Rebecca is Teacher Fellow at The Science Teachers Association of New South Wales (STANSW), who is developing a suite of resources to support science teachers. The Data Science Lesson Series: Patterns in the Sky, is available to current STANSW members. It develops core data science competencies for Year 7-8 students using authentic astronomical datasets and concepts from the Observing the Universe focus area.
Data now shapes much of how young people encounter the world. News reports, social media feeds, sporting commentary and climate discussions are all saturated with graphs and statistics. In New South Wales, this reality is reflected in the Science 7–10 (2023) Syllabus, which expects students to represent and analyse data as part of their core scientific work (New South Wales Education Standards Authority, 2023). The key question for schools is not whether data science belongs in science education, but how to integrate it in a sustainable and pedagogically sound way that benefits every student.
Why data science matters for science education in NSW
During the COVID-19 pandemic, students and their families were continually presented with plots of case numbers, vaccine effectiveness and risk projections. Some were able to interpret these representations and weigh claims. Others could only rely on authority. Hunter-Thomson describes the central goal as widening students' “inference space”: their capacity to decide what conclusions are justified by a given dataset (Hunter-Thomson, 2024). This inference space refers to the range of logical conclusions a person can draw from data based on their analytical skills and understanding. That capacity is not acquired automatically in adulthood. It is the product of repeated, structured engagement with data over time.
Similar demands arise in climate change, economic policy and public health debates. When students work with Bureau of Meteorology data on temperature and rainfall, or Australian Bureau of Statistics tables on health and housing, they see how evidence can both clarify and complicate public arguments. Lee argues that such data practices cultivate habits of reasoning that transfer beyond science, supporting more critical engagement with information in all learning areas (Lee, 2025). In this sense, integrating data science into secondary science supports not only disciplinary understanding but also informed citizenship in New South Wales
Integrating data science without overloading teachers
Some science teachers associate “data science” with advanced statistics or programming and understandably view it as an additional burden. Empirical work suggests a different starting point. Fischer and colleagues worked alongside science teachers who had limited formal preparation in data science. They found that successful classrooms built from familiar practices such as practical investigations, class discussions and basic use of spreadsheets (Fischer et al., 2025). In these classrooms, data science was introduced as an extension of existing inquiry, not as a separate subject.
Weintraub and Hunter-Thomson propose a practical principle: “leverage the data you have” (Weintraub & Hunter-Thomson, 2022). Rather than designing entirely new units, teachers take an existing activity, such as a reaction rate investigation or plant growth experiment. They then add one or two questions that require students to graph class results, compare them with a publicly available dataset or identify a pattern that has not yet been discussed. This approach maintains continuity with current programs and uses data students already collect.
Technological barriers have also reduced. Web-based analysis environments such as the Common Online Data Analysis Platform allow students to create graphs, filter cases and explore relationships between variables through direct manipulation of data tables and displays (Sagrans et al., 2022). No installation or coding is required. This enables teachers to concentrate on interpretation and questioning, rather than technical details. Integration becomes manageable when small, regular data activities are embedded within existing lessons instead of being treated as an additional large “data science unit”.
Where to begin in Year 7: essential foundations
For many Year 7 students, first encounters with authentic datasets feel complex and confusing. Erickson describes this experience as being “awash in data”, uncertain about where to begin or what is important (Erickson, 2025). Initial teaching should emphasise simple, concrete experiences that build confidence.
Three elements are particularly important at the beginning of Stage 4. First, students need to understand data as observations situated in context. Groth notes that this broader conception, which includes categories, images, time stamps and locations as well as numbers, helps students connect data work with experiments, field observations and social questions (Groth, 2025). Second, students should be able to organise modest datasets into tables and simple spreadsheets. They should also recognise obvious errors or omissions. Third, they should have frequent opportunities to construct and interpret straightforward graphs, such as bar charts, dot plots and time series. Teachers can support this with questions like “what does this display show” and “what might explain this pattern”.
These foundations can be developed within almost any Year 7 topic. In a unit on Forces, students might gather repeated measurements from a trolley on a ramp and construct their own force-distance graph. In Observing the Universe, they might record sunrise times or lunar phases over several weeks and represent them visually. The Science 7–10 (2023) Syllabus supports this kind of work by asking students to “use a range of representations to organise data” and to “select the type of graph best suited” to a dataset as part of Working Scientifically (New South Wales Education Standards Authority, 2023). Year 7 does not require sophisticated statistical techniques. It requires regular, meaningful engagement with data in forms students can understand.
Pedagogical approaches for teaching data skills in science
When students work with genuine data, they quickly discover that it is often incomplete, noisy or ambiguous. Effective pedagogy does not conceal this. Rather, it uses such features as opportunities for reasoning. Zhu and colleagues introduce the SPIRE model, which consists of the phases Stimulate, Practice, Improve and Reflect (Zhu et al., 2025). Learning is anchored in authentic questions that matter to students, such as local air quality or resource use. Students then practice data handling and analysis, receive feedback that helps them improve their approach and reflect on both the content and the process they have used.
Erickson emphasises the educational value of exploratory work with data (Erickson, 2025). Allowing students to try different graph types, adjust filters and regroup cases helps them appreciate that analytical choices shape what can be seen and concluded. This sense of agency is important for developing flexible reasoning about data analysis.
