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This module introduces the Python libraries NumPy and Pandas for data science, covering array operations, mathematical computations, data handling, data transformation, table creation, filtering, sorting, grouping, aggregation, and exporting datasets for efficient data analysis and processing.
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.
