Welcome to Statistical Computing at Johns Hopkins Bloomberg School of Public Health!
What is this course?
This course covers the basics of practical issues in programming and other computer skills required for the research and application of statistical methods. Includes programming in R and the tidyverse, data ethics, best practices for coding and reproducible research, introduction to data visualizations, best practices for working with special data types (dates/times, text data, etc), best practices for storing data, basics of debugging, organizing and commenting code, basics of leveraging Python from R. Topics in statistical data analysis provide working examples.
Getting started
I suggest that you start by looking over the Syllabus and Schedule under General Information. After that, start with the Lectures content in the given order.
Acknowledgements
This course was developed and is maintained by Stephanie Hicks.
The following individuals have contributed to improving the course or materials have been adapted from their courses: Roger D. Peng, Andreas Handel, Naim Rashid, Michael Love.
The course materials are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Linked and embedded materials are governed by their own licenses. I assume that all external materials used or embedded here are covered under the educational fair use policy. If this is not the case and any material displayed here violates copyright, please let me know and I will remove it.