Course overview
The Big Data Analytics for Healthcare & Population Health Training Workshop equips healthcare professionals, public health practitioners, researchers and data analysts with practical skills to manage, analyse and interpret large and complex healthcare datasets. The training covers healthcare data integration, big data architecture, clinical and population health analytics, electronic health records, health information systems, predictive modelling, data visualisation and AI-powered analytics. Participants explore tools and technologies such as Python, SQL, Power BI and cloud-based data platforms to identify disease patterns, forecast healthcare demand, assess population health risks and improve resource allocation. The course emphasises data quality, privacy, governance and responsible AI to support evidence-based clinical operations, public health planning, health systems performance and population health management.
Participants explore how data from electronic health records, hospital management systems, laboratory information systems, disease surveillance platforms, health insurance claims, demographic surveys, wearable devices and public health databases can be integrated and transformed into actionable insights. The programme introduces Python, SQL, Power BI, cloud data platforms and scalable data processing concepts, with practical applications in hospital performance, healthcare resource utilisation, disease burden analysis, population risk stratification and healthcare demand forecasting.
Course objectives
- Explain big data concepts, architectures and technologies used in healthcare and population health analytics.
- Translate complex analytical findings into evidence-based recommendations for healthcare managers, public health agencies, researchers and policymakers.
- Evaluate healthcare data privacy, security, interoperability, bias and responsible AI considerations when developing analytical solutions.
- Apply statistical analysis, machine learning and AI techniques to identify health trends, assess population risks and forecast healthcare service demand.
- Develop interactive dashboards and analytical reports using Power BI to monitor healthcare performance, service utilisation and population health indicators.
- Use Python and SQL to process, query, analyse and summarise healthcare and population health data.
- Collect, integrate, clean and transform large healthcare datasets from multiple sources while applying appropriate data quality and governance practices.
Who should attend
Healthcare data analysts, public health professionals, epidemiologists, health informatics specialists, hospital administrators, healthcare managers, medical researchers, biostatisticians, health information management officers, clinical research professionals, population health managers, health systems planners, monitoring and evaluation specialists, health programme managers, health policy analysts, disease surveillance officers, healthcare IT professionals, data scientists entering the healthcare sector, digital health specialists, health insurance analytics professionals, NGO and humanitarian health programme staff, professionals working in government health agencies and international health organisations.
Course content
- Module 1: Introduction to Big Data Analytics in Healthcare Definition and characteristics of big data, the five Vs of healthcare big data (volume, velocity, variety, veracity and value), evolution of healthcare analytics, traditional data analysis versus
- Module 2: Healthcare Data Sources and Information Systems Electronic health records and electronic medical records, hospital management information systems, laboratory information management systems, pharmacy and prescription records, radiology and medica
- Module 3: Big Data Architecture and Healthcare Data Engineering Components of a big data architecture, data ingestion and extraction, batch versus stream processing, data pipelines and workflow orchestration concepts, relational and non-relational databas
- Module 4: Healthcare Data Cleaning, Integration and Quality Management Data profiling and exploratory assessment, identifying missing and duplicate records, resolving inconsistent coding and formats, standardising dates and units, validating demographic a
- Module 5: SQL and Database Management for Healthcare Analytics Relational database principles, healthcare data schemas, primary and foreign keys, database relationships, SELECT queries and filtering, sorting and grouping records, aggregate functions, join
- Module 6: Python for Healthcare Big Data Analytics Python environments and Jupyter notebooks, variables and data structures, importing healthcare datasets, pandas and NumPy, DataFrame operations, filtering and aggregation, data type conversion, cleaning a
- Module 7: Big Data Processing and Distributed Analytics Limitations of single-machine data processing, distributed computing fundamentals, parallel processing, data partitioning and shuffling, cluster computing concepts, Apache Spark architecture, Spark D
- Module 8: Healthcare Statistics and Advanced Analytical Methods Descriptive and inferential statistics, distributions and variability, confidence intervals, hypothesis testing, correlation and association, statistical significance versus practical signifi
- Module 9: Population Health Analytics and Risk Stratification Population health concepts and frameworks, defining populations and cohorts, patient segmentation, disease burden measurement, morbidity and mortality analysis, chronic disease monitoring, mult
- Module 10: Healthcare Data Visualisation and Power BI Dashboards Principles of healthcare data visualisation, selecting charts for clinical and population health indicators, importing data into Power BI, Power Query data transformation, data modelling and
- Module 11: Healthcare Time-Series Analysis and Forecasting Time-dependent healthcare datasets, daily weekly monthly and annual trends, trend and seasonal components, moving averages, healthcare demand patterns, outpatient attendance forecasting, inpatient
- Module 12: Machine Learning for Healthcare Analytics Introduction to machine learning, supervised and unsupervised learning, defining prediction problems, selecting features and target variables, data preparation for modelling, training and test datasets,
- Module 13: Artificial Intelligence and Generative AI in Healthcare Analytics AI applications in clinical operations and population health, traditional analytics versus machine learning and generative AI, using AI assistants to support Python and SQL devel
- Module 14: Healthcare Interoperability and Data Integration Standards Healthcare interoperability principles, syntactic and semantic interoperability, patient and provider identifiers, terminology and coding systems, diagnosis and procedure coding concept
- Module 15: Cloud Computing and Scalable Healthcare Data Platforms Cloud computing fundamentals, infrastructure and platform services, cloud storage and data processing concepts, scalable data warehouses, cloud-based analytics environments, managed databas
- Module 16: Healthcare Data Governance, Privacy and Cybersecurity Healthcare data governance frameworks, data ownership and stewardship, access controls and least-privilege principles, personal and sensitive health information, consent and lawful data use,
- Module 17: Clinical Operations and Healthcare Performance Analytics Hospital performance measurement, outpatient and inpatient activity, patient flow and waiting times, bed occupancy and length of stay, emergency department performance, readmission analys
- Module 18: Geographic, Environmental and Social Determinants of Health Analytics Geographic health data fundamentals, GIS concepts and spatial datasets, mapping health facilities and population distribution, disease burden by geographic area, health servi
- Module 20: Integrated Healthcare Big Data Analytics Capstone Project Defining a healthcare analytics problem, identifying a suitable dataset, formulating analytical questions, documenting data sources and limitations, designing a data dictionary, developi
General notes
- Healthcare Big Data Analytics, Population Health Analytics Training, Healthcare Data Science Course, Health Informatics Training, Healthcare Predictive Analytics, AI in Healthcare, Clinical Data Analytics, Electronic Health Records Analytics, Healthcare Data Visualisation, Public Health Data Science, Healthcare Machine Learning.
