Probabilistic Modelling & Reasoning and Big Data Analytics

Module Description

This module has two parts. The first part of the module will equip learners with the basic understanding of probability theory, Bayesian network representation including Independence properties, I-Maps, Undirected Graphical Models, Local Models etc.

Learners will also learn Exact and Approximate inference in Bayesian networks such as Complexity Analysis and Junction trees, Approximate inference in Bayesian networks.

Learners will also get an exposure of Hidden Markov models (HMM), HMM Inference and Dynamic Bayesian networks.

The second part of the module will equip learners with the Big Data Analytics background required for Business Intelligence. It will include Big Data Analytics,

Framework for Big Data Analysis, Approaches for Analysis of Big Data, ETL in Big Data, Introduction to Hadoop Ecosystem, the Hadoop Distributed File System (HDFS), Map- Reduce Programming etc.

Entry Requirements

Candidates who apply for this course must possess one of the following: 

• a Level 6 degree related to AI/Computer-Science/Mathematics/Electronics; 

OR 

• a Level 6 degree not related to AI/Computer Science/Mathematics/Electronics and a minimum of two years’ relevant experience; 

 OR 

• a Level 5 diploma or higher diploma and five years’ of relevant work experience.   

Target Audience

This course is targeted at: 

• Industry professionals working in different domains, including Technology, Engineering, Science, IT, Finance, Accountancy, Management, Marketing, Insurance, Banking, Gaming, Healthcare, Medicine, Pharmaceutical, Human Resources, Psychology, Blockchain, Legal, Administration, Policy Making, Digital Art, Archaeology, Architecture, Education and other related areas. 
• Recent graduates with degrees in Computer Science, Technology, Marketing, Finance, Economics, Accountancy, Management, HR, Law, Engineering, Science, Medicine, Psychology, Digital Art, Game Development, Archaeology, Architecture or Business. 

• Mid-career-break professionals looking for opportunities to return to or change their career.  

The target group may also be extended to positions such as that of wedding manager, transport manager, maintenance manager, operations manager, marketing manager, conference manager and even that of general manager. 

Module / Unit Instructions

The proposed structure comprises a blended approach promoting the building of a community of practice via peer-to-peer learning.

The structure uses primarily two dimensions of teaching-learning modes:

1. Face-to-face sessions: 18 hours.
2. Online Learning Activities: 12 hours.

Face to Face sessions

• Face-to-face sessions include lectures, tutorials, discussions, presentations and workshop activities promoting peer-to-peer learning.

Online Learning Activities

Online learning activities incorporate tutorials and asynchronous discussions. These may consist of active interaction, participation and contributions in fora discussions, sharing resources and self-reflection exercises.

Learners also contribute to the building of the community of practice by providing feedback to their peers as critical friends, enhancing the learner’s critical engagement throughout the study period.

The tutor provides continual support during both teaching -learning modes by providing information, readings and tasks relevant to the module in question.

The tutor provides continuous formative feedback as an on-going guidance during the student’s learning experience in preparation for their summative assessment.

How you’ll be assessed

Assessment of each module consists of two assignments, each carrying a weighting as below:

a) One Formative assignment carries 20% of total module mark achieved. b) One Summative assignment carries 80% of total module mark achieved.

For successful completion of a study module the student is required to achieve a minimum of 41% pass mark in both the formative and the summative assignment.

The overall grade achieved for each module is calculated as the sum of:  20% of the mark achieved for formative assignment; and  80% of the mark achieved for the summative assignment.

All assignment tasks of both formative and summative assessment aim to provide the learner an opportunity to produce evidence of his/her competences aligned to the learning outcomes of each individual Module.

Assessment

a) Formative assessment tasks are provided in the form of structured online discussions that support learners in their development throughout all of the modules studied.

Such discussions are facilitated and monitored by lecturer who provides students with constructive feedback to help them improve and prepare for summative assignment and dissertation.

Formative assessment tasks will contribute to the student’s final mark to acknowledge their work and give chance to improve.

This method allows students to also contribute to the building of the community of practice by providing feedback to their peers as critical friends, enhancing the learner’s critical engagement throughout the study period.

b) Summative assessment is done via one assignment at the end of each module. The mode of assessment varies and may include in-class assignments and home-based written assignments.

For this module, the recommended assessment tool is using case studies and quizzes (MCQ) or programming exercises. Other assessment tools which may be used are proposals.

Word count range: 3000 ± 10% (except quizzes) For successful completion of a study module, the student is required to achieve a minimum of 41% pass mark in the summative assignment