Power BI SQL Server Python Business Analysis

The Student Performance Analytics & Decision Support System is an end-to-end Business Analysis portfolio project developed for Mega Central High School.

The project demonstrates the complete Business Analysis lifecycle—from requirement gathering and stakeholder analysis to solution design, SQL data validation, Python ETL, dashboard development, testing, and project closure.

The solution enables school management to monitor academic performance, identify at-risk students, detect weak learning areas, and make data-driven educational decisions through interactive Power BI dashboards.

• End-to-End Business Analysis Project
• 40+ Business Analysis Deliverables
• 4 Interactive Power BI Dashboards
• SQL Database Design & Business Queries
• Python ETL Pipeline
• Complete Testing & UAT Documentation
• 12 Documentation Modules

Mega Central High School relied on multiple Excel spreadsheets for tracking student performance.

This resulted in:

• Manual report preparation
• Time-consuming performance analysis
• Difficulty identifying struggling students
• No centralized reporting system
• Delayed academic interventions
• Limited visibility into subject and chapter performance

The school required a centralized analytics solution that provides real-time academic insights for teachers, academic coordinators, and school management.

The solution aims to:

• Monitor overall school academic performance
• Identify students requiring academic intervention
• Track individual student progress
• Analyze subject-wise performance
• Detect weak-performing chapters
• Support curriculum improvement
• Enable data-driven decision making
• Reduce manual reporting effort

• Principal
• School Management
• Academic Coordinator
• Subject Teachers
• Students (Indirect)
• Parents (Future Enhancement)

Student-Performance-Analytics/
│
├── 01_Project_Overview
├── 02_Stakeholder_Analysis
├── 03_Business_Analysis
├── 04_Requirements
├── 05_Data_Analysis
├── 06_Process_Modeling
├── 07_Solution_Design
├── 08_Data_Pipeline
├── 09_SQL
├── 10_Dashboard
├── 11_Testing
├── 12_Project_Closure
│
└── README.md

Architecture

The Power BI solution consists of four interactive dashboards.

Provides a school-wide summary of academic performance.

Executive Dashboard

• Total Students
• Tests Conducted
• Average Score
• Pass Percentage
• High Risk Students
• Top Performers

• Subject Performance
• Student Performance Distribution
• Student Risk Distribution
• Monthly Academic Performance Trend

Provides a detailed academic profile for each student.

Student Dashboard

• Student Selector
• Student Information Cards
• Subject-wise Performance
• Chapter-wise Learning Gaps
• Assessment History
• Student Performance Trend

Helps identify academically vulnerable students.

Risk Dashboard

• Total Students
• High Risk Students
• Medium Risk Students
• Low Risk Students
• Average Score
• Students Needing Support

• Student Risk Distribution
• Attendance Category Analysis
• Subject-wise Risk Assessment
• High Risk Student List

Evaluates curriculum effectiveness.

Subject Dashboard

• Total Subjects
• Total Chapters
• Average Accuracy
• Chapters Below Target

• Subject-wise Performance
• Question Difficulty Distribution
• Chapter Accuracy Distribution
• Lowest Performing Chapters
• Chapter Performance Table

This repository includes complete Business Analyst documentation.

• Project Charter
• Business Case
• Problem Statement
• Scope Document

• Stakeholder Register
• Stakeholder Matrix
• RACI Matrix
• Communication Plan

• Current State Analysis
• Gap Analysis
• Root Cause Analysis
• Solution Proposal

• BRD
• FRD
• User Stories
• Acceptance Criteria
• Requirements Traceability Matrix

• As-Is Process
• To-Be Process
• BPMN Diagrams
• Data Flow Diagram

• Solution Architecture
• Dashboard Wireframes
• Dashboard Requirements
• Dashboard User Guide

• Python ETL
• Data Dictionary
• Source Data Mapping

• Database Creation
• Business Queries
• Data Validation Queries

• Power BI Report
• DAX Measures
• Dashboard Screenshots

• Test Plan
• Test Cases
• Defect Log
• UAT Documentation

• Lessons Learned
• Challenges & Solutions
• Future Enhancements
• Final Project Presentation

✔ Automated manual reporting process

✔ Centralized student performance monitoring

✔ Enabled early identification of at-risk students

✔ Improved curriculum performance analysis

✔ Reduced reporting effort through interactive dashboards

✔ Supported data-driven academic decision-making

Microsoft SQL Server was used to strengthen the analytics solution.

SQL was used for:

• Creating the project database
• Importing cleaned datasets
• Data validation
• Business query development
• Data quality checks
• Supporting analytical reporting

Business queries include:

• Top performing students
• Subject performance analysis
• High-risk student identification
• Pass percentage calculation
• Weak chapter analysis
• Attendance analysis
• Performance trends

The Python ETL pipeline performs:

• Data Extraction
• Data Cleaning
• Duplicate Removal
• Missing Value Handling
• Data Transformation
• Score Calculation
• Performance Classification
• Risk Classification
• Export of Clean Analytical Dataset

• Pass Percentage ≥ 35%
• Target Score = 70%
• Risk Levels:
  • Low Risk
  • Medium Risk
  • High Risk
• Performance Bands:
  • Top Performer
  • High Performer
  • Average
  • Needs Support
  • At Risk

• Parent Portal
• Teacher Performance Dashboard
• Attendance Analytics
• Predictive Student Risk Analysis
• AI-powered Learning Recommendations
• LMS Integration
• Real-time Data Refresh
• Mobile Dashboard
• School Benchmarking
• Natural Language Query Interface

This project was developed as a complete Business Analyst portfolio project to demonstrate:

• Business Analysis
• Requirement Engineering
• Stakeholder Management
• SQL
• Data Validation
• ETL Pipeline Development
• Dashboard Design
• Data Visualization
• Testing
• Documentation
• End-to-End Project Delivery

Ramyashree GV

Business Analyst | Data Analytics Enthusiast

• Stakeholder Analysis
• Requirement Gathering
• BRD & FRD Documentation
• User Story Writing
• Acceptance Criteria
• Requirement Traceability Matrix (RTM)
• Process Modeling (BPMN)
• Data Mapping
• KPI Definition
• Dashboard Requirement Analysis
• SQL Business Queries
• Data Validation
• Python ETL
• Power BI Dashboard Development
• User Acceptance Testing (UAT)

This project uses synthetic educational data created solely for portfolio and learning purposes. No real student information has been used.

This repository showcases an end-to-end Business Analysis project demonstrating the complete software development lifecycle, including business analysis, SQL, ETL, analytics, dashboard development, testing, and project documentation.

The project was developed to demonstrate practical Business Analyst skills for entry-level BA opportunities.

⭐ If you found this project interesting, feel free to explore the documentation and dashboards.