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Statistics Innovation for Health Training Course

Online Training Download PDF

How to Register Click View Schedule for your preferred location, select your training dates, then register as an individual, group, or online participant. You will receive an invitation letter and invoice promptly after submission.

Training Locations Kenya (Nairobi, Mombasa, Malindi, Kisumu, Nakuru, Nanyuki) · Tanzania (Dodoma, Zanzibar, Dar es Salaam) · Dubai UAE · South Africa (Pretoria, Cape Town) · Istanbul · Accra · Banjul more ▾

Groups & Payment Groups of 5+ receive one complimentary place — see group rates. Payment due at least 1 month before (Europe & Asia) or 2 weeks before (Africa programs).

Upcoming Training Schedules 14 locations

Location	Duration	Next Start Date	Dates Available	Action
Nairobi, Kenya	10 days	Aug 31, 2026	97 dates
Accra, Ghana	10 days	Sep 7, 2026	48 dates
Addis Ababa, Ethiopia	10 days	Sep 7, 2026	49 dates
Cape Town, South Africa	10 days	Aug 31, 2026	48 dates
Dar es Salaam, Tanzania	10 days	Aug 31, 2026	49 dates
Dubai, UAE	10 days	Sep 14, 2026	68 dates
Istanbul, Turkey	10 days	Sep 7, 2026	49 dates
Kampala, Uganda	10 days	Oct 5, 2026	48 dates
Kigali, Rwanda	10 days	Sep 21, 2026	48 dates
Kuala Lumpur, Malaysia	10 days	Aug 31, 2026	48 dates
Mombasa, Kenya	10 days	Sep 14, 2026	48 dates
Pretoria, South Africa	10 days	Sep 21, 2026	48 dates
Singapore	10 days	Oct 5, 2026	48 dates
Zanzibar, Tanzania	10 days	Sep 7, 2026	49 dates

Statistics Innovation for Health Training Course

Course Overview

Statistics Innovation for Health Training Course is designed to equip healthcare professionals, epidemiologists, biostatisticians, public health specialists, researchers, monitoring and evaluation officers, health program managers, data scientists, and policymakers with advanced knowledge and practical skills in applying innovative statistical techniques to improve healthcare research, disease surveillance, healthcare planning, and evidence-based decision-making. As healthcare organizations increasingly embrace Health Management Information Systems (HMIS), Results-Based Management (RBM), Monitoring, Evaluation, Accountability and Learning (MEAL), Universal Health Coverage (UHC), Sustainable Development Goals (SDGs), precision public health, digital health transformation, artificial intelligence, predictive analytics, implementation science, and health systems strengthening, statistical innovation has become essential for generating high-quality evidence, improving patient outcomes, and optimizing healthcare resource utilization. This comprehensive course enables participants to integrate modern statistical methods with emerging digital technologies to address complex healthcare challenges.

The course provides participants with practical experience in descriptive and inferential statistics, biostatistics, predictive modeling, Bayesian statistics, survival analysis, multilevel modeling, longitudinal data analysis, machine learning, artificial intelligence, big data analytics, causal inference, geospatial statistics, statistical quality control, health economics, and healthcare forecasting. Participants will learn how to analyze healthcare data from DHIS2, Health Management Information Systems (HMIS), Electronic Medical Records (EMRs), SurveyCTO, KoboToolbox, ODK, laboratory information systems, demographic surveillance systems, and national health surveys using R, SPSS, STATA, Python, SAS, Power BI, Excel, Tableau, and GIS platforms. The course emphasizes innovative approaches that improve healthcare planning, disease prevention, policy formulation, operational research, and clinical decision support.

Participants will further explore advanced concepts including real-world evidence, clinical trial statistics, statistical learning, epidemiological modeling, uncertainty analysis, simulation techniques, dashboard analytics, statistical programming, reproducible research, FAIR data principles, cloud-based analytics, and ethical data science. Practical case studies cover maternal and child health, communicable and non-communicable diseases, immunization, HIV/AIDS, tuberculosis, nutrition, emergency preparedness, environmental health, pharmaceutical research, health financing, and healthcare quality improvement. The training demonstrates how statistical innovation strengthens healthcare governance, supports predictive decision-making, improves resource allocation, and enhances organizational resilience in dynamic healthcare environments.

Through instructor-led lectures, software demonstrations, practical statistical laboratories, collaborative workshops, healthcare simulations, and comprehensive case studies, participants will gain hands-on experience in designing innovative statistical solutions for real-world healthcare challenges. Upon successful completion of the course, participants will possess the technical expertise required to conduct advanced statistical analyses, implement innovative analytical methods, strengthen evidence-based healthcare policies, improve research quality, enhance healthcare monitoring systems, support strategic planning, and promote data-driven innovation across ministries of health, hospitals, universities, research institutions, NGOs, humanitarian organizations, pharmaceutical companies, and international development agencies.

Course Objectives

Understand modern statistical methods and innovations in healthcare.
Apply advanced statistical techniques to healthcare research and program evaluation.
Develop predictive models for healthcare planning and disease surveillance.
Analyze complex healthcare datasets using statistical software.
Utilize artificial intelligence and machine learning in health statistics.
Interpret statistical findings to support evidence-based decision-making.
Apply geospatial and longitudinal statistical analyses in healthcare.
Ensure statistical quality, reproducibility, and ethical data management.
Develop interactive statistical dashboards and reports.
Strengthen healthcare policy development through innovative statistical evidence.

