Short Courses
Advance Your Skills. Strengthen Your Expertise. Transform Your Career.
The Department of Statistics and Actuarial Science at the Kwame Nkrumah University of Science and Technology (KNUST) seeks to advance professional excellence through practical, industry-relevant education and training. Its proposed short-course portfolio supports professionals, researchers, graduates, policymakers and organisations seeking to strengthen skills in data analytics, statistics, actuarial science, insurance, financial risk management and quantitative decision-making. Through practical software laboratories, guided case studies and applied projects, participants can translate quantitative ideas into sound, evidence-based decisions.
Our learning approach combines hands-on exercises, real-world datasets, guided case studies and applied projects. The portfolio welcomes working professionals and organisations as well as suitably prepared graduates and researchers.
Programme formats
Executive certificates (2–6 weeks): Focused short courses with applied learning activities; any formal certificate requirements should be confirmed by the Department.
Weekend and modular professional training: Flexible sessions that may be arranged around professional schedules.
Customised corporate training: Organisation-specific workshops for financial institutions, insurers, the National Insurance Commission and other public or private bodies.
Detailed course descriptions
Data, Analytics, and Computing
Data and digital skills
DA01 | Data Analytics and Visualization with R, Python, and Power BI
Proposed duration: 4 weeks • 24 contact hours • Level: Foundation–Intermediate
Who should attend: Analysts, researchers, administrators and monitoring teams.
Build reproducible workflows for cleaning, analysing and presenting data through statistical graphics and interactive dashboards.
Core topics: Data preparation and exploratory analysis; Statistical summaries and effective visual design; Interactive dashboards and reporting; Communicating findings to decision-makers.
Expected learning outcomes: Prepare and explore real-world datasets; Develop and explain an analytical dashboard.
Practical output: A dashboard and concise decision brief. Tools/methods: R, Python, Power BI.
DA02 | Python Programming for Data Science (Beginner to Advanced)
Proposed duration: 6 weeks • 36 contact hours • Level: Foundation–Advanced pathway
Who should attend: New programmers, graduates and aspiring data scientists.
Progress from Python fundamentals to data manipulation, automation and introductory predictive modelling.
Core topics: Python syntax, functions and data structures; NumPy, pandas and data quality; Visualization, reusable scripts and notebooks; Statistical learning and end-to-end projects.
Expected learning outcomes: Write reusable Python programs; Deliver a documented analytical workflow.
Practical output: A reproducible notebook and small data-science project. Tools/methods: Python, pandas, NumPy, matplotlib, scikit-learn.
DA03 | Big Data Analytics and Business Intelligence
Proposed duration: 4 weeks • 24 contact hours • Level: Intermediate
Who should attend: BI teams, business analysts and operations managers.
Connect operational data to measurable business performance using structured data workflows and business intelligence techniques.
Core topics: Data warehouses, ETL concepts and SQL; Data quality, governance and scalable analytics; KPI design and business metrics; BI dashboards and storytelling.
Expected learning outcomes: Transform multi-source operational data; Interpret and communicate key business indicators.
Practical output: A business-performance dashboard. Tools/methods: SQL, Power BI, Python.
DA04 | Big Data Analytics and Health Intelligence
Proposed duration: 4 weeks • 24 contact hours • Level: Intermediate
Who should attend: Health analysts, public-health officers and health researchers.
Apply modern analytics to health-service and population-health data while respecting confidentiality and governance requirements.
Core topics: Health data structures and indicator definitions; Data cleaning, linkage and quality checks; Trends, geographic patterns and surveillance reporting; Ethical governance and actionable health dashboards.
Expected learning outcomes: Analyse health indicators responsibly; Present interpretable insights for service planning.
Practical output: A simulated health-system monitoring dashboard. Tools/methods: R, Python, Power BI.
DA05 | Time Series Analysis and Forecasting
Proposed duration: 4 weeks • 24 contact hours • Level: Intermediate
Who should attend: Economists, finance teams, planners and forecasting analysts.
Model time-dependent patterns and produce well-evaluated forecasts for planning, operations and policy.
Core topics: Trend, seasonality and stationarity; ARIMA and exponential smoothing; Forecast evaluation and prediction intervals; Communicating forecast assumptions and uncertainty.
Expected learning outcomes: Select suitable forecasting models; Generate and evaluate out-of-sample forecasts.
Practical output: A forecast report with uncertainty intervals. Tools/methods: R or Python.
DA06 | Machine Learning for Applied Statistics
Proposed duration: 6 weeks • 36 contact hours • Level: Intermediate–Advanced
Who should attend: Statisticians, analysts and quantitatively trained professionals.
