This course introduces key statistical concepts and methodologies essential for making data-driven predictions. Starting with fundamental statistical analysis, such as inference and simple regression, it expands into more advanced topics like logistic regression and model selection. You will learn to build predictive models using datasets with various structures, including quantitative and categorical responses and predictors. Discover the balance between over-predicting and under-predicting, and apply these methods to real-world business problems, such as healthcare operations and fraud detection, through practical examples and case studies. Excel will be used for data manipulation and visualization, while R will be employed for processing data and generating prediction models. No prior statistical knowledge or experience with Excel and R is required, making this course accessible to beginners.