Postgraduate Diploma in Artificial Intelligence
Build an applied postgraduate understanding of intelligent systems, predictive modelling and data-driven inquiry.
About the course
Build an applied postgraduate understanding of intelligent systems, predictive modelling and data-driven inquiry. The programme offers three specialisations: Artificial Intelligence, Machine Learning and Data Science. You will complete a shared core, study both subjects in one specialisation, and develop an evidence-based capstone project addressing a practical Southern African problem.
What you'll learn
- Compare AI, machine-learning and statistical approaches to a defined problem.
- Prepare reliable datasets and document data provenance and governance.
- Evaluate models using appropriate baselines, validation and error analysis.
- Apply the methods of your chosen specialisation to a Southern African use case.
- Present a reproducible capstone project with a critical account of its limitations.
Requirements
Entry requires a relevant bachelor’s degree or an assessed equivalent. English Language is required. BMIT assesses any professional registration, prior practice or supervised-placement requirement specific to this postgraduate diploma.
African Kalanga is a compulsory graduation subject in every year of this programme. BMIT confirms the approved entry route, any recognition of prior learning and access to required practical facilities before enrolment.
Course Content
Choose ONE specialisation: Artificial Intelligence, Machine Learning or Data Science. Complete the four shared foundation subjects in Stage 1, the two compulsory shared methods subjects and both subjects in your chosen specialisation in Stage 2, and all three applied research subjects in Stage 3. Each route comprises 21 subjects. The catalogue lists 21 subjects across all three routes. Specialisation subjects from the other two routes are optional enrichment, not additional compulsory subjects.
3 learning levels 21 subjects
Select a subject to read its learning outcomes, main topics, practical task and assessment.
Level 1: Shared foundations
Africa Indigenous Language: African Kalanga 101Compulsory graduation subject · Year 1
Develop African Kalanga through foundations for engineering, science and technology. Practise with authentic tasks, feedback and reflection across the programme year.
Learning outcomes
- Use African Kalanga accurately for Year 1 study and practice.
- Explain language choices to the intended audience.
- Revise your work using speaker and facilitator feedback.
Main topics
- Pronunciation
- Everyday vocabulary
- Listening
- Reading and respectful introductions
Practical task
Record a short introduction and explain its meaning to a peer.
Assessment
Submit the practical work, a brief reflection and evidence of language review. Assessment checks meaning, audience fit and improvement after feedback.
Entrepreneurship, Creativity and Innovation 101Compulsory core module · Level 1
Identify a real need, study the people affected and develop a responsible idea for a product, service or process. Apply critical analysis and relevant research to a specialist setting. Apply this learning to Postgraduate Diploma in Artificial Intelligence through an authentic brief and feedback from an appropriate audience.
Learning outcomes
- Investigate a defined opportunity and explain the evidence behind it.
- Develop and refine a creative response that fits the people and setting.
- Evaluate feasibility, ethical implications and the quality of the result.
Main topics
- Opportunity recognition
- Creative problem framing
- Community and user research
- Ethics and inclusion
- Idea generation and selection
Practical task
Investigate a need related to Postgraduate Diploma in Artificial Intelligence. Interview or observe intended users with consent, compare possible responses and present one justified concept.
Assessment
Submit an opportunity brief, research notes, a concept sketch and a reflection on feedback. Support your decisions with current research and a critical account of limitations. Assessment considers the quality of the evidence, the response to feedback, feasibility and your individual explanation.
African History and Pan Africanism 101Compulsory core module · Level 1
Study African history through oral, written and archaeological evidence. Examine the diversity of African societies, knowledge and exchange, then trace the emergence of Pan African ideas about identity, solidarity and self-determination.
Learning outcomes
- Place selected African societies and events within a supported historical timeline.
- Compare historical sources and identify perspective, evidence and uncertainty.
- Explain the foundations of Pan Africanism and the contributions of Africans and the diaspora.
Main topics
- Historical inquiry, chronology, oral traditions and source evaluation
- African civilisations, states, trade, livelihoods and knowledge systems
- Southern African history, including Mapungubgwe and Great Zimbabwe
- Slavery, the African diaspora and changing ideas about identity
- Origins of Pan Africanism, solidarity, self-determination and cultural expression
Practical task
Create an annotated timeline or digital learning poster on a selected African society or historical development. Compare at least two credible sources and explain one connection with your field of study.
Assessment
Submit the timeline or poster, an annotated source list and a short explanation of the evidence. Assessment considers chronology, historical accuracy, source judgement and the clarity of your interpretation.
