Certified Research Associate (CRA+) – 4 Months

• Complete Research Methodology

This comprehensive module builds upon foundational research principles and delves deeper into advanced research design and planning. Students will learn how to develop robust research protocols, formulate complex hypotheses, choose appropriate study designs, manage ethical approvals, and follow international research reporting guidelines (e.g., STROBE, PRISMA). Emphasis is placed on independent project development, critical thinking, and understanding the publication process.

• Cross-sectional Studies, Review Articles, Systematic Reviews & Meta-analysis

This expanded module trains students to not only design and conduct cross-sectional and narrative review studies but also to perform high-level evidence synthesis through systematic reviews and meta-analyses. Students will master literature search strategies (using databases like PubMed), study selection, bias assessment tools (like ROBIS), data extraction, and meta-analysis using statistical software. Emphasis is placed on producing publication-ready manuscripts suitable for peer-reviewed journals.

• Epidemiology & Biostatistics (Advanced)

This module offers a deeper understanding of epidemiological models, risk measurements (e.g., odds ratio, relative risk), multivariable analysis, confounding, and effect modification. Advanced biostatistics topics include regression analysis (linear, logistic), ANOVA, survival analysis (Kaplan-Meier, Cox regression), and statistical modeling. Students will learn to interpret and critically evaluate data in the context of clinical and public health research.

• Data Entry & Advanced Analysis Using Python

Focusing on real-world research data, this module equips students with the ability to perform data cleaning, manipulation, and advanced statistical analysis using Python. Training includes the use of libraries such as pandas, numpy, matplotlib, seaborn, and statsmodels for data visualization and inferential statistics. Students will gain practical skills to independently analyze large datasets, generate plots, and produce reproducible research code suitable for publication.