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This module introduces the principles of data visualization, including creating and interpreting charts, analyzing relationships using scatter plots, identifying trends, avoiding misleading visualizations, and combining multiple visuals to effectively communicate data-driven insights.
This course introduces the complete data science workflow, covering dataset understanding, data cleaning and preprocessing, exploratory data analysis, basic statistics, data visualization, Python libraries (NumPy and Pandas), and machine learning fundamentals. Learners gain hands-on experience in analyzing, transforming, visualizing, and interpreting data, apply data science techniques to real-world case studies, and complete an end-to-end project with implementation, presentation, and reporting of results.
