Module: Statistical Data Analysis

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Statistical Data Analysis
Module Code	CSC1140 (ITS: CA660)
Faculty	Computing	School	Engineering & Computing
NFQ level	9	Credit Rating	7.5

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

Section Breakdown
CRN	10628	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC3	Best Mark	N
Module Co-ordinator	Marija Bezbradica	Module Teacher	Andrew Mccarren, Denise Freir, Renaat Verbruggen

Type	Description	% of total	Assessment Date
Assessment 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
Formal Examination	End-of-Semester Final Examination	75%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

Indicative Reading List

Books:

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

Articles:
None

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.