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COMP8046 - Information Analytics

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Title:Information Analytics
Long Title:Information Analytics
Module Code:COMP8046
 
Credits: 5
NFQ Level:Advanced
Field of Study: Computer Science
Valid From: Semester 1 - 2016/17 ( September 2016 )
Module Delivered in 3 programme(s)
Module Coordinator: TIM HORGAN
Module Author: Ted Scully
Module Description: In Information Analytics a learner will use a range of analytical techniques to gain valuable insights from data for a specific application domain. The module will focus on the application of machine learning techniques that facilitate the identification of trends and patterns in data over time.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Describe the concepts, principles, methods and techniques of machine learning and its role in knowledge discovery.
LO2 Utilise data pre-processing and manipulation techniques on data from a specific application domain.
LO3 Select and apply appropriate machine learning algorithms to a range of datasets.
LO4 Analyse and interpret patterns and knowledge discovered from the application of machine learning algorithms to problems from a specific application domain.
LO5 Evaluate the accuracy of machine learning algorithms.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Introduction
Overview of terminology and applications in the area data science and analytics.
Data Extraction and Handling
Importation of data in different formats and from various sources. Cleaning/scrubbing data, data modelling and methods such as reshaping, manipulation and data filtering.
Predictive Modelling and Classification Methods
Decision Trees Induction, Bayesian, Rule-Based and Ensemble Learning.
Clustering Analysis
Categories of Clustering (e.g., Partitioning Methods, Hierarchical Methods), Identification of data clusters using k-means algorithm, k-centre approximations, density-based clustering.
Textual Analysis
Textual Mining and Sentiment analysis using industry standard tools such as SAS.
Validation
Testing and validating the algorithm accuracy using standard techniques e.g. simple split, k-fold cross-validation, bootstrapping.
Report Generation and Data Visualisation
Visualisation Techniques and Applications. Ability to visualise data in appropriate forms such as Spatial Data, Multivariate Data Trees, Graphs, etc.
Analytics in Industry
How data analytics is used within a business setting to monitor performance and identify significant trends in data (customer sentiment, product sales, etc.).
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Individual Project. Apply a machine learning algorithm to a specified data mining problem and produce a report documenting accuracy. 1,2,3 40.0 Week 6
Project Individual Project. Evaluate, select and interpret patterns and knowledge discovered as a result of applying a machine learning algorithm to a dataset from a specific application domain. Findings should be communicated in a final report incorporating visualisation. 2,3,4,5 60.0 Sem End
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture delivering theory underpinning learning outcomes 2.0 Every Week 2.00
Lab Computer Based Lab to support learning outcomes 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Independent Study 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture delivering theory underpinning learning outcomes 2.0 Every Week 2.00
Lab Computer Based Lab to support learning outcomes 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Independent Study 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Ramesh Sharda, Dursun Delen, Efraim Turban 2014, Business Intelligence and Analytics: Systems for Decision Support, 10th Ed., Pearson [ISBN: 0133050904]
Supplementary Book Resources
  • Peter Flach. 2012, Machine learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge, UK; Cambridge University Press [ISBN: 1107422221]
  • Christoper M. Bishop 2013, Pattern Recognition and Machine Learning, Springer [ISBN: 8132209060]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_KCMSD_8 Higher Diploma in Science in Cloud & Mobile Software Development 2 Mandatory
CR_KINDD_9 Master of Science in Information Design and Development 2 Elective
CR_KIDDE_9 Postgraduate Diploma in Science in Information Design and Development 2 Elective

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