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	CA683
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. Explain the different levels of measurement and data types within the context of data analytics and data mining.
2. Apply underlying probability principles and distribution examples for data analytics and data mining.
3. Distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics.
4. Appraise the scope and robustness of common analytical methods for one to many samples.
5. Implement a range of analytical statistical techniques and interpret outcomes.
6. Select an appropriate statistical software, having been exposed to several examples.
7. 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.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	36	Lecture Delivery.
Assignment Completion	25	Working on practical assignments.
Independent Study	126.5	Independent course reading and working through in class example.
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

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	Assessment Date
Course Work Breakdown
Assignment	Data Analysis Project: Students will complete a group project in data analysis. The project team will provide an end of project presentation. Marks will be allocated equally although additional marks may be allocated to a student who has been found to have performed exceptionally.	10%	Once per semester
Assignment	Data Mining Project: Students will complete a group project in data mining. The project team will provide an end of project presentation. Marks will be allocated equally although additional marks may be allocated to a student who has been found to have performed exceptionally.	15%	Once per semester

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
CAPM	MSc
CAPT	PhD-track
ECSA	Study Abroad (Engineering & Computing)
ECSAO	Study Abroad (Engineering & Computing)
EEPT	PhD-track
HHPT	PhD-track
MCM	M.Sc. in Computing

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