Data Analytics Project | Python, SQL, Power BI

Overview

This project demonstrates a complete data analytics lifecycle using Python, SQL, and Power BI. It focuses on extracting insights from structured data through exploratory data analysis (EDA), data cleaning, SQL-based analysis, and dashboard development.

The project is designed to reflect real-world business analytics workflows and highlights skills required for Data Analyst, Business Analyst, and Analytics Intern roles.

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Business Objective

• Analyze transactional data to identify trends, patterns, and key performance indicators (KPIs)
• Support data-driven decision-making using structured analysis
• Present insights through interactive dashboards and business-ready reports

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Dataset

• Format: CSV

• Type: Structured business / transactional data

• Characteristics:

  • Numerical and categorical features
  • Missing values and inconsistent entries
  • Suitable for EDA, SQL querying, and visualization

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Tools & Technologies

Programming & Analysis

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

Databases & Querying

• SQL
• PostgreSQL
• MySQL
• SQL Server

Visualization & Reporting

• Power BI
• Gamma (Presentation)
• Business Report (PDF)

Environment

• Jupyter Notebook
• VS Code

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Key Skills Demonstrated

• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• SQL Queries (Joins, Aggregations, Subqueries)
• KPI Calculation
• Database Integration
• Data Visualization
• Dashboard Development
• Business Insight Generation
• Data Storytelling

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Project Workflow

1. Data Loading

   • Imported dataset into Python using Pandas
   • Verified schema, data types, and data quality

2. Exploratory Data Analysis (EDA)

   • Descriptive statistics
   • Distribution analysis
   • Trend identification
   • Outlier detection

3. Data Cleaning & Feature Engineering

   • Handled missing values
   • Standardized column names
   • Corrected inconsistent records
   • Created derived features for analysis

4. Database Integration

   • Loaded cleaned data into relational databases
   • Structured tables for efficient querying

5. SQL Analysis

   • Business-focused SQL queries
   • Aggregations and filtering
   • Customer and category-level analysis
   • KPI and performance metrics

6. Power BI Dashboard

   • Interactive dashboard creation
   • KPIs, charts, slicers, and filters
   • Designed for stakeholder-level consumption

7. Reporting & Presentation

   • Compiled insights into a structured business report
   • Created a professional presentation using Gamma

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Power BI Dashboard

The dashboard includes:

• Key Performance Indicators (KPIs)
• Category-wise and segment-wise analysis
• Trend and comparative analysis
• Interactive filters for drill-down insights

Designed to support executive and business stakeholder decision-making.

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Results & Insights

• Identified key trends and performance drivers
• Highlighted high-impact segments and opportunities
• Translated data findings into actionable business insights
• Supported recommendations using quantitative analysis

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How to Run the Project

1. Clone the Repository

git clone <repository-url>
cd data-analytics-project

2. Install Dependencies

pip install pandas numpy matplotlib seaborn sqlalchemy psycopg2

3. Run Python Analysis

• Open Jupyter Notebook
• Execute notebooks for EDA and data cleaning

4. Database Setup

• Configure PostgreSQL / MySQL / SQL Server credentials
• Execute SQL scripts from the /sql directory

5. Power BI

• Open the .pbix file
• Connect to the database
• Refresh data to load insights

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Use Cases

• Data Analyst Portfolio Project
• Analytics Internship Assessment
• Business Intelligence Demonstration
• SQL + Power BI Practice Project

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Keywords for ATS

Data Analyst, Business Analyst, SQL, PostgreSQL, MySQL, SQL Server, Python, Pandas, NumPy, Power BI, EDA, Data Cleaning, Dashboard, KPI, Data Visualization, Business Intelligence, Analytics, Reporting