Course overview
Healthcare Data Analytics is the systematic use of healthcare data, statistical methods, data management techniques, visualization tools, and advanced analytical technologies to generate meaningful insights for better healthcare decision-making. It brings together data from sources such as electronic health records, hospital information systems, laboratory and pharmacy systems, health management information systems, public health programmes, research studies, and population datasets. Through effective collection, preparation, analysis, visualization, and interpretation of healthcare data, organizations can understand health trends, monitor performance, identify gaps, evaluate programmes, improve service delivery, and support evidence-based planning.
This course provides a comprehensive and practical approach to Healthcare Data Analytics, covering the complete journey from healthcare data collection and quality management to statistical analysis, SQL, Power BI, Python, GIS, predictive analytics, artificial intelligence, and decision support. Participants will learn how to transform healthcare datasets into actionable information through practical exercises, dashboards, analytical projects, and real-world healthcare scenarios. The course is designed to equip healthcare, public health, health information, monitoring and evaluation, research, management, and IT professionals with the analytical skills required to work effectively with modern healthcare data and support data-driven healthcare systems.
Course objectives
- Understand the principles, concepts, frameworks, and applications of healthcare data analytics.
- Collect, prepare, clean, validate, standardize, and manage healthcare datasets for analysis.
- Assess healthcare data quality, completeness, accuracy, consistency, validity, and timeliness.
- Develop interactive healthcare reports, dashboards, KPIs, and visualizations using Power BI and related tools.
- Analyze routine health information and HMIS/DHIS2 data for monitoring, reporting, and decision-making.
- Apply GIS and spatial analytics to examine healthcare accessibility, coverage, distribution, and geographic health patterns.
- Design and implement end-to-end healthcare data analytics projects that transform healthcare data into actionable insights for evidence-based decision-making.
- Communicate healthcare analytical findings effectively through reports, dashboards, visualizations, and presentations.
Who should attend
Healthcare Data Analysts and Data Officers
Health Information Management and Health Records Officers
Health Informatics Professionals
Public Health Professionals
Epidemiologists and Biostatisticians
Monitoring and Evaluation (M&E) Officers
Healthcare Managers and Hospital Administrators
Medical and Healthcare Researchers
Health Programme and Project Officers
Disease Surveillance and Health Monitoring Officers
Health Management Information System (HMIS) Officers
DHIS2 Users and Health Information System Professionals
Ministry of Health and County Health Department Officers
NGO and Development-Partner Health Programme Staff
Healthcare Quality and Performance Improvement Professionals
Clinical Research and Research Data Professionals
Digital Health and eHealth Professionals
Healthcare Planning and Policy Support Professionals
Health Economists and Healthcare Resource Management Professionals
IT Professionals working in healthcare organizations
Data Analysts and Business Intelligence Professionals entering the healthcare sector
GIS and Geospatial Professionals working with health-related data
Healthcare Consultants and Analytics Professionals
University Lecturers, Researchers, and Academic Professionals in health-related fields
Medical, Nursing, Public Health, Health Informatics, and related graduates seeking healthcare analytics skills
Course content
- Module 1: Foundations of Healthcare Data Analytics Introduction to Healthcare Data Analytics and its Applications Healthcare Data, Information, Knowledge and Intelligence Healthcare Analytics Concepts, Models and Frameworks Healthcare Analytics Lifecycle
- Module 2: Healthcare Data Sources and Information Systems Healthcare Data Sources, Types and Characteristics Collection and Management of Clinical, Administrative, Operational and Public Health Data Electronic Health Records and Electronic Medical Records
- Module 3: Collection, Preparation, Cleaning and Management of Healthcare Data Healthcare Data Collection, Capture and Preparation Data Entry, Validation, Coding and Standardization Cleaning and Transformation of Healthcare Datasets Management of Missing,
- Module 4: Healthcare Statistics, Indicators and Quantitative Analysis Statistical Concepts and Methods for Healthcare Data Healthcare Variables, Measurements and Data Distributions Descriptive and Exploratory Statistical Analysis Frequencies, Percentages,
- Module 5: Healthcare Data Analysis and Exploratory Analytics Healthcare Data Analysis Frameworks and Methodologies Exploratory Analysis of Healthcare Datasets Univariate, Bivariate and Multivariate Analysis Comparative and Cross-Sectional Healthcare Analy
- Module 6: Healthcare Database and SQL Analytics Healthcare Database Concepts and Architecture Relational Healthcare Data Models and Database Structures Design and Organization of Healthcare Analytical Databases Healthcare Tables, Relationships and Data Mo
- Module 7: Healthcare Data Visualization and Business Intelligence Principles and Methods of Healthcare Data Visualization Visual Exploration and Communication of Healthcare Data Healthcare Charts, Graphs, Tables and Interactive Visualizations Healthcare K
- Module 8: Public Health, Epidemiological and Population Health Analytics Public Health Data Analytics Concepts and Methods Epidemiological Data Analysis and Interpretation Population Health Data Analysis Disease, Health Event and Risk Analysis Health Tren
- Module 9: Healthcare Operations, Performance and Decision Analytics Healthcare Operations and Service Delivery Analytics Healthcare Performance Measurement and Analysis Healthcare Quality and Outcome Analytics Healthcare Resource and Capacity Analysis Hea
- Module 10: Python for Healthcare Data Analytics Introduction to Python for Healthcare Analytics Python Data Structures and Healthcare Data Processing Importing, Exporting and Managing Healthcare Datasets Collection, Cleaning and Transformation of Healthca
- Module 11: GIS, Predictive Analytics, AI and Advanced Healthcare Analytics Geographic and Spatial Healthcare Data Analytics GIS-Based Healthcare Mapping and Spatial Analysis Healthcare Accessibility, Coverage and Location Analysis Spatial Patterns, Geogra
- Module 12: Healthcare Data Governance, Privacy, Security and Ethics Healthcare Data Governance Principles and Frameworks Healthcare Data Policies, Standards and Stewardship Patient Privacy, Confidentiality and Data Protection Healthcare Data Access, Shari
- Module 13: Healthcare Analytics Projects, Reporting and Decision Support Healthcare Analytics Project Planning and Problem Definition Identification of Healthcare Data and Analytical Requirements Designing End-to-End Healthcare Analytics Workflows Data An
- Capstone areas Healthcare performance Population health Public health Disease trends Health-service delivery Healthcare resource management Health outcomes Healthcare operations Healthcare accessibility Predictive healthcare analytics Healthcare dashboard
Frequently asked questions
1. What types of healthcare data will be covered?
Participants work with clinical, administrative, operational, public health, population, programme, research, health service, disease surveillance, and healthcare performance data.
2. Will Artificial Intelligence and Machine Learning be covered?
Yes. The course introduces predictive analytics, machine learning, AI, forecasting, classification, regression, clustering, natural language processing, generative AI, and responsible use of AI in healthcare analytics.
3. Will the course cover DHIS2 and HMIS data?
Yes. The course introduces healthcare information systems and demonstrates how routine health information and HMIS/DHIS2-type datasets can be prepared, analyzed, visualized, and used for reporting and decision-making.
Schedule & fees
| Dates | Location | Duration | Fee (USD) |
|---|
| 05/10/2026 - 09/10/2026 |
Nairobi,Kenya or Virtual |
5 Days |
$1,100 |