Develop predictive models using sound statistical principles, transparent validation and careful interpretation.
Core topics: Supervised learning and model baselines; Feature engineering and leakage prevention; Cross-validation and performance metrics; Interpretability, fairness and deployment considerations.
Expected learning outcomes: Compare predictive approaches rigorously; Explain performance and limitations to stakeholders.
Practical output: A model-comparison report and reproducible code. Tools/methods: R or Python, scikit-learn.
Actuarial Science and Insurance
Insurance and risk
AS01 | Actuarial Modelling and Risk Analysis
Proposed duration: 6 weeks • 36 contact hours • Level: Intermediate–Advanced
Who should attend: Actuarial trainees, insurance analysts and risk officers.
Build practical models for contingent events, losses and uncertainty in insurance and financial settings.
Core topics: Probability models and loss distributions; Frequency–severity modelling; Simulation and aggregate-risk models; Scenario and sensitivity analysis.
Expected learning outcomes: Estimate and compare risk measures; Quantify uncertainty with simulations.
Practical output: An actuarial risk model with scenario analysis. Tools/methods: R, Excel.
AS02 | Financial Risk Management and Insurance Analytics
Proposed duration: 4 weeks • 24 contact hours • Level: Intermediate
Who should attend: Risk managers, insurance analysts and financial-sector staff.
Measure portfolio and insurance exposure, assess downside risk and communicate key findings for risk oversight.
Core topics: Risk identification and data preparation; Value at Risk and Expected Shortfall concepts; Stress testing and scenario analysis; Claims, portfolio and dashboard analytics.
Expected learning outcomes: Summarise and compare risk exposures; Build an evidence-based risk dashboard.
Practical output: A risk-monitoring dashboard and short memo. Tools/methods: R, Excel, Power BI.
AS03 | Pricing and Valuation of Insurance Products
Proposed duration: 6 weeks • 36 contact hours • Level: Intermediate–Advanced
Who should attend: Insurance pricing teams, actuaries and underwriting analysts.
Explore quantitative pricing and valuation approaches using claims experience, assumptions and sensitivity assessment.
Core topics: Insurance product structure and pricing data; Claims frequency and severity models; Pure premium, loading and valuation principles; Model checking, sensitivities and communication.
Expected learning outcomes: Construct transparent pricing calculations; Evaluate key assumption-driven changes.
Practical output: An illustrative pricing model and technical note. Tools/methods: R, Excel.
AS04 | Microinsurance and Inclusive Insurance
Proposed duration: 3 weeks • 18 contact hours • Level: Foundation–Intermediate
Who should attend: Insurers, development practitioners, regulators and NGOs.
Design and assess affordable insurance products for underserved markets while considering financial sustainability and consumer needs.
Core topics: Inclusive insurance markets and product needs; Affordability, distribution and enrolment; Simplified risk and premium calculations; Consumer protection and performance measurement.
Expected learning outcomes: Assess an inclusive insurance concept; Develop measurable product success indicators.
Practical output: A microinsurance product concept note. Tools/methods: Excel, case-based tools.
AS05 | Digital Insurance and InsurTech Applications
Proposed duration: 3 weeks • 18 contact hours • Level: Foundation–Intermediate
Who should attend: Insurance managers, digital transformation teams and fintech professionals.
Evaluate how data and digital technologies can improve insurance distribution, underwriting and customer experience.
Core topics: Digital insurance business models; Analytics in underwriting and claims; Automation, customer journeys and dashboards; Privacy, fairness, cybersecurity and implementation.
Expected learning outcomes: Map a digital insurance use case; Assess implementation risks and opportunities.
Practical output: An InsurTech solution blueprint. Tools/methods: Excel, Power BI, illustrative workflows.
Statistics and Research Methods
Research and evaluation
SR01 | Statistical Methods for Research and Decision-Making
Proposed duration: 4 weeks • 24 contact hours • Level: Foundation–Intermediate
Who should attend: Postgraduate students, research officers and programme managers.
Strengthen statistical reasoning from research questions and data summaries through inference and evidence-based recommendations.
Core topics: Research questions, data structures and study designs; Descriptive analysis and uncertainty; Hypothesis testing, confidence intervals and regression; Interpreting and reporting findings.
Expected learning outcomes: Choose suitable statistical procedures; Write clear, defensible findings.
Practical output: A reproducible research-analysis report. Tools/methods: R, SPSS or Stata.
