Introduction to Artificial Intelligence and Data Science
6 Credits (ECTS)
Introduction to Artificial Intelligence and Data Science
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 what AI is, its importance and impact on the industry, business and society, and AI applications in various sectors including finance, healthcare, education, and government.
Learners will also learn various components of AI, including knowledge representation, reasoning, decision making and problem-solving.
Learners will also get an exposure of some of the AI tools such as Rapid miner, Weka and R.
The second part of the Module will equip learners with the data science background required for AI and its applications. It will include data exploration, preparation, representation, transformation, modelling, visualisation and presentation.
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:
- Evaluate the need for AI technologies and its role and impact in an industry or business.
- Propose how AI technologies can be best exploited for an industry or business.
- Rigorously analyze the requirements for AI transition/ implementation in an industry or business.
- Assess potential challenges and risks AI can bring in the future, especially to remain competitive in an industry or business sector.
- Research-based diagnose data-oriented problems and data-driven decisions in an industry or business in terms of data exploration and visualisation.
Knowledge:
At the end of the module/unit the learner will have been exposed to the following:
- Discuss AI definitions, capabilities, challanges and AI impact on the industry, business and society.
- Categorise and explain broad range applications of AI.
- Describe Data Science as a discipline including its foundations, Data Growth-issues and challenges and data science process and application.
- Compare and contrast 4 levels of data, forms of data and modern databases.
Introduction to Artificial Intelligence
- Definitions of AI
- Problem-solving
- Importance and impact of AI on the industry, business and society
- AI Capabilities
- Industrialising AI
- Future of AI
- AI Challenge
Knowledge, Reasoning, Planning and Learning, Communicating, Perceiving and Acting
- Classical Planning
- Knowledge Representation and Reasoning
- Learning from examples
- Knowledge in learning
- Communication
- Perception
AI Applications
- Speech Recognition
- Natural Language Processing
- Natural Language understanding
- Dimensionality reduction
- Virtual Personal Assistants/ Chatbots
AI Applications Cont.
- Image Recognition
- Computer Vision
- Objects Recognition
- Describing images
- Feature Selection
- Feature Extraction
- Business Applications of AI
- Health Care Applications of AI
- Education Applications of AI
- Finance Applications of AI
- Robotics
- Autonomous Transportation
- Gaming
Introduction to Data Science
- Data Science-a discipline
- Landscape-Data to Data science
- Data Growth-issues and challenges
- Data science process
- Foundations of data science
Data Exploration, Preparation, Representation and Transformation
- Structured vs unstructured data
- Quantitative vs qualitative data.
- Four levels of data – nominal, ordinal, interval, ration. Messy data, Anomalies and artifacts in datasets. Cleaning data.
- Forms of data-tabular, text data, graph-based data.
- Modern databases- text files, spreadsheets, SQL databases, NoSQL databases, distributed databases, live data streams.
Data Science Applications
- In Business
- In Insurance
- In Energy
- In Health care
- In Biotechnology
- In Manufacturing
- In Utilities
- In Telecommunication
- In Travel
- In Governance
- In Gaming
- In Pharmaceuticals
- In Geospatial analytics and modeling.
Skills:
At the end of the module/unit the learner will have acquired the following skills:
- Carry out an engaging “data-story” to communicate the problem and the inference.
- Present a case on the need for AI in their business of Industry domain.
- Thoroughly understand and apply AI-based planning and decision making.
- Judiciously apply data science in the relevant Industry domain.
- Rigorously analyze AI applications and their impact on business, industry and society.
Judgment Skills and Critical Abilities:
The learner will be able to:
- Master problem- solving skills by analysing case studies and working out the issues presented in real life situations.
- Evaluate and critically appropriate to discipline being studied.
- 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:
- 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.