This course prepares students to become skilled data professionals capable of extracting, analyzing, and interpreting complex datasets. It covers key areas such as programming with Python, R, and SQL, statistical methods like hypothesis testing and regression analysis, and machine learning techniques including supervised and unsupervised learning. Students will also learn data visualization using tools like Tableau, Matplotlib, and Seaborn, and will work on real-world projects to build predictive models and present actionable insights. By the end of the course, students will be ready to tackle roles in data analysis, machine learning, and data engineering.
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