Research and Data Analysis

Epidemiology: Advanced Data Analysis for Public Health Training Course

Introduction

In today's data-driven healthcare landscape, the ability to perform advanced data analysis in epidemiology is a vital skill for public health professionals seeking to make evidence-based decisions. Epidemiology in Advanced Data Analysis for Public Health Training Course is a cutting-edge program tailored to equip learners with robust skills in data interpretation, predictive modeling, statistical software application, and epidemiologic surveillance. With a growing global demand for accurate health data insights, this course bridges the gap between theoretical epidemiology and real-world public health data solutions, ensuring learners gain practical expertise in tools like R, Python, SAS, and advanced biostatistics.

Through immersive case studies, hands-on projects, and interactive modules, participants will master core competencies such as multivariate analysis, spatial epidemiology, machine learning integration in epidemiological research, and interpretation of population health data. This course is designed for professionals aiming to lead in disease prevention, outbreak analysis, and policy development using advanced analytical methodologies and current technologies.

Programme Curriculum

Epidemiology in Advanced Data Analysis for Public Health Training Course

Introduction

Course Objectives

Analyze epidemiologic data using machine learning algorithms.
Apply predictive modeling to identify trends in public health.
Use data visualization tools to interpret epidemiological patterns.
Conduct time-series analysis for disease outbreak prediction.
Integrate geospatial analytics in health surveillance systems.
Perform risk factor analysis using multivariate regression.
Evaluate public health interventions using advanced metrics.
Utilize big data platforms such as Hadoop and Spark for epidemiological studies.
Manage large-scale epidemiologic datasets using R and Python.
Interpret findings to influence health policy decisions.
Conduct meta-analysis for evidence synthesis in epidemiologic studies.
Assess the impact of social determinants of health through data mining.
Develop data-driven public health strategies based on real-time analytics.

Target Audiences

Epidemiologists
Public Health Officers
Biostatisticians
Health Data Scientists
Policy Analysts
Healthcare Administrators
Global Health Researchers
Graduate Students in Public Health and Data Science

Course Duration: 5 days

Course Modules

Module 1: Foundations of Advanced Epidemiologic Analysis

Review of epidemiologic concepts and study designs
Introduction to advanced statistical methods
Data cleaning and transformation techniques
Software overview: R, Python, SAS
Common pitfalls in data interpretation
Case Study: Re-analysis of CDC obesity dataset using R

Module 2: Multivariate and Regression Analysis

Linear and logistic regression models
Cox proportional hazards modeling
Confounding and interaction effects
Model diagnostics and goodness-of-fit
Reporting and visualizing multivariate models
Case Study: Modeling cardiovascular risk factors using NHANES data

Module 3: Time-Series and Longitudinal Data Analysis

Time-series decomposition and forecasting
Repeated measures and growth curve models
Autocorrelation and seasonality in data
Tools: ARIMA, GEE, Mixed Models
Best practices for temporal data visualization
Case Study: Forecasting flu trends with Google Flu Trends and CDC data

Module 4: Spatial Epidemiology and Geospatial Data

Mapping disease distribution
Introduction to GIS in health research
Spatial regression and clustering methods
Tools: QGIS, GeoDa, ArcGIS
Addressing spatial autocorrelation and bias
Case Study: Identifying malaria hotspots using Kenyan health data

Module 5: Machine Learning in Epidemiology

Supervised vs. unsupervised learning
Algorithms: Random Forests, SVM, KNN
Feature selection and model validation
Ethical implications of machine learning in health
Deployment of models for real-time surveillance
Case Study: Predicting diabetes risk using a machine learning model on BRFSS data

Module 6: Data Visualization and Communication

Principles of health data storytelling
Creating dashboards with Tableau and Power BI
Custom visualizations in R (ggplot2) and Python (Seaborn)
Designing visuals for policy and public consumption
Communicating uncertainty and limitations
Case Study: Visualizing COVID-19 vaccine rollout across demographic groups

Module 7: Meta-Analysis and Systematic Reviews

Designing and conducting systematic reviews
Effect size calculation and heterogeneity assessment
Fixed vs. random-effects models
Funnel plots and publication bias detection
PRISMA guidelines for transparency
Case Study: Meta-analysis of interventions for childhood asthma

Module 8: Ethics, Policy, and Data-Driven Decision Making

Data governance and ethical considerations
Informed consent and anonymization techniques
Translating data into policy action
Frameworks for decision-making under uncertainty
Engaging stakeholders in data interpretation
Case Study: Policy shift analysis following opioid epidemic data in the U.S.

Training Methodology

Interactive lectures with real-time demonstrations
Guided coding labs using R, Python, and SAS
Hands-on group projects and datasets
Case-based learning for real-world applicability
Continuous assessment through quizzes, peer reviews, and feedback
Access to a collaborative learning platform and mentorship

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

Epidemiology public health advanced analytics

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

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000