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Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title Data Analytics & Data Mining
Module Code CA683 (ITS) / CSC1144 (Banner)
Faculty Engineering & Computing School Computing
Module Co-ordinatorAndrew Mccarren
Module Teachers-
NFQ level 9 Credit Rating 7.5
Pre-requisite Not Available
Co-requisite Not Available
Compatibles Not Available
Incompatibles Not Available
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 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

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 Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentIn Class Test In class supervised lab exam on data analysis25%Once per semester
AssignmentIn Class Test In class supervised lab exam on data analysis25%Once per semester
Indicative Reading List

    Other Resources

    None
    CA/Exam split change agreed by PB 50/50 from 25/75

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