SR02 | Survey Design, Sampling, and Data Collection Techniques
Proposed duration: 4 weeks • 24 contact hours • Level: Foundation–Intermediate
Who should attend: Research institutions, field supervisors, NGOs and government agencies.
Plan sound surveys from target populations and sampling designs to electronic field collection and data quality review.
Core topics: Questionnaire design and survey errors; Probability sampling and sample-size planning; Digital data-collection workflows; Nonresponse, weighting and quality assurance.
Expected learning outcomes: Prepare a defensible sampling plan; Develop a tested digital questionnaire.
Practical output: A survey protocol and digital questionnaire. Tools/methods: KoboToolbox or ODK, R, Excel.
SR03 | Impact Evaluation and Policy Analysis
Proposed duration: 6 weeks • 36 contact hours • Level: Intermediate
Who should attend: Policy analysts, M&E specialists, researchers and programme officers.
Apply credible causal reasoning and evaluation designs to estimate and communicate programme effects.
Core topics: Theory of change and evaluation questions; Experimental and quasi-experimental designs; Difference-in-differences and matching concepts; Validity, sensitivity and policy communication.
Expected learning outcomes: Choose an appropriate evaluation strategy; Critically interpret estimated programme effects.
Practical output: An impact-evaluation design and analysis brief. Tools/methods: R or Stata.
SR04 | Experimental Design and Data Analysis
Proposed duration: 4 weeks • 24 contact hours • Level: Intermediate
Who should attend: Researchers, laboratory scientists and quality-improvement staff.
Design efficient comparative studies and analyse experimental data with suitable models and diagnostics.
Core topics: Randomisation, replication and blocking; Completely randomised and block designs; Factorial designs and interactions; ANOVA, diagnostics and multiple comparisons.
Expected learning outcomes: Plan a defensible experiment; Analyse and interpret treatment effects.
Practical output: An experimental protocol and statistical report. Tools/methods: R, SPSS or Stata.
Emerging and Cross-cutting Skills
New and cross-sector skills
EC01 | Cybersecurity and Data Protection in Financial Systems
Proposed duration: 3 weeks • 18 contact hours • Level: Foundation–Intermediate
Who should attend: Financial-services staff, analysts and data stewards.
Understand practical controls for protecting sensitive financial data and managing information-security risks.
Core topics: Threats to financial data and systems; Access controls and secure data handling; Incident response and risk assessment; Privacy, organisational policies and compliance awareness.
Expected learning outcomes: Identify priority information-security risks; Propose sensible controls for a financial-data workflow.
Practical output: A data-protection risk assessment. Tools/methods: Case studies, spreadsheet risk register.
EC02 | Cloud Computing for Data Analytics
Proposed duration: 4 weeks • 24 contact hours • Level: Intermediate
Who should attend: Analysts, IT staff and data managers.
Understand scalable cloud-based analytical workflows, from governed storage to processing, reporting and cost oversight.
Core topics: Cloud service concepts and architectures; Secure data storage and permissions; Cloud analytics pipelines and notebooks; Cost, reliability and governance.
Expected learning outcomes: Sketch a governed cloud analytics workflow; Identify security and cost trade-offs.
Practical output: A cloud analytics architecture plan. Tools/methods: Vendor-neutral cloud demonstrations, SQL, Python.
EC03 | Quantitative Finance and Financial Engineering
Proposed duration: 6 weeks • 36 contact hours • Level: Advanced
Who should attend: Finance analysts, actuarial professionals and quantitative graduates.
Use mathematical and computational methods to study financial valuation, portfolios and risk under uncertainty.
Core topics: Time value of money and financial returns; Portfolio concepts and optimisation; Derivative valuation fundamentals; Monte Carlo methods and risk scenarios.
Expected learning outcomes: Model representative financial decisions; Explain model assumptions and risk exposure.
Practical output: A quantitative finance modelling report. Tools/methods: R, Python, Excel.
EC04 | Decision Science and Analytics for Managers
Proposed duration: 3 weeks • 18 contact hours • Level: Foundation–Intermediate
Who should attend: Executives, administrators, project leaders and managers.
Use structured quantitative frameworks to improve decisions involving uncertainty, constrained resources and competing objectives.
Core topics: Decision framing and business metrics; Decision trees and scenario analysis; Optimisation and resource allocation basics; Multi-criteria decisions and communication.
Expected learning outcomes: Compare alternatives transparently; Translate analytical findings into action.
Practical output: A management decision brief. Tools/methods: Excel, Power BI.
Recommended call to action
“Interested in a course or tailored professional training for your organisation? Contact the Department of Statistics and Actuarial Science for the next available intake, entry guidance and corporate training enquiries.”