​Overview
This activity enhances data literacy by guiding participants to critically engage with a complex, multi-layered data visualisation, fostering the ability to interpret and question visual data representations. By analysing separate datasets - timeline, triip counts, geography, and temperature - learners develop skills in understanding data sources, regognising gaps, and appreciating how different data types contrubute to a fuller story. Through data aggregation, participants see how combinimng datasets can clarify narritives and reveal deeper insights.
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Ultimately, the activity cultivates the essential skill of constructing meaningful evidence-based stories from diverse data, which is as the heart of data literacy.
Deconstruct
A DataViz
Learnings
Develop the ability to critically interpret complex, multi-dimensional data visualisations.
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Understand the importance of examining and questioning data sources and their limitations.
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Recognize how different types of data (temporal, quantitative, geographic environmental) contribute to a complex narrative.
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Enhance critical thinking skills about data accuracy, assumptions, and gaps in information.
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Learn how data aggregation and joins can clarify and deepen insights
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Build skills in constructing meaningful, evidence-based stories from diverse datasets
​Overview
In this activity you will develop the ability to interpret and manipulate data by identifying key attributes such as ingredients and cooking techniques. You will apply algorithmic thinking to analyse and compare these datapoints, recognising patterns and similarities across different dishes. Through exploring unstructured or semi-structured data like recipes you will learn how to draw meaningful insights beyond surface level observations. Additionally, you will enhance your skills in communicating complex findings by translating algorithmic results into clear and understandable patterns.
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Overall this experience will strengthen your data fluency and equip you with practical techniques for analysing real world datasets.
Recipe Insights
Learnings
Data Cleansing Skills: Enhances ability to identify and correct inconsistencies in raw data for accurate analysis.
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Data Standardisation: Builds fluency in normalising data for reliable comaprisons.
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Data Representation: Demonstrates how to transform unstructured data into formats suitable for computational analysis.
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Similarity Measures: Shows the importance of techniques like cosine similarity for quantifying relationships within data.
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