Earning Criteria

• Work in a group to collate, clean and format data on a chosen topic, then create and present effective visualisations of that data. Demonstrates the use of Git for collaboration, APIs and Pandas to source, clean and format datasets, and Matplotlib to create compelling visualisations of their data.  Groups also prepare a detailed report on the work processes, challenges and solutions of their project.
• Use machine learning to visualise and analyse data on a chosen issue or problem, glean and effectively present valuable information on that issue, and suggest solutions. Write a technical report on the processes performed.  Demonstrates ETL methodology on multiple real datasets,  data pre-processing techniques and algorithms including logistic regression, SVM, KNN, decision trees, and random forests and the creation of a web-based data visualisation dashboard application.
• Work in a group to tell a story through data in an interactive and visually compelling way, focusing on providing an interactive means to explore data. Demonstrates the use of Python Flask application (API, HTML/CSS, JavaScript) powered by a large dataset, driven by user-focused interactions and a visualisation providing multiple views to present their findings in a variety of ways. Groups also prepare a presentation detailing a data deep-dive and infrastructure walkthrough.