Module: Data Analytics and Data Mining

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Data Analytics and Data Mining
Module Code	CA683I
School	School of Computing
Module Co-ordinator	Semester 1: Andrew McCarren Semester 2: Andrew McCarren Autumn: Andrew McCarren
Module Teachers	Denise Freir Andrew McCarren
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Repeat examination

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

1. Demonstrate an understanding of the different levels of measurement and data types.
2. Demonstrate an understanding and ability to apply the underlying probability principles and distribution examples.
3. Demonstrate the ability to distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics.
4. Demonstrate an appreciation of the scope and robustness of common analytical methods for one to many samples.
5. The ability to use a range of analytical statistical techniques and interpret outcomes.
6. The ability to select appropriate statistical software, having been exposed to several examples.
7. Demonstrate the ability to apply techniques to a range of illustrative examples and case studies in big data and other real-world systems.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	36	No Description
Assignment Completion	25	No Description
Independent Study	64	No Description
Total Workload: 125

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

Assessment Breakdown
Continuous Assessment	25%	Examination Weight	75%

Type	Description	% of total
Course Work Breakdown
Assignment	Data Analysis	10%
Assignment	Data Mining	15%

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 1

Indicative Reading List

Other Resources

None

Programme or List of Programmes

CAPD	PhD
CAPT	PhD-track
GCCM	Graduate Certificate in Computing
MCM	M.Sc. in Computing
MCMEC	Micro-Credential Modules Eng & Comp
SMPEC	Single Module Programme (Eng & Comp)

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