Learning resources
Probability and Statistical ModellingCompulsory for all specialisations
Examine random variables, distributions and their relationship within probability and statistical modelling. The subject develops conditional probability and likelihood, then examines bayesian reasoning and model checking. Compare statistical models using a transparent teaching dataset.
Learning outcomes
- Explain random variables and distributions using an appropriate example.
- Analyse a subject-related problem involving conditional probability and likelihood.
- Present reasoned evidence addressing bayesian reasoning and model checking.
Main topics
- Random variables
- Distributions
- Conditional probability
- Likelihood
- Bayesian reasoning
- Model checking
Practical task
Compare statistical models using a transparent teaching dataset.
Assessment
Submit calculations, model checks and an interpretation.
Programming FundamentalsCompulsory for all specialisations
Develop your understanding of programming fundamentals through variables and control flow. The subject develops functions and data structures, then examines file handling and testing. Develop a small program which processes a defined teaching dataset.
Learning outcomes
- Explain variables and control flow using an appropriate example.
- Analyse a subject-related problem involving functions and data structures.
- Present reasoned evidence addressing file handling and testing.
Main topics
- Variables
- Control flow
- Functions
- Data structures
- File handling
- Testing
Practical task
Develop a small program which processes a defined teaching dataset.
Assessment
Submit working code, tests and a concise user explanation.
Artificial Intelligence FoundationsCompulsory for all specialisations
Study search and problem representation as foundations for artificial intelligence foundations. The subject develops heuristics and planning, then examines intelligent agents and evaluation. Implement a small problem-solving agent in a teaching simulation.
Learning outcomes
- Explain search and problem representation using an appropriate example.
- Analyse a subject-related problem involving heuristics and planning.
- Present reasoned evidence addressing intelligent agents and evaluation.
Main topics
- Search
- Problem representation
- Heuristics
- Planning
- Intelligent agents
- Evaluation
Practical task
Implement a small problem-solving agent in a teaching simulation.
Assessment
Submit a representation rationale and performance evaluation.
Machine LearningCompulsory for all specialisations
Examine learning tasks, features and their relationship within machine learning. The subject develops training and generalisation, then examines model selection and error analysis. Compare a baseline and candidate model on appropriately separated data.
Learning outcomes
- Explain learning tasks and features using an appropriate example.
- Analyse a subject-related problem involving training and generalisation.
- Present reasoned evidence addressing model selection and error analysis.
Main topics
- Learning tasks
- Features
- Training
- Generalisation
- Model selection
- Error analysis
Practical task
Compare a baseline and candidate model on appropriately separated data.
Assessment
Submit reproducible experiments and an error analysis.
Level 2: Shared methods and specialisations
Entrepreneurship, Creativity and Innovation 201Compulsory core module · Level 2
Turn a promising idea into a testable proposal using evidence, creative methods and basic resource planning. Apply critical analysis and relevant research to a specialist setting. Apply this learning to Postgraduate Diploma in Artificial Intelligence through an authentic brief and feedback from an appropriate audience.
Learning outcomes
- Investigate a defined opportunity and explain the evidence behind it.
- Develop and refine a creative response that fits the people and setting.
- Evaluate feasibility, ethical implications and the quality of the result.
Main topics
- Co-design and prototyping
- Value proposition
- Feasibility and costs
- Risk and responsible practice
- Testing and iteration
Practical task
Develop a small prototype or service model for a Postgraduate Diploma in Artificial Intelligence problem. Test it with relevant users, record feedback and revise the proposal.
Assessment
Submit a prototype, a feasibility note, user feedback and a record of revisions. Support your decisions with current research and a critical account of limitations. Assessment considers the quality of the evidence, the response to feedback, feasibility and your individual explanation.
African History and Pan Africanism 201Compulsory core module · Level 2
Examine colonial change, resistance and liberation across Africa, with attention to Zimbabwe and South Africa. Compare Pan African movements, organisations and debates, including contributions from women, workers and diaspora communities.
Learning outcomes
- Analyse the effects of colonial rule on land, labour, institutions and social relations.
- Compare liberation movements and Pan African arguments using contextual evidence.
- Explain how different groups shaped independence, solidarity and debates about freedom.
Main topics
- Colonial rule, land, labour, education and forms of resistance
- Nationalism, liberation and the end of colonial rule and apartheid
- Pan African congresses, organisations and contrasting interpretations
- Women, labour movements and diaspora participation
- The Organisation of African Unity and debates about unity, sovereignty and development
Practical task
Develop a comparative case study of a liberation issue or Pan African debate relevant to Southern Africa. Use a historical document alongside a scholarly account and explain differences in purpose and interpretation.
