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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Statistical Data Analysis
Module Code CA660
School School of Computing
Module Co-ordinatorSemester 1: Denise Freir
Semester 2: Denise Freir
Autumn: Denise Freir
Module TeachersRenaat Verbruggen
Denise Freir
Martin Crane
Marija Bezbradica
Andrew McCarren
NFQ level 9 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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

Workload Full-time hours per semester
Type Hours Description
Lecture36principles and methods
Assignment Completion56practical application/formal analysis
Independent Study83.5reading, understanding, applying conceots and reviewing examples
Total Workload: 187.5

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

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 Non-parametrics
less rigorous assumptions and distributional requirements

Advanced methods
experimental design and Multivariate - an outline

Complex Systems Models & Analysis
Problem-solving : blueprint/approach for real-world data analytics Illustrative Examples/Case Studies from Biology,Env. Sci., Business & Finance

Assessment Breakdown
Continuous Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentStudents wil be asked to formally assess and analyse a cae study/dataset and report on the required analyses and outcomes. Choice of software used will also be considered.25%Week 7
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 3
Indicative Reading List

  • Crawley M.J.: 2005, Statistics: An Introduction Using R, Wiley-Blackwell, Chichester, West Sussex, England, 0470022981
  • Wheater C.P. and Cook P.A. ; illustrated by Wright J.: 2000, Using Statistics to understand the Environment, Routledge, 0415198887
  • Moore P.: 1997, Introductory Statistics for Environmentalists, (Environmental Management, Science & Technology), Ellis Horwood, 013128077
  • Gotelli N.J. , Ellison A.M.: 2004, A Primer of Ecological Statistics, Sinauer Associates Publishers, Sunderland, MA, 0878932690
  • Davenport T.H.and Harris J.G.: 2007, Competing on Analytics, Harvard Business School Press, Boston, Mass., 1422103323
  • Davenport T.H. and Harris J. G. and Morison R.: 2010, Analytics at Work: Smarter Decisions, Better Results, Harvard Business Press, 1422177696
  • Vose D.: 2008, Risk Analysis:A Quantitative Guide, Wiley & Sons, 0470512849
  • Savage S.L., with Danziger J.(Illustrator): 2009, The Flaw of Averages, Wiley & Sons, 0471381977
  • McKillup S.: 2011, Statistics Explained: An Introductory Guide for Life Scientists, Cambridge University Press, 0521183286
  • Ekstrom C.T., Sorensen H.: 2010, Introduction to Statistical Data Analysis for the Life Sciences, CRC Press, 1439825556
  • Draghici S.: 2012, An Introduction to Statistics and Data Analysis for Bioinformatics using R, Chapman & Hall/CRC, 1439892369
Other Resources

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
ECSAStudy Abroad (Engineering & Computing)
ECSAOStudy Abroad (Engineering & Computing)
MCMM.Sc. in Computing
Date of Last Revision24-AUG-11

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