Module Specifications..
Current Academic Year 2023  2024
Please note that this information is subject to change.
 
None Array 

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 realworld 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 

Indicative Content and Learning Activities
Review Basic Probability and nature of Statistical Investigations Probability distributions/Bayes and data handling Discrete and Continuous distributions and examples Conditional, joint Probability, data and Bayes Sampling distributions and Statistical Inference genration, interpretation, use and examples for data types Estimation and Hypothesis Testing One, two, many samples and principal analytical methods More on many samples Analytical methods for counts/proportions, means/variances and role of regression Principles of Nonparametrics less rigorous assumptions and distributional requirements Advanced methods experimental design and Multivariate  an outline Complex Systems Models & Analysis Problemsolving : blueprint/approach for realworld data analytics Illustrative Examples/Case Studies from Biology,Env. Sci., Business & Finance  
 
Indicative Reading List
 
Other Resources None  
Other resources, such as specific articles and web sites will be provided as appropriate or required, e.g. for tutorial back up and for case study reading/assignment. These are not listed explicitly as will be selected from recent/most relevant.  
Programme or List of Programmes
 
Date of Last Revision  24AUG11  
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