Assessment
Submit the case study, a source comparison and a reflection on your conclusions. Assessment considers context, evidence, recognition of different perspectives and the quality of your argument.
Learning resources
Responsible AI and Data GovernanceCompulsory for all specialisations
Examine consent, privacy and their relationship within responsible ai and data governance. The subject develops bias and explainability, then examines human oversight and deployment accountability. Audit a proposed AI use case for data and decision risks.
Learning outcomes
- Explain consent and privacy using an appropriate example.
- Analyse a subject-related problem involving bias and explainability.
- Present reasoned evidence addressing human oversight and deployment accountability.
Main topics
- Consent
- Privacy
- Bias
- Explainability
- Human oversight
- Deployment accountability
Practical task
Audit a proposed AI use case for data and decision risks.
Assessment
Submit a documented governance and evaluation plan.
Data Preparation and ExplorationCompulsory for all specialisations
Develop your understanding of data preparation and exploration through data provenance and cleaning. The subject develops missing values and transformation, then examines visual exploration and reproducibility. Prepare a documented dataset while retaining an audit of changes.
Learning outcomes
- Explain data provenance and cleaning using an appropriate example.
- Analyse a subject-related problem involving missing values and transformation.
- Present reasoned evidence addressing visual exploration and reproducibility.
Main topics
- Data provenance
- Cleaning
- Missing values
- Transformation
- Visual exploration
- Reproducibility
Practical task
Prepare a documented dataset while retaining an audit of changes.
Assessment
Submit the cleaned dataset, data dictionary and reproducible workflow.
Knowledge Representation and ReasoningArtificial Intelligence specialisation: required if selected
Study logic and rules as foundations for knowledge representation and reasoning. The subject develops ontologies and inference, then examines uncertainty and knowledge quality. Build a small knowledge model and test it against defined questions.
Learning outcomes
- Explain logic and rules using an appropriate example.
- Analyse a subject-related problem involving ontologies and inference.
- Present reasoned evidence addressing uncertainty and knowledge quality.
Main topics
- Logic
- Rules
- Ontologies
- Inference
- Uncertainty
- Knowledge quality
Practical task
Build a small knowledge model and test it against defined questions.
Assessment
Submit the model, test cases and limitations.
Natural Language Processing and Language TechnologiesArtificial Intelligence specialisation: required if selected
Build and critically evaluate language-processing systems, with attention to multilingual and low-resource Southern African settings. Compare practical baselines with pretrained language models and document their limitations.
Learning outcomes
- Design a reproducible text-processing pipeline.
- Compare systems using task-specific and language-sensitive evaluation.
- Explain the limitations and oversight needed for deployment.
Main topics
- Text preparation
- Embeddings and language models
- Retrieval and text classification
- Multilingual evaluation
- Hallucination and bias
- Human review
Practical task
Develop a small multilingual document-retrieval or text-classification prototype using public or permissioned text.
Assessment
Submit the prototype, evaluation report, data statement and an individual demonstration.
Deep LearningMachine Learning specialisation: required if selected
Develop your understanding of deep learning through neural representations and optimisation. The subject develops regularisation and model architectures, then examines training diagnostics and evaluation. Investigate a small model using an approved public dataset.
Learning outcomes
- Explain neural representations and optimisation using an appropriate example.
- Analyse a subject-related problem involving regularisation and model architectures.
- Present reasoned evidence addressing training diagnostics and evaluation.
Main topics
- Neural representations
- Optimisation
- Regularisation
- Model architectures
- Training diagnostics
- Evaluation
Practical task
Investigate a small model using an approved public dataset.
Assessment
Submit code, learning curves and a reasoned model comparison.
Machine Learning Deployment and MonitoringMachine Learning specialisation: required if selected
Translate an evaluated model into a maintainable prediction service. Examine the gap between experimental performance and reliable operation when data, users and resource constraints change.
Learning outcomes
- Package a model with reproducible dependencies and tests.
- Design monitoring for performance deterioration and data changes.
- Justify release and rollback criteria.
Main topics
- Model packaging
- Version control and experiment tracking
- Batch and online prediction
- Data and concept drift
- Performance monitoring
- Rollback and human oversight
Practical task
Deploy a small prediction service in a controlled learning environment and simulate a change in its input data.
Assessment
Submit the service, automated tests, monitoring results and an operational handover.
Statistical Inference and Data VisualisationData Science specialisation: required if selected
Use statistical evidence and clear visual communication to answer applied questions. Distinguish association from causation and report uncertainty without overstating conclusions.
Learning outcomes
- Select statistical methods suited to the study question and data.
