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This module introduces Exploratory Data Analysis (EDA), covering summary statistics, data interpretation, trend analysis, relationships and correlations, and pattern identification. Learners develop the skills to explore datasets, uncover meaningful insights, and support data-driven decision-making.
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.
