존스홉킨스 대학은 정말 훌륭한 데이터 분석 강의를 많이 제공한다.
아래는 공공의료부문 관련 통계 수업인데 기초부터 조목조목 짚어가며 설명해준다.
Statistical Reasoning for Public Health: Estimation, Inference, & Interpretation
A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.
About the Course
In this data centric era, statistics has become an essential tool for processing information from the realm of public health and medical research. Understanding both the implications and limitations of the results from such research is essential for making informed treatment decisions, public health practice protocols and policy recommendations. In this class, a conceptual and interpretive approach is applied to some of the most used methods from basic statistics. The course will detail appropriate summary measures for quantifying the health of single populations, and comparing such outcomes between populations using results from representative but imperfect data samples. Additionally, the role of uncertainty in sample based estimated will be covered, allowing for conclusions to be drawn regarding the larger populations under study while recognizing the imperfection in the study estimates. All topics will be considered through a “conceptual lens” allowing students to focus on the “what”, “why”, and “so what” with regards to the implications of research results.
Course Syllabus
- study designs for comparing populations
- single sample numerical summary measures for continuous, binary and time-to-event outcomes
- visual displays for continuous and time-to-event outcomes
- the normal (Gaussian) distribution
- measures of association: mean differences, risk differences, relative risks, odds ratios and incidence rate ratios
- confidence intervals for means, mean differences, proportions, relative risks, odds ratios, incidence rates and incidence rate ratios
- hypothesis testing: paired and two-sample t-tests, the chi-square test, Fisher's exact test, and the log-rank test
- power and sample size