Courses Taught

In my role as an educator, I have designed and delivered a comprehensive suite of courses that bridge the disciplines of biomedical data science, bioinformatics, machine learning, and public health. These courses are structured to provide students with both a strong theoretical foundation and practical, hands-on experience in utilizing computational tools and data science techniques to address complex health-related challenges. The courses I teach are tailored to undergraduate and graduate students, researchers, and professionals across a range of fields, including epidemiology, bioinformatics, public health, and machine learning. The curriculum is designed with a focus on advanced methodologies in data analysis, genomic research, and the application of artificial intelligence to public health and healthcare systems. Students are trained in statistical analysis, programming in R and Python, and the application of machine learning algorithms to real-world health data, with a particular emphasis on areas such as cancer bioinformatics, RNA-Seq analysis, and AI-driven healthcare interventions. By fostering a rigorous academic environment, my courses aim to equip students with the skills necessary to navigate and contribute to the rapidly evolving fields of biomedical research and health data science. I strive to promote a deep understanding of these areas while encouraging students to engage critically with the latest technological advancements, thereby preparing them for meaningful careers in research, clinical applications, and technology development.

Research Methodology and Communication

Course Title	Syllabus	Learning Outcomes	Target Audience
Ethics in Research	Syllabus	Understand ethical principles, guidelines, and regulations in research	Graduate students, researchers
Fundamentals of Health Research	Syllabus	Gain foundational knowledge in health research design, methods, and analysis	Public health students, early-career researchers
Mastering Biomedical Data Management	Syllabus	Learn data management strategies, from collection to archiving in biomedical research	Biomedical researchers, data managers
Public Health Writing and Communication Skills	Syllabus	Develop effective writing and communication skills for public health research	Public health students, health communication professionals

Advanced R & Data Analytics for Biomedical Research

Course Title	Syllabus	Learning Outcomes	Target Audience
R for Research	Syllabus	Master R for statistical analysis and data visualization in biomedical research	Graduate students, bioinformaticians
Clinical Reporting using R	Syllabus	Learn clinical data reporting techniques using R, including statistical analysis and reporting formats	Healthcare professionals, clinical researchers
Building Dashboard with R	Syllabus	Gain expertise in developing interactive dashboards with R for biomedical data visualization	Data scientists, biomedical researchers
Research Data Analysis with SPSS	Syllabus	Master SPSS for analyzing biomedical research data, focusing on advanced statistical methods	Graduate students, public health researchers

Biomedical Data Science & AI Applications in Health

Course Title	Syllabus	Learning Outcomes	Target Audience
Ethics and Governance of Artificial Intelligence for Health	Syllabus	Understand the ethical and governance frameworks for AI in healthcare, including privacy, security, and accountability	Graduate students, AI researchers, healthcare professionals
Foundations of Health Data Science	Syllabus	Learn the fundamentals of health data science, including data collection, preprocessing, and basic analytics	Public health students, aspiring data scientists
AI for Public Health	Syllabus	Apply AI techniques to public health challenges, including disease modeling, surveillance, and policy analysis	Public health researchers, AI practitioners
AI for Climate Change	Syllabus	Learn to use AI models to analyze and predict the impacts of climate change on public health	Environmental scientists, public health professionals
AI for Disaster Management and Public Health Emergency	Syllabus	Use AI to support disaster management, emergency response, and public health resilience during crises	Public health professionals, disaster management experts
Applied Machine Learning for Healthcare	Syllabus	Gain hands-on experience applying machine learning techniques to healthcare datasets for disease prediction, diagnosis, and treatment optimization	Healthcare data scientists, researchers, machine learning enthusiasts

Bioinformatics & Machine Learning in Healthcare

Course Title	Syllabus	Learning Outcomes	Target Audience
Bioinformatics BootCamp	Syllabus	Learn basic bioinformatics tools, data processing techniques	Graduate students, researchers new to bioinformatics
Cancer Bioinformatics	Syllabus	Understand cancer genomics, variant calling, and analysis workflows	Graduate students in cancer research
R for Bioinformatics	Syllabus	Gain proficiency in using R for bioinformatics tasks, such as sequence analysis	Bioinformatics researchers, R beginners
R for Cancer Bioinformatics	Syllabus	Apply R tools for cancer bioinformatics, focusing on mutation analysis and pathways	Cancer researchers, R users
Machine Learning for Bioinformatics	Syllabus	Implement machine learning algorithms for omics data analysis	Data scientists, bioinformaticians
Bulk RNA-Seq Analysis with R	Syllabus	Analyze bulk RNA-Seq data using R, including differential expression and visualization	Graduate students in genomics, bioinformaticians
Single Cell RNA-Seq Analysis with Python	Syllabus	Analyze single-cell RNA-Seq data using Python, focusing on clustering and trajectory analysis	Researchers working with single-cell data
AI for Bioinformatics	Syllabus	Apply AI techniques (e.g., deep learning) to bioinformatics problems	Advanced researchers, AI practitioners in life sciences

YouTube Contents

Machine Learning for Bioinformatics

Cancer Bioinformatics

R for Research

RNA-Seq Analysis with R