This course applies statistics in business and data science, suited for students with a B or higher in undergraduate statistics. It begins with a survey of statistics across fields, covering probability, data types, distributions, and Bayesian methods. Students will learn key techniques like sampling, hypothesis testing, and both descriptive and inferential statistics, with emphasis on tests such as t-tests, regression, PCA, and model evaluation. The course focuses on experimental design, randomization, blinding, and addressing confounds. It covers validity, replication, and best practices in reporting and visualization, using tools like R, SAS, and ggplot2. Discussions also address the philosophy of science and challenges in subjective measures. Prerequisite: undergraduate statistics.