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This module covers the fundamentals of data cleaning and preprocessing, including identifying data quality issues, handling missing and inconsistent data, transforming datasets, and preparing clean, reliable data for accurate analysis and machine learning applications.
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.
