Postgraduate Diploma in Data Science and Analytics
Develop the analytical and computing skills needed to turn a defined data question into a defensible result.
About the course
Develop the analytical and computing skills needed to turn a defined data question into a defensible result. The proposed diploma uses Southern African organisational and public-interest cases. You will examine how data were produced, choose appropriate methods and communicate findings in forms suited to the intended decision.
What you'll learn
Assess dataset quality, permissions and suitability for an analytical question.
Use programming and statistical methods to conduct reproducible analysis.
Evaluate predictive or descriptive results with appropriate uncertainty.
Present findings and recommendations without overstating the evidence.
Requirements
Recommended preparation is a relevant recognised undergraduate qualification and readiness for academic reading, applied analysis and independent study. A masters route also requires adequate research preparation and an appropriate supervisor. BMIT must confirm admission equivalence, bridging requirements and its approved research and assessment regulations.
Course content
The subjects are grouped by learning stage, from foundations to specialist study and supervised application. Complete earlier foundations before the related advanced work. Clinical, laboratory, field and workplace subjects use approved facilities and supervision appropriate to the programme.
3 learning stages 11 subjects
1Stage 1: Postgraduate disciplinary study4 subjects
2Stage 2: Specialist analysis and application4 subjects
3Stage 3: Supervised research or applied integration3 subjects
Subjects in this programme
The subjects are grouped by learning stage, from foundations to specialist study and supervised application. Complete earlier foundations before the related advanced work. Clinical, laboratory, field and workplace subjects use approved facilities and supervision appropriate to the programme.
11 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: Postgraduate disciplinary study
- Applied Statistics. Examine descriptive measures, probability and their relationship within applied statistics. The subject develops sampling and estimation, then examines hypothesis testing and regression. Analyse an approved dataset and distinguish uncertainty from systematic bias.
- 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.
- Database Systems. Study data models and relational design as foundations for database systems. The subject develops queries and transactions, then examines data integrity and access control. Build a database for synthetic organisational records.
- Data Preparation and Exploration. Examine data provenance, cleaning and their relationship within data preparation and exploration. The subject develops missing values and transformation, then examines visual exploration and reproducibility. Prepare a documented dataset while retaining an audit of changes.
Stage 2: Specialist analysis and application
- Applied Data Analytics. Develop your understanding of applied data analytics through business questions and indicators. The subject develops descriptive analysis and predictive analysis, then examines validation and decision limits. Analyse a defined organisational question using authorised data.
- Machine Learning. Study learning tasks and features as foundations for 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.
- Data Visualisation and Communication. Examine visual encoding, chart selection and their relationship within data visualisation and communication. The subject develops accessibility and uncertainty, then examines dashboard design and narrative accuracy. Create visualisations for a defined analytical audience.
- Data Engineering. Develop your understanding of data engineering through data pipelines and storage design. The subject develops transformation and batch processing, then examines data validation and pipeline monitoring. Build a reproducible pipeline using synthetic or permissioned data.
Stage 3: Supervised research or applied integration
- 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.
- 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.
- Integrated Project and Technical Report. Develop your understanding of integrated project and technical report through implementation and evidence collection. The subject develops analysis and verification, then examines limitations and communication. Complete the approved project and maintain an auditable evidence record.
Assessment and practical learning
- Proposed assessment: data-quality audits, statistical exercises and reproducible programming assignments.
- A staged research project containing a question, evidence review, documented dataset and analysis plan.
- An individual analytical report and presentation, with clear uncertainty and ethical reflection. Weighting and progression require BMIT approval.
Practical application
Practical work requires suitable computing tools and authorised data. Learners should retain data dictionaries, processing decisions and reproducible analysis scripts. Reports must distinguish description, prediction and causal inference. Organisational projects require explicit agreement on confidentiality, access and how results will be shared.
Subject descriptions
Select a subject to read its learning outcomes, main topics, practical task and assessment.