Organizational Benefits

Improves evidence-based healthcare planning and strategic management.
Enhances healthcare research quality and statistical rigor.
Strengthens disease surveillance and predictive analytics capabilities.
Improves monitoring, evaluation, and performance measurement systems.
Supports efficient healthcare resource allocation.
Enhances policy formulation through reliable statistical evidence.
Promotes innovation in healthcare analytics and digital transformation.
Strengthens institutional research and data science capacity.
Improves organizational accountability and decision-making.
Supports continuous healthcare quality improvement and operational excellence.

Target Participants

This course is suitable for:

Biostatisticians
Epidemiologists
Public Health Specialists
Health Researchers
Monitoring and Evaluation Officers
Health Program Managers
Data Scientists
Health Information Managers
Hospital Administrators
Ministry of Health Officials
Clinical Researchers
Academic Researchers
NGO and Humanitarian Project Staff
Health Informatics Specialists
Policy Analysts
Pharmaceutical Research Professionals
Quality Improvement Officers
Healthcare Consultants
Development Partners
Professionals responsible for healthcare research, statistical analysis, policy development, and evidence-based decision-making.

Course Outline

Module 1: Foundations of Modern Health Statistics

Principles of biostatistics
Statistical thinking
Healthcare data types
Descriptive statistics
Inferential statistics
Statistical innovation trends

Case Study: Applying modern statistical methods to evaluate maternal and child health outcomes.

Module 2: Research Design and Statistical Planning

Study design selection
Sampling methodologies
Sample size calculation
Randomization techniques
Statistical power analysis
Bias reduction strategies

Case Study: Designing a statistically robust national immunization effectiveness study.

Module 3: Advanced Data Management

Healthcare data cleaning
Data validation
Missing data management
Data transformation
Data integration
Statistical quality assurance

Case Study: Managing national healthcare datasets from multiple hospitals and surveillance systems.

Module 4: Advanced Statistical Analysis

Regression analysis
Logistic regression
Survival analysis
Time series analysis
Multilevel modeling
Longitudinal analysis

Case Study: Evaluating long-term tuberculosis treatment outcomes using survival analysis.

Module 5: Predictive Analytics and Machine Learning

Predictive modeling
Classification algorithms
Clustering techniques
Machine learning fundamentals
Artificial Intelligence applications
Healthcare forecasting

Case Study: Predicting hospital readmission risks using machine learning models.

Module 6: Geospatial and Epidemiological Statistics

Spatial statistics
GIS integration
Disease mapping
Cluster analysis
Outbreak prediction
Environmental health analysis

Case Study: Mapping malaria hotspots using GIS and spatial statistical models.

Module 7: Statistical Software Applications

R programming
SPSS
STATA
Python for health analytics
SAS applications
Power BI integration

Case Study: Comparing statistical analyses across R, SPSS, and STATA using national health survey data.

Module 8: Statistical Innovation in Healthcare Decision Making

Decision support analytics
Health Technology Assessment
Health economics
Cost-effectiveness analysis
Bayesian decision models
Policy simulation

Case Study: Supporting healthcare investment decisions using cost-effectiveness and statistical modeling.

Module 9: Healthcare Dashboards and Data Visualization

Dashboard design
Interactive visualization
Statistical graphics
Healthcare scorecards
Executive reporting
Data storytelling

Case Study: Developing executive dashboards to monitor healthcare system performance.

Module 10: Quality Assurance and Ethical Statistics

Statistical quality control
Reproducible research
Data governance
Ethical statistical practice
FAIR data principles
Research transparency

Case Study: Ensuring statistical integrity during a multicenter healthcare research project.

Module 11: Translating Statistical Evidence into Policy

Evidence synthesis
Policy analysis
Strategic planning
Resource allocation
Stakeholder communication
Knowledge translation

Case Study: Using statistical evidence to improve national non-communicable disease prevention policies.

Module 12: Emerging Trends in Statistical Innovation for Health

Artificial Intelligence-driven analytics
Big data analytics
Precision public health
Real-world evidence
Cloud-based statistical computing
Future directions in healthcare statistics

Case Study: Developing an AI-enabled national health analytics platform integrating big data, predictive modeling, geospatial intelligence, and real-time statistical dashboards to strengthen healthcare planning and policy implementation.

General Information

Customized Training: All our courses can be tailored to meet the specific needs of participants.
Language Proficiency: Participants should have a good command of the English language.
Comprehensive Learning: Our training includes well-structured presentations, practical statistical exercises, software demonstrations, web-based tutorials, collaborative group work, healthcare simulations, and real-world healthcare case studies. Our facilitators are seasoned experts with over a decade of experience in biostatistics, epidemiology, public health research, data science, monitoring and evaluation, and health systems strengthening.
Certification: Upon successful completion of the training, participants will receive a certificate from Foscore Development Center (FDC-K).
Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training customized to the client's schedule.
Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of training days.
Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
Equipment: Tablets and laptops can be provided to participants at an additional cost.
Post-Training Support: We offer one year of free consultation and coaching after the course.
Group Discounts: Register as a group of more than two participants and enjoy a discount ranging from 10% to 50%.
Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call +254712260031.
Website: Visit www.fdc-k.org for more information.

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training@fdc-k.org • +254 712 260 031 • Nairobi, Kenya