This applied introduction to data science course equips students with foundational skills for working with real-world data. Building on courses in statistical methods, numerical methods, and foundations of machine learning, students will learn techniques for data handling, cleaning, transformation, and visualization, with practical applications in healthcare, finance, and marketing. Topics include data integration, interactive dashboards, advanced visualizations, and the use of predictive and classification models. Hands-on exercises and case studies support the development of analytical and modeling skills. The course also addresses ethical issues such as bias, fairness, and privacy. By the end, students will be able to clean and prepare complex datasets, create effective visualizations, build dashboards, apply machine learning models, and complete a capstone project demonstrating their abilities.