Level 6 / Honours-level integration
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Independent data science and statistics honours project
Build practical command of Independent data science and statistics honours project for Data Science and Statistics Pathway: explain the concept, make applied-assessment-ready decisions, complete a hands-on public or synthetic dataset, statistical notebook, database, visualisation tool, model-validation suite, and governance worksheet exercise, and produce evidence that supports the Data Scientist, Statistician, and Decision Analyst Learner role. Work at Level 6 aligned depth by synthesize advanced statistical learning, bayesian reasoning, causal inference, governance, and independent investigation.
Advanced undergraduate study comparable in challenge to a bachelor's degree final year.
Assessment evidence: Honours-style independent data research project, dissertation, reproducible repository, and presentation.
Academic level alignment describes learning depth. It does not confer university credit or an awarded qualification.
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1. Population, sample, variable, distribution, estimand, and measurement
Independent data science and statistics honours project is studied through population, sample, variable, distribution, estimand, and measurement. In this module, learners connect that foundation to data-generating processes, statistical reasoning, computation, uncertainty, prediction, causality, communication, and governance. At Level 6, the expected performance is to synthesise evidence and independently defend an honours-level decision; claims must follow from stated assumptions and relevant evidence rather than from terminology alone. Unlock the paid lesson to read the complete method, worked example, misconception analysis, glossary, evidence task, and answers.
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2. Data quality, missingness, bias, confounding, and leakage
Independent data science and statistics honours project is studied through data quality, missingness, bias, confounding, and leakage. In this module, learners connect that foundation to data-generating processes, statistical reasoning, computation, uncertainty, prediction, causality, communication, and governance. At Level 6, the expected performance is to synthesise evidence and independently defend an honours-level decision; claims must follow from stated assumptions and relevant evidence rather than from terminology alone. Unlock the paid lesson to read the complete method, worked example, misconception analysis, glossary, evidence task, and answers.
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An Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor
Unlock the lesson for all three ranked books, lesson-fit guidance, reading tasks, and edition verification notes. Chapter or page references are shown only where a curator has recorded them.