Dive into medical and healthcare statistics using Python and Jupyter notebooks in this 6-hour introductory course. Learn common statistical tests while simultaneously developing practical programming skills in Python for data analysis. Explore research types, data types, and essential statistical concepts such as measures of central tendency, probability, and hypothesis testing. Master the use of Pandas for data manipulation, visualize data with Plotly and Seaborn, and gain hands-on experience with parametric and nonparametric tests, confidence intervals, and linear regression. Conclude by understanding key healthcare metrics like sensitivity, specificity, and predictive values. Follow along with interactive Jupyter notebooks to apply your knowledge and perform your own statistical analyses in a medical context.