- 5. Practical Training Exercises and Expected Outputs Import and profile a large synthetic healthcare dataset, data profiling report and data quality summary, clean and standardise patient visit and facility records, validated analysis-ready dataset and documented cleaning log, integrate hospital admissions and outpatient attendance data, integrated healthcare dataset and data dictionary, query patient activity and service utilisation using SQL, reusable SQL queries and healthcare utilisation summaries, process a large dataset using Python and pandas, reproducible notebook and analytical summary, perform distributed data processing using PySpark, scalable data transformation workflow and processing performance notes, calculate disease prevalence and hospital readmission indicators, epidemiological indicator report, analyse high-risk population groups using suitable health indicators, population risk stratification summary, develop a Power BI hospital performance dashboard, interactive healthcare performance dashboard, forecast monthly hospital attendance, forecast report with error metrics and uncertainty intervals, develop a basic machine learning model using synthetic or appropriately de-identified data, trained model evaluation report and documented limitations, apply AI assistance to generate a draft analytical report, verified report with checked calculations and evidence, map health facilities and population access indicators, geographic health access map and interpretation, conduct a healthcare data governance assessment, privacy and security checklist with mitigation actions, complete an integrated healthcare big data project, final analytical report, reproducible analysis, dashboard and evidence-based recommendations
- Training Methodology The course uses a practical, project-based learning approach combining instructor-led presentations, software demonstrations, guided exercises, case studies, group assignments and a final capstone project. Participants work with synthetic, public or appropriately de-identified healthcare datasets to practise integrating data, processing large files, querying databases, building dashboards, developing predictive models and communicating findings. Exercises can be adapted for hospital operations, population health, epidemiology, health insurance, disease surveillance or healthcare programme management.
- : Healthcare Big Data Analytics Course, Population Health Analytics Training, Healthcare Data Science Training, Big Data in Healthcare, Healthcare Predictive Analytics, Health Informatics Training, Python for Healthcare Analytics, SQL for Healthcare Data, Apache Spark Training, AI in Healthcare Analytics, Population Health Data Science, Healthcare Data Engineering, Healthcare Machine Learning, Health Data Visualisation.
Frequently asked questions
1. 1. What is big data analytics in healthcare?
Big data analytics in healthcare involves processing and analysing large, diverse and sometimes rapidly generated datasets to identify patterns, understand healthcare performance, assess population health needs and support better decisions.
2. 3. What makes this course different from general healthcare data analysis?
The course extends beyond basic statistical analysis and reporting to include scalable data processing, data integration, database querying, big data architecture, predictive modelling, population health analytics and AI applications.
3. 4. Will I learn Python and SQL?
Yes. Participants use Python for data cleaning, statistical analysis and machine learning, and SQL for querying, joining and summarising healthcare data. The programme also introduces distributed processing with PySpark.
4. 5. Do I need access to real hospital records?
No. Practical exercises can use synthetic or publicly available datasets. Real patient records should only be used where access is authorised and privacy, consent and institutional requirements are satisfied.
5. 6. Will the course cover artificial intelligence?
Yes. The curriculum includes machine learning, forecasting, AI-assisted analytical workflows, automated report drafting and responsible AI. Participants learn to validate outputs and recognise bias, uncertainty and privacy risks.
6. 7. What dashboards will participants learn to develop?
Depending on the selected project, participants may develop dashboards for hospital admissions, bed occupancy, waiting times, service utilisation, readmissions, disease trends, health indicators, population risk groups and resource planning.
7. 8. Is Apache Spark necessary for healthcare data analytics?
Spark is useful when datasets or processing workloads exceed what is practical on a single computer. The course introduces distributed processing so participants understand when Spark is appropriate and when Python, SQL or conventional databases are sufficient.
8. 9. What practical outputs will participants produce?
Outputs may include cleaned datasets, SQL queries, Python notebooks, data processing workflows, healthcare performance dashboards, population health reports, forecasting models, machine learning evaluation reports and a final capstone project.
Schedule & fees
| Dates | Location | Duration | Fee (USD) |
|---|
| 05/04/2027 - 16/04/2027 |
Mombasa,Kenya |
10 Days |
$2,000 |
| 05/07/2027 - 16/07/2027 |
Nairobi,kenya or Virtual |
10 Days |
$2,000 |
| 19/07/2027 - 30/07/2027 |
Naivasha,Kenya |
10 Days |
$2,000 |
| 06/09/2027 - 17/09/2027 |
Rwanda,Kigali |
10 Days |
$3,500 |