Registry
Module Specifications
Archived Version 2019 - 2020
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Description This module will familiarise the student with basic data analytics techniques as used in present and future-generation marketing. It will cover the basic statistical approaches as well as the data analytics pipeline of data acquisition, cleansing, storage, mining, actioning, and visualisation. It will include hands-on access to some aspects of this pipeline as well as the use of advanced analytics including psychometric profiling and machine learning (both supervised and unsupervised) | |||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Understand the importance of the data analytics pipeline, particularly as applied to marketing applications. 2. Understand how various machine learning applications operate, covering both supervised and unsupervised and also ranging into coverage of deep learning. 3. Understand various aspects of data visualisation as an output from an analytics process, including what makes good, bad and indifferent visualisation 4. Gained experience in creating good (and bad) data visualisations. 5. Understand psychometrics and personality profiling, how it works, its pros and cons. 6. Have an understanding of the importance and potential use for advanced marketing analytics developments, especially the use of psychometric profiling. | |||||||||||||||||||||||||||||||||||||
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 |
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Other Resources 30410, Online resources available through Loop, 0, Online resources, | |||||||||||||||||||||||||||||||||||||
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