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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-ordinatorSemester 1: Andrew McCarren
Semester 2: Andrew McCarren
Autumn: Andrew McCarren
Module TeachersDenise Freir
Andrew McCarren
NFQ level 9 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Repeat examination

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.

Workload Full-time hours per semester
Type Hours Description
Lecture36Lecture Delivery.
Assignment Completion25Working on practical assignments.
Independent Study126.5Independent 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

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.

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 Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentData 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
AssignmentData 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

    Programme or List of Programmes
    ECSAStudy Abroad (Engineering & Computing)
    ECSAOStudy Abroad (Engineering & Computing)
    MCMM.Sc. in Computing

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