This project showcases a complete end-to-end data analytics process. It includes loading and exploring a dataset in Python, cleaning and preparing the data, running SQL queries in PostgreSQL, creating an interactive Power BI dashboard, and presenting the results through a written report and a Gamma presentation. The goal is to demonstrate strong analytical skills, technical proficiency, and the ability to communicate insights clearly.
Programming / Analysis
- Python (Pandas, NumPy, Matplotlib/Seaborn)
Database
- PostgreSQL & SQL
Visualization
- Power BI
Reporting
- Gamma (final presentation)
- Data Loading & Understanding
Load dataset in Python
Inspect structure, data types, missing values, duplicates
- Exploratory Data Analysis (EDA)
Summary statistics
Feature distributions
Correlation analysis
Identification of patterns and anomalies
- Data Cleaning
Handle missing values
Remove duplicates
Standardize formats
Create new derived fields if needed
- SQL Analysis in PostgreSQL
Upload cleaned data to PostgreSQL
Perform analytical SQL queries (aggregations, joins, filtering, ranking)
Validate insights from Python
- Power BI Dashboard
Build interactive dashboard
Highlight KPIs, trends, comparisons, and segmentation
Focus on clear and actionable visual storytelling
- Final Report
Summary of project goals, methodology, findings, and recommendations
Includes visualizations and key takeaways
- Gamma Presentation
Polished slide deck summarizing insights and business value
Designed for recruiters, stakeholders, or interview presentations
The Power BI dashboard presents:
High-level KPIs
Trend analysis
Category comparisons
Interactive filters to explore insights
Key trends discovered
Top contributing factors
Opportunities or risks identified
Data-driven recommendations