Introduction

Public health professionals today operate in a highly data-driven environment where evidence-based decision-making, epidemiological surveillance, predictive analytics, and health systems research are essential for improving healthcare outcomes. The increasing demand for real-time health intelligence, biostatistical analysis, disease modeling, and data visualization has made statistical software proficiency a critical competency for researchers, epidemiologists, monitoring and evaluation specialists, and healthcare managers. Statistical Software for Public Health Training Course equips participants with practical skills in R, SPSS, and Stata to manage, analyze, interpret, and visualize complex public health datasets effectively.

The course emphasizes hands-on learning using real-world public health datasets and modern analytical approaches aligned with global health trends such as digital health analytics, epidemiological intelligence, health informatics, machine learning in healthcare, advanced biostatistics, data governance, and predictive public health modeling. Participants will gain competencies in statistical programming, quantitative research methods, data management, survey analysis, regression modeling, and evidence generation for policy formulation and program evaluation. The training integrates practical exercises, case studies, and interactive workshops to ensure participants can confidently apply statistical techniques to solve current and emerging public health challenges.

Programme Curriculum

Statistical Software for Public Health Training Course

Introduction

Course Duration

5 days

Course Objectives

By the end of the training, participants will be able to:

Apply advanced biostatistics techniques using modern statistical software platforms.
Perform epidemiological data analysis for disease surveillance and outbreak investigations.
Conduct data cleaning, transformation, and validation for large public health datasets.
Generate interactive data visualizations and dashboards for health reporting.
Execute predictive analytics and health forecasting using statistical models.
Analyze health survey and demographic data using sampling and weighting techniques.
Perform regression modeling and multivariate analysis for public health research.
Utilize machine learning applications in healthcare analytics.
Interpret and communicate evidence-based public health findings effectively.
Conduct monitoring and evaluation (M&E) analytics for donor-funded programs.
Develop reproducible research workflows using statistical programming and automation.
Apply health informatics and digital health analytics in decision-making.
Strengthen competencies in research data management, statistical reporting, and policy analysis.

Target Audience

Public Health Officers
Epidemiologists and Disease Surveillance Officers
Monitoring & Evaluation (M&E) Specialists
Health Researchers and Biostatisticians
Medical and Healthcare Professionals
NGO and Development Program Staff
University Lecturers and Graduate Students
Data Analysts and Health Information Officers

Course Modules

Module 1: Introduction to Statistical Software in Public Health

Overview of public health analytics
Introduction to R, SPSS, and STATA interfaces
Installing packages and managing datasets
Public health data structures and formats
Ethical considerations and data governance
Case Study: Analysis of national health survey data for disease prevalence estimation.

Module 2: Data Management and Cleaning

Data importing and exporting techniques
Data transformation and recoding
Missing data management strategies
Data validation and quality assurance
Creating analysis-ready datasets
Case Study: Cleaning COVID-19 surveillance datasets for epidemiological reporting.

Module 3: Descriptive Statistics and Data Visualization

Measures of central tendency and dispersion
Frequency tables and cross-tabulations
Advanced charts and dashboards
Geographic and spatial health visualization
Automated reporting techniques
Case Study: Visualizing maternal and child health indicators across regions.

Module 4: Biostatistics and Inferential Analysis

Hypothesis testing methods
Confidence intervals and significance testing
Correlation and association analysis
Parametric and non-parametric tests
Interpretation of statistical outputs
Case Study: Comparing treatment outcomes between intervention groups.

Module 5: Regression Modeling and Predictive Analytics

Linear regression analysis
Logistic regression for health outcomes
Survival analysis techniques
Predictive modeling in healthcare
Model diagnostics and validation
Case Study: Predicting malaria risk factors using demographic and environmental variables.

Module 6: Epidemiological and Survey Data Analysis

Disease surveillance analytics
Incidence and prevalence calculations
Sampling methodologies and weighting
Complex survey data analysis
Time-series analysis for outbreaks
Case Study: Outbreak investigation and trend analysis for infectious diseases.

Module 7: Machine Learning and Advanced Analytics

Introduction to machine learning in public health
Classification and clustering techniques
Decision trees and random forests
Health risk prediction models
AI-driven health analytics applications
Case Study: Machine learning model for predicting hospital readmissions.

Module 8: Reporting, Interpretation, and Decision Support

Statistical reporting standards
Developing evidence-based recommendations
Policy-oriented data interpretation
Presentation of analytical findings
Reproducible research documentation
Case Study: Developing a public health policy brief from analyzed survey data.

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Data management Statistical analysis Data visualization Epidemiological modeling

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Sep 21 2026

21 Sep — 25 Sep 2026

💻 Online / Virtual session

Online $1,000