Postgraduate Diploma in AI, Machine Learning and Data Science
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
A relevant bachelor’s degree or equivalent preparation in computing, mathematics, statistics, engineering or a related quantitative field is expected. You should be comfortable with programming, probability and basic linear algebra. BMIT assesses qualification equivalence, relevant experience and any preparatory study before admission.
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 11 subjects. The catalogue lists 15 subjects across all three routes. Specialisation subjects from the other two routes are optional enrichment, not additional compulsory subjects.
3 learning stages 15 subjects
1Stage 1: Shared foundations4 subjects
2Stage 2: Shared methods and specialisations8 subjects
3Stage 3: Applied research and capstone3 subjects
Subjects in this programme
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 11 subjects. The catalogue lists 15 subjects across all three routes. Specialisation subjects from the other two routes are optional enrichment, not additional compulsory subjects.
15 subjects are listed below with a short description of each. The Subject descriptions tab contains the learning outcomes, topics, practical tasks and assessments.
Stage 1: Shared foundations
- Probability and Statistical Modelling. 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. Compulsory for all specialisations
- Programming Fundamentals. 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. Compulsory for all specialisations
- Artificial Intelligence Foundations. 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. Compulsory for all specialisations
- Machine Learning. 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. Compulsory for all specialisations
Stage 2: Shared methods and specialisations
- Responsible AI and Data Governance. 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. Compulsory for all specialisations
- Data Preparation and Exploration. 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. Compulsory for all specialisations
- Knowledge Representation and Reasoning. 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. Artificial Intelligence specialisation: required if selected
- Natural Language Processing and Language Technologies. 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. Artificial Intelligence specialisation: required if selected
- Deep Learning. 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. Machine Learning specialisation: required if selected
- Machine Learning Deployment and Monitoring. 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. Machine Learning specialisation: required if selected
- Statistical Inference and Data Visualisation. Use statistical evidence and clear visual communication to answer applied questions. Distinguish association from causation and report uncertainty without overstating conclusions. Data Science specialisation: required if selected
- Data Engineering and Reproducible Analytics. Design documented data pipelines supporting trustworthy analysis. Manage data quality, provenance and access across collection, storage, transformation and delivery. Data Science specialisation: required if selected
Stage 3: Applied research and capstone
- Research Methods. 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. Compulsory for all specialisations
- Project Development and Feasibility. 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. Compulsory for all specialisations
- Integrated Project and Technical Report. 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. Compulsory for all specialisations
Assessment and practical learning
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.
Subject descriptions
Select a subject to read its learning outcomes, main topics, practical task and assessment.
Stage 1: Shared foundations
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.
Stage 2: Shared methods and specialisations
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.
Stage 3: Applied research and capstone
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.
Instructors
