This course aims to provide a thorough introduction to R programming skills and enable students with comprehensive understanding and practical experience in public health data analysis. The course is structured into two distinct sections. The first section will train the students on using R, statistical software (freely available), towards writing smart codes for accomplishing data manipulation, data-processing and statistical analysis. In section 2, the students will be provided with real health data and will be trained to conduct a step by step analyses protocol implementing the techniques that they learnt in section 1. Additionally, a theoretical introduction will be provided at the beginning of each class to ensure a wholesome understanding of the concepts underlying each days task. • R-programming: importing data, data handling and manipulation, resampling strategies, statistical analysis techniques encompassing descriptive statistics, testing of hypothesis and regression. Data visualization techniques using ggplot2 and r-base plots, reading plots towards correct interpretation. • Health datasets analysis: Real de-identified datasets will be provided. Alternately, students can acquire health datasets by themselves (if they want) or use their own research datasets too. The students will be trained and guided to conduct hands-on analysis in a step-by step manner to accomplish descriptive data analysis, variable selection, association analysis/survival analysis, on the provided dataset(s). The students will also be allowed to apply any bioinformatics tools for visualization techniques. Combining R-programming, theoretical introduction along with hands-on analysis, the course equips participants with the skills to effectively analyze public health datasets and make informed, data-driven decisions in their research and practice. The course will for most part be computer based.

Upon completion the students will be able tosuccesfully do the following • Use R to conduct all kinds of data manipulation and data cleaning • Develop statistical thinking and apply statistics in modern public health research and practice • Describe a data set using descriptive statistics and graphical methods as an initial step for more advanced analysis in R software. • Implement suitable methods to formulate and analyze statistical associations between variables in a data set using R. • Interpret the results and provide potential explanations for the findings. Skills that the student will gain: • Data analysis with R • Linear Regression • Logistic regression • Group comparison testing • Survival analysis • Visualization of data • Statistical thinking.

Biostatistics, Statistics, Basic programming (optional), Data preprocessing (optional), Data acquisition (optional)

1. Biostatistics with R, a guide for Medical doctors, Marco Moscarelli, Springer 2. A learning guide to R, Remko Duursma, Jeff Powel, Glenn Stone, Western Sydney University 3. Survival Analysis in Medicine and Genetics, Jialiang Li, Shuangge Ma, Chapman and Hall 4. Working with Data in Public Health, A practical pathway with R, Peng Zhao, Springer

1. The Practical Guide to Clinical Research and Publication, Academic Press, Uzuung Yoon 2. Practical Clinical Research Design and Application, A Primer for Physicians, Surgeons, and Clinical Healthcare Professionals, Springer Open, Peter D. Fabricant