How fatfatma/novashop-business-analysis works
NovaShop is a Business Analyst case study, not an implementation of an e-commerce platform. Its main workflow moves from framing a conversion problem through requirements and process analysis, data-backed diagnosis, solution design, and validation; the dashboard and AI assessment are additional deliverables. The README documents the lifecycle, while sampled Python excerpts confirm dataset generation and analytical work. Source for several documented subsystems was not sampled, so their internal wiring is not inferred.
Main entry points: v4 V4 raw data · 06_Root_Cause_Analysis Root-cause analysis · 09_Solution_Design Solution design · 01_eda_business_kpi.py KPI validation & EDA
Components 18
Business Discovery
- Problem & scope
01_Business_Problem - Stakeholder analysis
02_Stakeholder_Analysis - Requirements
03_Requirements - Process analysis
04_Process_Analysis
Data Analysis
- V4 raw data
data/raw/v4 - Dataset generation
05_Data_Analysis/Data_Generation/create_dataset.py - KPI validation & EDA
05_Data_Analysis/01_eda_business_kpi.py - Conversion & funnel
05_Data_Analysis/02_conversion_trend.py - Funnel analysis
05_Data_Analysis/03_funnel_analysis.py - Segment diagnostics
05_Data_Analysis
Diagnosis & Insights
- Root-cause analysis
06_Root_Cause_Analysis - Recommendations
05_Data_Analysis/09_recommendations.py - KPI dashboard
07_Dashboard/dashboard/index.html - AI opportunity assessment
08_AI_Solution
Solution Design
- Solution design
09_Solution_Design
Monitoring & Validation
- Testing & UAT
10_Testing - Final project report
11_Project_Documentation
Other components
- Business stakeholders
Connections 20
- Business stakeholders → Problem & scope: frame problem
- Problem & scope → Stakeholder analysis: identify stakeholders
- Stakeholder analysis → Requirements: inform requirements
- Requirements → Process analysis: define change
- Dataset generation → V4 raw data: generates dataset
- V4 raw data → KPI validation & EDA: read data
- V4 raw data → Conversion & funnel: read data
- V4 raw data → Funnel analysis: read data
- V4 raw data → Segment diagnostics: read data
- KPI validation & EDA → Root-cause analysis: inform diagnosis
- Conversion & funnel → Root-cause analysis: inform diagnosis
- Funnel analysis → Root-cause analysis: inform diagnosis
- Segment diagnostics → Root-cause analysis: inform diagnosis
- Root-cause analysis → Recommendations: guide actions
- Recommendations → Solution design: inform design
- AI opportunity assessment → Solution design: inform design
- KPI validation & EDA → KPI dashboard: supports KPIs
- Solution design → Testing & UAT: define validation
- Solution design → Final project report: summarize proposal
- Testing & UAT → Final project report: report outcomes