Registry
Module Specifications
Archived Version 2019 - 2020
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Description This module 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. 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. | |||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Demonstrate an understanding of the different levels of measurement and data types. 2. Demonstrate an understanding and ability to apply the underlying probability principles and distribution examples. 3. Demonstrate the ability to distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics. 4. Demonstrate an appreciation of the scope and robustness of common analytical methods for one to many samples. 5. The ability to use a range of analytical statistical techniques and interpret outcomes. 6. The ability to select appropriate statistical software, having been exposed to several examples. 7. Demonstrate the ability to apply techniques to a range of illustrative examples and case studies in big data 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 Measurement type, data types and quality. Consistency.Review of basis probability, probability distributions and their uses.Practical examples from relevant application : e.g. business , decision-making, queuing and service; bio-ict, - e.g. recombination and mapping functions, segregation, rare events and motif detection; social interaction, e.g. networks, and introduction to stochastic time series basis for event pattern recognition.Bayes Methods and revised probabilities from new data feedback/incorporationEstimationClassical vs MLE, Bayesian and outline of when to use Non-parametrics.Estimation and Hypothesis Testing for one to many-samples.ANOVA (basic Experimental Design principles), and equivalent for discrete data – e.g. Contingencies (for several populations), as well as extensions to linear regression.ApplicationsExamples for above, inc. time series features for time-conditioned data in e.g. bio-ict, social nets, business and financial series.Software options | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||
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