- Check assumptions and explain uncertainty in the findings.
- Produce accessible visualisations supporting a defensible conclusion.
Main topics
- Sampling and estimation
- Confidence intervals
- Hypothesis testing
- Regression diagnostics
- Visual design and accessibility
- Communicating uncertainty
Practical task
Analyse an open Southern African social, environmental or economic dataset and prepare a visual evidence brief.
Assessment
Submit a reproducible analysis, annotated visual report and methods justification.
Data Engineering and Reproducible AnalyticsData Science specialisation: required if selected
Design documented data pipelines supporting trustworthy analysis. Manage data quality, provenance and access across collection, storage, transformation and delivery.
Learning outcomes
- Design a data model and repeatable transformation workflow.
- Implement validation and traceability checks.
- Document access controls and operating procedures.
Main topics
- Relational data and SQL
- Pipeline design
- Batch processing
- Data validation
- Provenance and versioning
- Access and retention controls
Practical task
Build a tested pipeline combining two public or synthetic datasets into an analytical dataset.
Assessment
Submit the pipeline, schema, validation evidence and operating documentation.
Level 3: Applied research and capstone
Entrepreneurship, Creativity and Innovation 301Compulsory core module · Level 3
Plan and evaluate the practical introduction of an innovation in a relevant organisation or community. Apply critical analysis and relevant research to a specialist setting. Apply this learning to Postgraduate Diploma in Artificial Intelligence through an authentic brief and feedback from an appropriate audience.
Learning outcomes
- Investigate a defined opportunity and explain the evidence behind it.
- Develop and refine a creative response that fits the people and setting.
- Evaluate feasibility, ethical implications and the quality of the result.
Main topics
- Implementation planning
- Stakeholder engagement
- Resources and finance
- Monitoring and impact
- Learning from a pilot
Practical task
Prepare and defend a pilot plan for an innovation relevant to Postgraduate Diploma in Artificial Intelligence. Set measures of success, identify risks and explain how results will guide improvement.
Assessment
Submit a pilot plan, budget assumptions, evaluation measures and an individual rationale. Support your decisions with current research and a critical account of limitations. Assessment considers the quality of the evidence, the response to feedback, feasibility and your individual explanation.
Research MethodsCompulsory for all specialisations
Study research questions and literature review as foundations for research methods. The subject develops study design and data collection, then examines ethics and interpretation. Prepare a feasible investigation proposal with a defined evidence need.
Learning outcomes
- Explain research questions and literature review using an appropriate example.
- Analyse a subject-related problem involving study design and data collection.
- Present reasoned evidence addressing ethics and interpretation.
Main topics
- Research questions
- Literature review
- Study design
- Data collection
- Ethics
- Interpretation
Practical task
Prepare a feasible investigation proposal with a defined evidence need.
Assessment
Submit a proposal, methods rationale and ethics considerations.
Project Development and FeasibilityCompulsory for all specialisations
Examine problem definition, evidence review and their relationship within project development and feasibility. The subject develops requirements and method selection, then examines feasibility and evaluation criteria. Develop an approved discipline-specific project proposal with a supervisor.
Learning outcomes
- Explain problem definition and evidence review using an appropriate example.
- Analyse a subject-related problem involving requirements and method selection.
- Present reasoned evidence addressing feasibility and evaluation criteria.
Main topics
- Problem definition
- Evidence review
- Requirements
- Method selection
- Feasibility
- Evaluation criteria
Practical task
Develop an approved discipline-specific project proposal with a supervisor.
Assessment
Submit a proposal, evidence review and evaluation plan.
Integrated Project and Technical ReportCompulsory for all specialisations
Develop, evaluate and document an applied project in your chosen specialisation. Artificial Intelligence projects focus on reasoning or language systems, Machine Learning projects on predictive models and deployment, and Data Science projects on statistical inquiry and data pipelines.
Learning outcomes
- Explain implementation and evidence collection using an appropriate example.
- Analyse a subject-related problem involving analysis and verification.
- Present reasoned evidence addressing limitations and communication.
Main topics
- Implementation
- Evidence collection
- Analysis
- Verification
- Limitations
- Communication
Practical task
Deliver a reproducible solution to the approved problem using authorised data, and document limitations, governance decisions and evaluation results.
Assessment
Submit the final project, report and individual oral defence.
Practical Learning
Authentic e-learning at BMIT
BMIT uses Authentic E-Learning to connect your studies with practical problems in your field. You use digital resources to investigate a realistic brief, make decisions and produce work for a defined purpose and audience. Your qualification's activities below put this approach into practice.
