Module: Data Analytics and Data Mining

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Analytics and Data Mining
Module Code	CSC1171 (ITS: CA4046)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	5

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.

Learning Outcomes

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2. Explain the different levels of measurement and data types within the context of data analytics and data mining.
3.
4. 7,6
5. 1
6. 1DAAC59F-86FD-0001-A0A8-51108EF01811
7. Apply underlying probability principles and distribution examples for data analytics and data mining.
8.
9. 8
10. 2
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12. Distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics.
13.
14. 9
15. 3
16. 1DAAC59F-9873-0001-2446-953F11F09160
17. Appraise the scope and robustness of common analytical methods for one to many samples.
18.
19. 11
20. 4
21. 1DAAC59F-9CF7-0001-2835-1C619660F110
22. Implement a range of analytical statistical techniques and interpret outcomes.
23.
24. 10
25. 5
26. 1DAAC59F-A3E5-0001-5F6E-9050F6931F6A
27. Select an appropriate statistical software, having been exposed to several examples.
28.
29. 11
30. 6
31. 1DAAC59F-A88A-0001-544A-1DC41840F4B0
32. Apply data analysis and data mining y techniques to a range of illustrative examples and case studies in big data and other real world systems.
33.
34. 8
35. 7

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	Lecture Delivery.
Independent Study	101	Independent course reading and working through in class example.
Total Workload: 125

Section Breakdown
CRN	11946	Part of Term	Semester 1
Coursework	50%	Examination Weight	50%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Maryam Basereh	Module Teacher	Andrew McCarren, Denise Freir

Type	Description	% of total	Assessment Date
Assessment Breakdown
In Class Test	In class supervised lab exam on data analysis	20%	Once per semester
Group assignment	Data Analysis Project: Students will complete a group project on data analysis. The project team will submit code , documentation and report documenting their findings	20%	Once per semester
Oral Examination	This will be an interactive oral exam where students discuss their project and defend method selection, experimental design, analysis and results. Marks will be allocated equally although additional individual marks may be allocated to a student who has been found to have performed exceptionally.	10%	Sem 1 End
Formal Examination	End-of-Semester Final Examination	50%	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

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/incorporation

Estimation
Classical 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.

Applications
Examples for above, inc. time series features for time-conditioned data in e.g. bio-ict, social nets, business and financial series.

Software options

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None