ADA Global Academy – Medical Research Projects
01 Essentials of Clinical Research Methods

A foundational course covering study design, ethical considerations, data collection protocols, and the fundamentals of conducting rigorous clinical research.

02 Medical Statistics for Evidence-Based Research

Hands-on training in statistical analysis using SPSS, R, or Stata, with a focus on applying statistical methods to real-world health data and interpreting results.

03 How to Design, Conduct & Publish a Medical Research Paper

Step-by-step guidance on formulating a research question, designing a novel study, writing for peer-reviewed journals, and navigating the publication process.

04 Mastering Systematic Reviews and Meta-Analysis

Comprehensive training on PRISMA guidelines, literature search strategies, data extraction, risk-of-bias assessment, and conducting pooled statistical analyses.

05 Randomized Controlled Trials (RCTs): Design and Execution

In-depth coverage of RCT design principles, randomization and blinding techniques, sample size calculation, CONSORT reporting, and regulatory considerations.

06 Grant Writing for Medical and Health Research

Practical skills for crafting competitive grant proposals, identifying funding sources, building a strong specific aims page, and responding to reviewer feedback.

07 Infectious Disease Modeling Course

Introduces compartmental models (SIR, SEIR), parameter estimation, and simulation techniques for understanding and predicting the spread of infectious diseases.

08 Survey Design and Sampling Methods in Health Research

Covers questionnaire construction, reliability and validity testing, probability and non-probability sampling, and analysis of survey data in health settings.

09 Research Presentation & Visualization Skills for Health Professionals

Training in data visualization best practices, scientific poster design, oral presentation techniques, and communicating research findings to diverse audiences.

10 Machine Learning for Health Research

Applied ML techniques for healthcare datasets including supervised and unsupervised learning, model evaluation, and translating predictive models into clinical insights.

11 Foundations of Health Data Science

An introductory course on health informatics, electronic health records, big data in healthcare, and programming for health data analysis using Python or R.

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