Project Title: End-to-End UPI Transaction Data Analysis, Python, SQL Database & Power BI Dashboard Project Overview: I am looking for an experienced Data Analyst to complete a comprehensive end-to-end analytics project using a provided UPI (Unified Payments Interface) transaction dataset. The goal is to transform raw data into actionable business insights, covering everything from data cleaning and database design to statistical analysis and interactive dashboard creation. Scope of Work: Data Validation & Cleaning: Assess data integrity (foreign keys, missing values, inconsistencies). You must document what the data lacks and the specific steps taken to correct it (e.g., standardizing categorical values, handling nulls). Database Design (SQL): Write DDL scripts to create a relational schema (Customer, Device, Merchant, Transaction tables) and ingest the cleaned data. Exploratory Data Analysis (Python): Perform EDA using pandas/matplotlib/seaborn. Create derived metrics (e.g., Fraud Rate, Failure Rate, Customer Retention). Statistical Analysis: Validate business hypotheses using statistical tests (T-tests, ANOVA, Chi-square, Correlation) using scipy or statsmodels. Power BI Dashboard: Develop an interactive dashboard (Executive & Fraud Analyst views) connected to the SQL database or cleaned datasets. Reporting: Provide a brief executive summary with top 5 insights and strategic recommendations backed by data. Deliverables: Data Quality Log (Excel/PDF) documenting issues and fixes. SQL DDL Scripts & Data Ingestion queries. Python Jupyter Notebooks (Cleaning, EDA, Statistics). Power BI .pbix file with data model and reports. 2-Page Executive Summary (Insights & Recommendations). Required Skills: Python: pandas, numpy, matplotlib, seaborn, scipy/statsmodels. SQL: MySQL or PostgreSQL (DDL, Constraints, Joins). Visualization: Power BI (DAX, Data Modeling). Statistics: Hypothesis testing, A/B testing concepts. Documentation: Ability to clearly explain data cleaning decisions. To Apply: Please share examples of previous dashboards or analytics projects. In your proposal, briefly mention how you handle missing foreign keys or inconsistent categorical data during the cleaning phase.