Module: Statistical Data Analysis

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-ordinator	Semester 1: Denise Freir Semester 2: Denise Freir Autumn: Denise Freir
Module Teachers	Renaat 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

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 real-world systems

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	36	principles and methods
Tutorial	12	examples
Assignment Completion	56	practical application/formal analysis
Independent Study	83.5	reading, 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

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 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 Assessment	25%	Examination Weight	75%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Students 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

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

AMPT	PhD-track
CAPD	PhD
CAPM	MSc
CAPT	PhD-track
CHPT	PhD-track
ECSA	Study Abroad (Engineering & Computing)
ECSAO	Study Abroad (Engineering & Computing)
EEPD	PhD
EEPM	MEng
EEPT	PhD-track
HHPT	PhD-track
MCM	M.Sc. in Computing
SSPT	PhD-track

Date of Last Revision

24-AUG-11

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