Contextual framing also matters. The Patterns in the Sky lessons, developed by the Science Teachers Association of New South Wales, deliberately place Aboriginal and Torres Strait Islander astronomical knowledge alongside Western models of the Sun, Earth and Moon system (Science Teachers Association of New South Wales, 2025). This highlights long traditions of systematic observation, pattern recognition and prediction in First Nations science. Lee suggests that such human-centred, culturally responsive approaches help students view data science as a way of engaging with the world, not simply as a technical skill set (Lee, 2025).
Student-friendly classroom resources
Several resources allow teachers to incorporate data science into lessons without extensive redevelopment of programs. The Science Teachers Association of New South Wales Data Science Integrated Lesson Series 1, “Patterns in the Sky”, offers a sequence of lessons that use real astronomical and seasonal data. Students begin by extracting and tabulating data from information sources. They then build towards creating graphs, identifying patterns and connecting these to both scientific explanations and Aboriginal and Torres Strait Islander perspectives on the night sky. The series includes detailed teacher notes and student worksheets. Its guiding principles are to start with phenomena with which students are familiar, use authentic datasets, respect multiple knowledge systems and reduce planning time for teachers by providing comprehensive scaffolding.
The Common Online Data Analysis Platform provides a browser-based environment where students can explore prepared or imported datasets using drag-and-drop graphing and interactive filtering (Sagrans et al., 2022). There is no requirement for student accounts or software installation. With a membership, journals of the National Science Teaching Association, including Science Scope and The Science Teacher, publish classroom-ready activities that invite students to analyse existing datasets in topics such as disease spread, ecology and climate (Weintraub & Hunter-Thomson, 2022). These tasks can be adapted readily to the New South Wales context and to the expectations of the current syllabus.
Sources of authentic Australian and global datasets
Once students are comfortable with small, structured datasets, teachers can access authentic sources of larger data that can support more open investigations. Within Australia, the Bureau of Meteorology offers extensive records of temperature, rainfall and extreme events, downloadable in formats suitable for spreadsheets and online analysis tools (Sagrans et al., 2022). The Australian Bureau of Statistics provides public data on population, health, education and environmental indicators that can be used in human biology and Earth and environmental science contexts (Murphy et al., 2018). State and federal environmental agencies publish datasets on air quality, water quality and biodiversity monitoring, which allow students to connect lesson content directly to local environments (Witte et al., 2025).
At the global level, organisations such as the US National Aeronautics and Space Administration make climate, atmospheric and oceanic datasets freely available. Platforms like Kaggle host cleaned datasets on a wide range of topics, from global emissions to food security. Avila-Garzón and colleagues caution that for school use, datasets should be chosen with care: they should be of manageable size, clearly documented and closely connected to questions that are meaningful for students (Avila-Garzón et al., 2025).
A progression from Stage 4 to Stage 5
A planned progression across Years 7 to 10 can make data science integration more coherent and less demanding for teachers. In early Stage 4, the focus is on collecting and organising small datasets, drawing simple graphs and articulating basic observations. Short “data conversations”, in which students describe what a graph shows and suggest possible explanations, can serve as formative assessments and reveal their current reasoning (Hunter-Thomson, 2024).
By the end of Stage 4, students can begin to compare groups, comment informally on variability and recognise outliers. They can work with modest secondary datasets, such as a year of local rainfall records or a sample of species observations from citizen science projects. These experiences prepare them for more complex work in Stage 5.
In early Stage 5, attention can turn to relationships between variables. Students might investigate how temperature and humidity interact, or how one health outcome varies across regions. Modest projects in which students pose a question, select a dataset, create a small set of graphs and write a brief commentary can function as both learning activities and components of summative assessment.
By late Stage 5, students are ready for depth studies in which data plays a central role. They can analyse multiyear climate records, evaluate trends in local water quality, or explore changes in species abundance. At this level, they comment not only on patterns but also on data quality and limitations. Assessments that reward these forms of judgement, as well as technical accuracy, align well with the intent of the Science 7–10 (2023) Syllabus.
Conclusion
Integrating data science into science education in New South Wales is both important and achievable. Success does not require that every teacher become a statistician. It does require deliberate attention to data in everyday lessons, use of appropriate pedagogical strategies and adoption of high-quality, freely available resources. Schools should begin with simple, concrete data experiences in Year 7 and build systematically towards more complex analysis and interpretation by Year 10. By taking this approach, schools can help students learn to work confidently with data, to think critically about evidence and to participate more fully in a society where data-informed decisions are increasingly the norm.
References
- Avila-Garzón, C., & Bacca-Acosta, J. (2025). Curriculum, pedagogy, and teaching/learning strategies in data science education. Education Sciences, 15(2), Article 186. https://doi.org/10.3390/educsci15020186
- Erickson, T. (2025, July). Awash in data. CODAP. https://codap.xyz/awash/
- Fielding, J., Makar, K., & Ben-Zvi, D. (2025). Developing students' reasoning with data and dataing. ZDM Mathematics Education, 57, 1–18. https://doi.org/10.1007/s11858-025-01671-6
- Fischer, M., Pritchard, C., Xu, Z., & Rosenberg, J. (2025). Finding your way into data science education as a science teacher. The Science Teacher, 92(6), 49–55. https://doi.org/10.1080/00368555.2025.2558519
- Groth, R. E. (2025). Data science teacher education goals: Essential elements of Pre-K–12 data science curriculum implementation. Journal of Statistics and Data Science Education, 13(1), 1–13. https://doi.org/10.1080/26939169.2025.2526627
