fields such as Data Science. Early in 2022, we celebrated the launch of our Introduction to Data Science course with an event attended by our Data Science students, teachers and 100 family, friends and supporters. The purpose of the event was to inspire members of our community who work in STEM-related tertiary, industry or community organisations to form long-term partnerships with the College to give our pioneering Data Science students authentic opportunities to learn in the field. These opportunities include, but are not limited to: • mentoring • support for specific projects • internships • designing learning resources • providing contextualised datasets for investigation.
At our launch event, students had the opportunity to connect with professionals working in STEM-related fields and work with potential mentors on data profiles.
Our Pymble Learning Partners program has its own microsite, which is updated as new opportunities arise and shared with potential learning partners.
Data Science is one of my favourite courses because… “…we have the ability to ask questions and dictate different parts we want to focus on.
What I love about Data Science...
The lessons give us the opportunity to learn
“…when I first started the course I was
a variety of things and discover new ways
extremely sceptical and soon learnt that
to deal with the data we collect. The size of the
resilience and adaptability were essential
class also means we have more time to discuss
if I wanted to succeed. There were many
with our peers and share perspectives.”
problems I needed to solve but the feeling
– Rachel (Year 10)
of accomplishment when I did is one of the reasons why I love it.” – Senu (Year 11)
DATA SCIENCE PROJECTS IN ACTION Year 9 – Clothing Project
Year 10 – Mirror Mirror Project
Students collected data using a Google Form, then asked various statistical questions of this data before analysing the data and developing insights to support a presentation of their findings.
How small can a changeroom mirror be while allowing you to see your whole self? This activity aimed to consolidate learning in preparation for the second assessment task for Year 10. Using the data cycle as a scaffold, students measured dimensions of classroom objects (including themselves) and the dimensions of their reflections in a mirror. Using R, students were quickly able to identify relationships between variables and “discover” the law of reflection.
“This project allowed me to extend the skills we have been learning in class and properly understand how to formulate questions based on
“ This was an especially complicated project.
a collection of real-world data. One of the most
We had to collect data and design the size
valuable things that I learned was that not all data
of a boutique’s changeroom mirror. We took
has a conclusion. To elaborate, we had to use
measurements of objects and compared them
the data cycle to question, observe, interpret and
to their reflections to tell us how big the mirror
analyse the data, and I expected there would be
had to be while minimising the cost to get them
one answer that drew a conclusion. Instead, I found that there wasn’t an answer to the
manufactured. The project showed how many applications there are for Data Science – and
question my partner and I came up with,
it was fun measuring reflections.”
rather, the answer was in the actions we
– Rachel (Year 10)
took as a society to properly manage our clothing usage.” – Ollisha (Year 9)
DATA SCIENCE PROJECTS IN ACTION (CONTINUED) Year 11 – Our World in Data Project Students were asked to choose a graph from ourworldindata.org, learn the grammar of graphics with the ggplot2 package using resources such as the R Graph Gallery or Data Visualisation, and write an R script to copy the graph.
What I love about Data Science... “…is that we can learn content that is less textbook-based and that makes us think critically and out of the box. I also enjoy working collaboratively with others to hear their thoughts and also produce work that others in the class could compare, critique, improve and learn from (and vice versa).” – Ollisha (Year 9) “...the projects we complete are related to reality, therefore we can understand some social trends.”
“ I successfully learnt how to
– Manni (Year 11)
visualise the data and some basic data selection during this project. Using the geom_area function, I learnt how to make a stacked area graph. In addition, I was able to manipulate labels and legends to the desired size, position, theme and colour.” – Manni (Year 11) Schools Weather and Air Quality (SWAQ) Data Analysis Project Students investigated a large dataset to find statistical evidence of the 2019 bushfires in relation to air quality at different schools across NSW.
“SWAQ compared the air quality between spring and summer in
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2019 and 2020. One year had raging bushfires while the other was in lockdown, hence several conclusions and observations were made. As well as learning
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about the impact humans and bushfires have on the
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planet, I have learnt how to analyse patterns and
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draw conclusions, and coding skills such as filtering, selecting and graphing data.” – Senu (Year 11)