How you will learn
The approach draws on nine elements of authentic learning:
- Realistic contexts: Work with situations relevant to your profession or community.
- Purposeful tasks: Investigate a substantial problem with more than one possible solution.
- Expert examples: Examine demonstrations and examples of professional reasoning.
- Different perspectives: Compare sources and consider the needs of different people.
- Collaboration: Share ideas and review work with peers, while showing your own contribution.
- Reflection: Record what worked, what changed and what needs improvement.
- Explanation: Present your reasoning and support your decisions with evidence.
- Guidance: Use tutor feedback and structured support as you develop independence.
- Authentic assessment: Demonstrate learning through the task and its practical outcome.
From a brief to a practical outcome
Start by identifying the problem, intended audience and assessment criteria. Investigate relevant evidence, plan your response and develop an initial solution. Use discussion and feedback to test your ideas, then revise your work and explain your choices.
Digital tools support research, collaboration, simulation and the presentation of evidence. Your output depends on the module. Examples include a business proposal, technical design, tested program, investigation report or community resource. Keep drafts, calculations, test results and reflections to show how your work developed.
The module brief sets the required tasks, individual and group responsibilities, submission format and assessment criteria. The practical activities and supervision requirements for this qualification are set out below.
Research informing this approach
- Herrington, J. Authentic Learning: nine elements of authentic learning.
- Lombardi, M. M. (2007). Authentic learning for the 21st century: An overview. EDUCAUSE Learning Initiative.
- Parker, J., Maor, D. and Herrington, J. (2013). Authentic online learning: Aligning learner needs, pedagogy and technology. Issues in Educational Research, 23(2), 227–241.
Use guided online cases and data tasks to prepare for practical work. Laboratory, field or equipment tasks require suitable facilities and supervision.
What you will practise
- Artificial Intelligence Foundations: Implement a small problem-solving agent in a teaching simulation.
- Deep Learning: Investigate a small model using an approved public dataset.
- Project Development and Feasibility: Develop an approved discipline-specific project proposal with a supervisor.
Projects, evidence and safeguards
Assessment combines reproducible programming and analytical tasks, critical reports, subject assessments and an individual capstone demonstration. Complete the shared subjects and both subjects in your selected specialisation. The capstone must state its data permissions, validation methods, limitations and arrangements for human oversight. Practical work uses public, synthetic or explicitly authorised datasets.
African History and Pan Africanism practical learning
- African History and Pan Africanism 101: Create an annotated timeline or digital learning poster on a selected African society or historical development. Compare at least two credible sources and explain one connection with your field of study.
- African History and Pan Africanism 201: Develop a comparative case study of a liberation issue or Pan African debate relevant to Southern Africa. Use a historical document alongside a scholarly account and explain differences in purpose and interpretation.
Career Prospects
These examples show work across this qualification pathway, from support roles to specialist and management posts. Your entry point depends on your study, experience and employer requirements. Regulated roles require separate recognition or registration.
Top Career Paths and Job Titles
- Data assistant: Cleans datasets and documents how information was collected and prepared.
- Machine learning developer: Builds and tests models against defined business or research questions.
- AI and data science specialist: Evaluates model performance, risks and deployment decisions with stakeholders.
- Data engineer: Builds reliable data pipelines and checks the quality of information used by models.
- AI product lead: Directs teams that test, deploy and monitor AI systems against real needs and risks.
Growth and Leadership Trajectory
- Starting practice: After meeting entry and any registration requirements, begin with the work represented by Data assistant. Build supervised experience and keep a record of reliable work.
- Specialist progression: Relevant study and evidence from more complex projects support a move towards Machine learning developer or AI and data science specialist.
- Leadership: With substantial experience, a role such as AI product lead adds responsibility for people, quality, budgets or service decisions.
Further Education to Boost Prospects
- Next study step: A relevant master’s degree in artificial intelligence and data science is a possible next step. Research preparation or bridging study may be required.
- Continue at BMIT: Master of Science in AI and Machine Learning Admission and any credit transfer are assessed separately.
Professional Certifications and Registrations
- Role-specific requirements: No single statutory credential covers every role in artificial intelligence and data science. Employers may require a relevant short course, supervised experience or role-specific licence. Check the requirements for the job and country before applying.
Potential Government and Industrial Employers
- Entrepreneurship and Small Business Management: Start or manage a small venture related to this field. Regulated work still requires the relevant professional registration or licence.
- Private and commercial organisations: Software and analytics companies.
- Specialist employers: Banks, telecoms and digital service providers.
- Research and community organisations: Research laboratories and technology consultancies.
- Government departments and public bodies: Government data, statistics and digital-service units.