Stage 1: Postgraduate disciplinary study
Applied Statistics
Examine descriptive measures, probability and their relationship within applied statistics. The subject develops sampling and estimation, then examines hypothesis testing and regression. Analyse an approved dataset and distinguish uncertainty from systematic bias.
Learning outcomes
- Explain descriptive measures and probability using an appropriate example.
- Analyse a subject-related problem involving sampling and estimation.
- Present reasoned evidence addressing hypothesis testing and regression.
Main topics
- Descriptive measures
- Probability
- Sampling
- Estimation
- Hypothesis testing
- Regression
Practical task
Analyse an approved dataset and distinguish uncertainty from systematic bias.
Assessment
Submit a reproducible analysis with justified methods and interpretation.
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.
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.
Database Systems
Study data models and relational design as foundations for database systems. The subject develops queries and transactions, then examines data integrity and access control. Build a database for synthetic organisational records.
Learning outcomes
- Explain data models and relational design using an appropriate example.
- Analyse a subject-related problem involving queries and transactions.
- Present reasoned evidence addressing data integrity and access control.
Main topics
- Data models
- Relational design
- Queries
- Transactions
- Data integrity
- Access control
Practical task
Build a database for synthetic organisational records.
Assessment
Submit the schema, queries, test data and integrity checks.
Data Preparation and Exploration
Examine data provenance, cleaning and their relationship within data preparation and exploration. 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.
Stage 2: Specialist analysis and application
Applied Data Analytics
Develop your understanding of applied data analytics through business questions and indicators. The subject develops descriptive analysis and predictive analysis, then examines validation and decision limits. Analyse a defined organisational question using authorised data.
Learning outcomes
- Explain business questions and indicators using an appropriate example.
- Analyse a subject-related problem involving descriptive analysis and predictive analysis.
- Present reasoned evidence addressing validation and decision limits.
Main topics
- Business questions
- Indicators
- Descriptive analysis
- Predictive analysis
- Validation
- Decision limits
Practical task
Analyse a defined organisational question using authorised data.
Assessment
Submit an analytical report with recommendations and limitations.
Machine Learning
Study learning tasks and features as foundations for 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.
Data Visualisation and Communication
Examine visual encoding, chart selection and their relationship within data visualisation and communication. The subject develops accessibility and uncertainty, then examines dashboard design and narrative accuracy. Create visualisations for a defined analytical audience.
Learning outcomes
- Explain visual encoding and chart selection using an appropriate example.
- Analyse a subject-related problem involving accessibility and uncertainty.
- Present reasoned evidence addressing dashboard design and narrative accuracy.
Main topics
- Visual encoding
- Chart selection
- Accessibility
- Uncertainty
- Dashboard design
- Narrative accuracy
Practical task
Create visualisations for a defined analytical audience.
Assessment
Submit a visual report and explain how misleading interpretations were avoided.
Data Engineering
Develop your understanding of data engineering through data pipelines and storage design. The subject develops transformation and batch processing, then examines data validation and pipeline monitoring. Build a reproducible pipeline using synthetic or permissioned data.
Learning outcomes
- Explain data pipelines and storage design using an appropriate example.
- Analyse a subject-related problem involving transformation and batch processing.
- Present reasoned evidence addressing data validation and pipeline monitoring.
Main topics
- Data pipelines
- Storage design
- Transformation
- Batch processing
- Data validation
- Pipeline monitoring
Practical task
Build a reproducible pipeline using synthetic or permissioned data.
Assessment
Submit the pipeline, validation checks and operating notes.
Stage 3: Supervised research or applied integration
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.
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 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.
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 Report
Develop your understanding of integrated project and technical report through implementation and evidence collection. The subject develops analysis and verification, then examines limitations and communication. Complete the approved project and maintain an auditable evidence record.
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
Complete the approved project and maintain an auditable evidence record.
Assessment
Submit the final project, report and individual oral defence.
Instructors
