Introduction

In the age of precision medicine and data-driven health interventions, biomedical data analysis is a game-changer. Biomedical Data Analysis in Omics Data for Public Health training course offers participants a robust foundation in analyzing and interpreting high-throughput biological data—including genomics, transcriptomics, proteomics, metabolomics, and epigenomics—to drive impactful public health decisions. With real-world applications and hands-on case studies, this course is ideal for professionals aiming to bridge the gap between bioinformatics and public health strategy.

The course empowers learners with trending skills in multi-omics integration, biostatistics, machine learning, and health informatics—all tailored to uncover actionable insights from complex biological systems. By leveraging open-source tools and real omics datasets, participants will gain the competence to lead initiatives in disease surveillance, predictive modeling, population health management, and policy design, all while aligning with ethical standards and regulatory compliance in biomedical research.

Programme Curriculum

Biomedical Data Analysis in Omics Data for Public Health Training Course

Introduction

Course Objectives

Understand core concepts of biomedical and omics data analysis.
Analyze genomic and transcriptomic datasets using bioinformatics pipelines.
Apply data normalization, quality control, and transformation techniques.
Integrate multi-omics data for holistic health insights.
Use statistical modeling and machine learning in omics analytics.
Employ R, Python, and open-source tools for biological data analysis.
Interpret proteomic and metabolomic patterns in disease pathways.
Develop reproducible workflows with FAIR data principles.
Visualize omics data for policy advocacy and public health reporting.
Apply case-based learning for infectious and chronic disease studies.
Address ethical, legal, and social issues (ELSI) in biomedical data usage.
Communicate scientific findings to multidisciplinary health teams.
Explore the future of AI and big data in omics-driven public health.

Target Audience

Public Health Analysts
Epidemiologists
Biomedical Researchers
Data Scientists
Biostatisticians
Policy Makers in Health
Healthcare IT Professionals
Graduate Students in Health Sciences

Course Duration: 5 days

Course Modules

Module 1: Foundations of Biomedical and Omics Data

Introduction to omics: genomics, proteomics, transcriptomics, metabolomics
Public health relevance of omics data
Key databases and repositories (NCBI, EMBL-EBI)
Introduction to data standards and formats
Overview of ethical data management
Case Study: Building a genomic profile for population risk mapping

Module 2: Data Preprocessing and Quality Control

Omics data acquisition and cleaning
Batch effect correction and normalization
Handling missing values and outliers
Preprocessing tools in R and Python
Data quality reporting
Case Study: Preprocessing of lung cancer gene expression dataset

Module 3: Genomic and Transcriptomic Data Analysis

Sequence alignment and variant calling
Expression profiling and clustering
Differential gene expression analysis
Use of tools (DESeq2, edgeR, STAR)
Integrative data interpretation
Case Study: Transcriptomic analysis of COVID-19 patients

Module 4: Proteomics and Metabolomics Integration

Protein identification and quantification
Metabolite annotation and pathway analysis
Use of MS and NMR data
Integration with transcriptomic data
Systems biology approach
Case Study: Multi-omics approach in type 2 diabetes research

Module 5: Statistical Methods in Omics

Biostatistical principles for omics
Regression, ANOVA, and correlation in high-dimensional data
Adjusting for multiple comparisons (FDR, Bonferroni)
Feature selection techniques
Dimensionality reduction (PCA, t-SNE)
Case Study: Statistical modeling in Alzheimer's biomarker discovery

Module 6: Machine Learning for Biomedical Data

Supervised vs. unsupervised learning
Classification and clustering algorithms (Random Forest, K-means)
Model training, validation, and performance metrics
Deep learning in omics
Risk prediction modeling
Case Study: AI-based cancer subtype classification using omics

Module 7: Data Visualization and Communication

Data storytelling in public health
Creating interactive dashboards (Shiny, Plotly, Tableau)
Heatmaps, volcano plots, network maps
Best practices in scientific communication
Visualizing uncertainty and reproducibility
Case Study: Visualization of Zika outbreak genomics in Latin America

Module 8: Ethical, Legal, and Future Trends

ELSI in biomedical data analysis
Privacy, consent, and de-identification
Regulatory compliance (HIPAA, GDPR)
The future of AI and federated learning
Open science and reproducibility
Case Study: Ethical challenges in global genome-sharing initiatives

Training Methodology

Interactive lectures and expert-led webinars
Guided hands-on practicals using real datasets
Peer-reviewed collaborative projects
Capstone project aligned with learners' work environments
Case-based discussions for every module
Continuous feedback and real-time Q&A sessions

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

Omics data biomedical analytics research

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

Biomedical Data Analysis in Omics Data Training Course

Introduction

Programme Curriculum

Skills & Software Covered

Available Sessions