This course provides a solid foundation in statistical concepts and techniques essential for data science and machine learning. Through a combination of theory and practical applications, students will learn to analyze, interpret, and visualize data effectively. Topics include descriptive statistics, probability, hypothesis testing, confidence intervals, regression analysis, and advanced techniques like ANOVA and time series analysis. The course emphasizes the use of statistical programming tools like Python or R for real-world data analysis. By the end of the course, students will be equipped to extract meaningful insights from data, assess the quality of data-driven models, and apply statistical reasoning to solve complex problems.