Overview
This topic includes study in:Design of experiments: experiments, observational studies, sample surveys; measurements & variables; replication & pseudo-replication, experimental ethicsDescriptive statistics: graphical & numerical summaries; the shape of a distribution; data screening & outliersExploring relationships: predictor-response data; the least-squares line; prediction; the sample correlation coefficient; time seriesProbability: basic concepts; conditional … For more content click the Read More button below.
Topic availabilities
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Tuition pattern
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Aims
This topic is directed towards students with little quantitative experience. It serves as an introduction to the interdisciplinary and emerging field of data science. Students will learn how to combine tools and techniques from statistics, computer science and data visualization. It aims to impart an understanding of the key issues in the analysis of statistical data together with practical experience in using a modern statistical package to perform elementary statistical analysis in a wide range of applications
Learning outcomes
On completion of this topic you will be expected to be able to:
1.
Apply statistical concepts and techniques to a range of complex statistical problems
2.
Apply principles of data visualisation and data processing to large collections of data
3.
Critically evaluate the results of statistical analysis and present findings to a broad audience
Assessments
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Current students should refer to FLO for detailed assessment information, including due dates. Assessment information is accurate at the time of publishing.
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Requisites information
Anti-requisites: