📊 Data Analytics Project

📌 Overview

This project demonstrates a complete data analytics workflow, starting from data loading and cleaning to data visualization and reporting. The project uses Python for data preprocessing and exploratory data analysis (EDA), SQL for querying the database, and Power BI for building an interactive dashboard. A final report and presentation summarize the key findings.

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📂 Dataset

The dataset contains business-related data used for analysis.

Dataset Features:

• Multiple records with numerical and categorical attributes
• Missing values handled during preprocessing
• Suitable for SQL analysis and dashboard creation

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🛠️ Tools & Technologies

• Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
• SQL (PostgreSQL / MySQL / SQL Server)
• Power BI
• Gamma (Presentation)
• Jupyter Notebook / VS Code

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📈 Project Workflow

1. Data Loading

• Imported dataset into Python
• Verified data types and structure

2. Data Cleaning

• Removed duplicates
• Handled missing values
• Fixed inconsistent data
• Converted data types where necessary

3. Exploratory Data Analysis (EDA)

• Summary statistics
• Distribution analysis
• Correlation analysis
• Trend identification
• Data visualization

4. SQL Analysis

Executed SQL queries to:

• Retrieve important business insights
• Filter and sort records
• Perform aggregations
• Use JOINs and GROUP BY
• Analyze trends

5. Power BI Dashboard

Created an interactive dashboard featuring:

• KPI Cards
• Bar Charts
• Line Charts
• Pie Charts
• Slicers
• Filters

6. Report & Presentation

• Documented findings
• Created a presentation using Gamma
• Highlighted business insights and recommendations

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📊 Dashboard Highlights

• Total Records
• Key Performance Indicators (KPIs)
• Sales/Business Trends
• Category-wise Analysis
• Interactive Filters
• Visual Comparisons

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📌 Results

The project successfully:

• Cleaned and prepared raw data
• Extracted valuable insights using SQL
• Identified important trends through EDA
• Built an interactive Power BI dashboard
• Presented findings in a professional report and presentation

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▶️ How to Run

Python

1. Clone the repository

git clone <repository-link>

2. Install dependencies

pip install pandas numpy matplotlib seaborn

3. Open the Jupyter Notebook

jupyter notebook

4. Run all notebook cells.

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SQL

• Import the dataset into PostgreSQL, MySQL, or SQL Server.
• Execute the provided SQL scripts.

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Power BI

• Open the .pbix file in Power BI Desktop.
• Refresh the dataset if required.

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📁 Project Structure

Data-Analytics-Project/
│
├── Dataset/
├── Python/
│   └── EDA.ipynb
├── SQL/
│   └── queries.sql
├── PowerBI/
│   └── Dashboard.pbix
├── Report/
│   └── Project_Report.pdf
├── Presentation/
│   └── Gamma_Presentation.pdf
├── README.md
└── requirements.txt

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📚 Skills Demonstrated

• Data Cleaning
• Exploratory Data Analysis (EDA)
• SQL Query Writing
• Data Visualization
• Dashboard Development
• Business Insight Generation
• Report Writing
• Presentation Skills

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👩‍💻 Author

Shrawani Baisane

Data Analytics Enthusiast

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