Probabilistic Modelling & Reasoning and Big Data Analytics
6 Credits (ECTS)
Probabilistic Modelling & Reasoning and Big Data Analytics
Get 70% back via Tax Credit
€683 refund on this module
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.
Career Paths
The programme prepares for positions such as and not limited to:
- Compliance Supervisor/Manager/Officers
- Anti-Money Laundering Managers
- Money Laundering Reporting Officer and their deputies
- Law enforcement agencies and supervisory and regulatory authorities
- Managers with responsibilities for internal AML controls
- Risk Managers
- Director of Compliance
How you’ll be assessed
The course comprises:
- Evening classes for part-time courses.
- Classes held throughout the day for full-time courses.
- Guided learning, presentations, comprising synchronous online discussions, tutorials and/or videos.
- Self-study hours comprising research, reading and assignment work.
Assessment
Assessment is carried out via two mandatory components:
- Modular Assessment
- Summative Assessment
The programme includes different forms of assessment which allow for and promote students’ critical engagement. The formative and summative assessment tasks may include an in-class assignment and/or a home-based written assignment using diverse assessment tools which may take the form of online and in-class discussions, examinations, case studies, reports, proposals, essays, and presentations, etc., as applicable to the diverse modules.
Additional Info
Upon successful completion of this course, students will be eligible for a 70% refund of the cost through the ‘Get Qualified’ scheme.**
Due to the modular structure of the course, you may also opt to take individual modules as stand-alone. The entry requirements still apply.***
*Prices are applicable to students who reside in Malta at the time of applying.
**Terms and conditions apply.
Learning Outcomes
Competences:
At the end of the module/unit the learner will have acquired the responsibility and autonomy to:
- Exhaustively evaluate the need for probabilistic modelling & reasoning and the representation of the Bayesian network
- Propose Big Data Technology for business intelligence.
- Judiciously examine Exact and Approximate inference in Bayesian networks with maximum likelihood and its structure learning.
- Understand and apply temporal models such as hidden Markov models and dynamic Bayesian networks.
- Rigorously assess Causality, Utilities & decisions.
- Evalaute fundamentals of Big Data Analytics and evaluate Hadoop Ecosystem, HDFS, Map- Reduce Programming.
Knowledge:
At he end of the module/unit the learner will have been exposed to the following:
- Recall and relate to Probability Theory and its connection to AI.
- Classify the elements of Exact and Approximate inference in Bayesian networks and Learning.
- Identify and discuss Temporal Models.
- Explore the concepts of business intelligence and Big Data Analytics.
Key indicative topic areas cover
- Probability Theory
- Bayesian Network representation
- Big Data Analytics
Skills:
At the end of the module/unit the learner will have acquired the following:
- Judiciously apply Probabilistic Modeling & Reasoning.
- Thoroughly understand and apply the representation of Bayesian network, exact and approximate inference in Bayesian networks and learning Bayesian networks with maximum likelihood and its structure learning.
- Evaluate temporal models such as hidden Markov models and dynamic Bayesian networks.
- Appropriately apply Big Data Technology for Business Intelligence (BI), BI Framework and components, BI Project Life Cycle.
Judgment Skills and Critical Abilities:
The learner will be able to:
- Collect, analyse and interpret data/information to support arguments, and to develop and apply ideas.
Module-Specific Communication Skills:
The learner will be able to:
- Communicates ideas, problems, and solutions to both specialist and non-specialist audiences using a range of techniques involving qualitative and quantitative information to sustain arguments.
Module-Specific Learner Skills:
The learner will be able to:
- Undertake independent and self-directed study through primary and secondary research.
Module-Specific Digital Skills and Competences:
The learner will be able to:
- Navigate through the online learning platform to find assignments, discussion boards, literature, tutorials etc.