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The course is structured into five detailed sections. It begins with fundamental Python programming tutorials, covering syntax, variables, operators, conditional statements, loops, data structures, and functions. Next, it delves into data science-specific tutorials, including reading and writing files, working with CSV files, and utilizing powerful libraries like NumPy and Pandas for data analysis. The third part focuses on data cleaning techniques, essential for preparing datasets for analysis.

The fourth section introduces machine learning concepts, including supervised and unsupervised learning, with practical exercises on linear regression, decision trees, and clustering methods. The final part explores data visualization, teaching students to create insightful charts, including line, bar, pie, scatter, and histogram plots, to effectively communicate findings.

What makes this course stand out is its practical approach, including numerous exercises and homework assignments that reinforce learning. It is suitable for anyone with a basic Python background who wants to specialize in data science. Whether you are a beginner or an intermediate coder, this course offers valuable skills to enhance your professional toolkit.

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