Data Analytics with Python — Study Notes

A comprehensive, exam-focused guide to Data Analytics with Python, covering NumPy arrays and vectorization, pandas DataFrames (including the .loc[] vs. .iloc[] distinction), data cleaning (missing values, duplicates), data transformation (groupby/split-apply-combine, merging, pivoting), exploratory data analysis, data visualization principles (matplotlib/seaborn chart type selection), basic statistics in Python (scipy.stats), the iterative data analytics pipeline, and reproducibility/communication best practices. Includes a custom data analytics pipeline diagram, fully worked problems (a groupby aggregation, a missing-data diagnosis, chart type selection reasoning), comparison tables clarifying commonly confused pandas concepts, a 10-term glossary, and original practice questions with detailed explanations. Built around the practical, workflow-based reasoning (not just syntax memorization) that effective data analytics requires.