End to End Data Analytics Project (Python + SQL)

Summary

Key Points

• —The project concludes by highlighting the end-to-end process from data extraction and cleaning in Python to data loading and analysis in SQL Server, with all code provided on GitHub. 0:26
• —Downloaded data, initially in a ZIP file, is extracted to a CSV format for further processing. 5:13
• —The project begins by using the Kaggle API and Python to download a dataset, requiring users to set up a Kaggle API token. 6:26
• —Pandas is used to load the CSV data, clean it by handling missing values (like 'not available' and 'unknown') and standardizing column names to lowercase with underscores. 9:20
• —New columns such as 'discount', 'sale_price', and 'profit' are derived from existing price and discount information. 15:11
• —The 'order_date' column is converted from an object type to a datetime format for proper date-based analysis. 15:37
• —Unnecessary columns like 'list_price', 'discount_percentage', and 'cost_price' are dropped to streamline the dataset. 17:33
• —The cleaned Pandas DataFrame is loaded into SQL Server using SQLAlchemy, with options to replace or append data to tables. 23:52
• —The video demonstrates how to create an empty table in SQL Server with specific data types before appending data to ensure optimal storage and performance. 23:52
• —Several SQL queries are executed to answer business questions, including finding top revenue-generating products, top-selling products per region, month-over-month sales growth, highest sales months per category, and subcategory profit growth. 31:57