Registry
Module Specifications
Archived Version 2015 - 2016
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Description Aims to review and complement foundation statistical knowledge and to establish the context for a range of methods, used in the analysis of simple and complex systems. Reasonable proficiency in algebra and the ability to grasp concepts of probability and its importance are predominantly required. The emphasis is on an intuitive understanding of the principles and a practical ability to apply these to data examples drawn from diverse systems, rather than mathematical sophistication. | |||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. 'demonstrate' that they understand different levels of measurement and data types 2. 'demonstrate' that they understand and apply underlying probability principles and distribution examples 3. 'demonstrate' that they can distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics 4. 'demonstrate' that they appreciate the scope and robustness of common analytical methods for one to many samples 5. use a range of analytical statistical techniques and interpret outcomes 6. select appropriate statistical software, having been exposed to several examples; (options for practical work) 7. 'demonstrate' that they can apply techniques to a range of illustrative examples and case studies in complex bio- and other real-world systems | |||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and
Learning Activities ReviewBasic Probability and nature of Statistical InvestigationsProbability distributions/Bayes and data handlingDiscrete and Continuous distributions and examples Conditional, joint Probability, data and BayesSampling distributions and Statistical Inferencegenration, interpretation, use and examples for data typesEstimation and Hypothesis TestingOne, two, many samples and principal analytical methodsMore on many samplesAnalytical methods for counts/proportions, means/variances and role of regressionPrinciples of Non-parametricsless rigorous assumptions and distributional requirementsAdvanced methodsexperimental design and Multivariate - an outlineComplex Systems Models & AnalysisProblem-solving : blueprint/approach for real-world data analytics Illustrative Examples/Case Studies from Biology,Env. Sci., Business & Finance | |||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||
Programme or List of Programmes | |||||||||||||||||||||||||||||||||||||||||
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