Getting Started with Retail Data Analytics

Dive into a comprehensive 2-hour tutorial on Retail Data Analytics using real-world Walmart store data. Learn to develop geo-traceable consumer profiles, including family details, vehicle information, and credit card data for over 4,500 Walmart locations. Create purchase histories spanning three years for each customer and construct a retail data analytics dashboard featuring consumer and store maps. Master complex applications of Python libraries such as Pandas, Matplotlib, Faker, NumPy, and datetime, with a focus on advanced Plotly mapping techniques for multi-layered visualizations. Explore potential project extensions using Streamlit, Dask, and H2O Wave, and discover how to build full-stack applications with Python, Java Quarkus, React, and Next.js using the generated dataset.

- Tutorial Starts
- Content Introduction
- Phase 1: Starts
- Retail Data Management
- Gnerating User Profile Master Data
- Faker Tutorial
- User Profile - Personal Info
- User Profile - Job Info
- User Profile - Credit card Info
- User Profile - Income group Info
- User Profile - Marital Status
- User Profile - Vehicle Info
- User Profile - Geo based Address
- Accessing Walmart Locations
- Show Walmart and user address in Map
- Combining everything so far
- Master Function to generate data
- Saving master profile data
- Phase 2: Product List Creation
- Instacart Product Data
- Phase 3: Master Store List Creation
- Phase 4: Purchase History Creation
- Important concept for Geo Proximity
- Finalizing Data Creation
- Phase 5: Data Viz and Retail analytics
- Store and user proximity map
- Phase 6: Retail/Consumer Analytics
- Retail data analytics
- Future Extensions
- Recap