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This module covers the fundamentals of statistics for data science, including measures of central tendency, measures of spread, probability, and statistical interpretation. Learners develop the skills to summarize, analyze, and interpret data for informed 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.
