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Built an end-to-end football analytics project using 1.7M+ records covering matches, players, clubs, competitions, appearances, transfers, and valuations.
I cleaned and explored the data with Python, performed analytical work using SQL and SQLite, and developed an interactive Power BI dashboard covering club performance, league intelligence, match analysis, player performance, and the transfer market.
The project was designed around realistic questions a sporting or performance department might need to answer, with the focus on turning a large dataset into clear, decision-ready insights.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Too many businesses try to build complex predictive AI models before their basic data pipelines are even functioning. You cannot skip the fundamentals.
The Data Hierarchy of Needs for scaling SMBs:
Level 1: Stable Collection (SQL) — Your database health and data pipelines. If your raw data is broken, everything built on top of it will fail.
Level 2: Data Governance (Python) — Enforcing uniform definitions, cleaning messy data, and ensuring total integrity.
Level 3: Automated BI (Power BI) — Moving away from manual spreadsheets to automated executive views and daily KPI tracking.
Level 4: Predictive Insights (AI / ML) — Churn models and forecasting. This only generates ROI if levels 1 through 3 are rock solid.
Stop wasting time on manual data entry and broken reports. If you are stuck at Level 1 or 2, I can help you scale up. Check out my Contra services to get your SQL pipelines automated and your Power BI dashboards built right the first time.
Amazing Information
Built a demo trading web app for client related to his university project. This is just a demo trading platform built with Next.js + Python
Building a real-time trading interface with Next.js and Python is a killer stack for university-level projects! Next.js keeps the frontend responsive for high frequency price updates while Python handles data processing cleanly. Very sleek work on the execution
Retail Store Performance Analysis | Python, Excel & Business Analytics
I analyzed 10 days of retail store performance data to uncover the factors driving sales, profitability, margins, and staff efficiency.
Using Python, Pandas, Excel, data cleaning, KPI analysis, and data visualization, I transformed raw business data into a structured analysis and clear, actionable insights.
🔍 Key Insights
Store D achieved the highest operational efficiency and profitability.
Store B showed low productivity despite higher staff hours.
Store C generated strong sales but struggled with lower profit margins.
Operational efficiency had a stronger relationship with profitability than sales volume alone.
🛠️ What I Worked On
Data cleaning & preprocessing
Exploratory Data Analysis (EDA)
KPI identification & calculation
Sales & profit analysis
Margin analysis
Staff productivity & efficiency analysis
Business insight generation
Data visualization & reporting
💡 Business Value
The analysis demonstrates how businesses can move beyond simply tracking sales revenue and use profitability, margins, productivity, and operational KPIs to identify improvement opportunities and make better data-driven decisions.
Tools: Python • Pandas • Excel • Data Analysis • Data Visualization
Data Analyst, Business Analytics, Retail Analytics, Data Analysis, Exploratory Data Analysis, Python Data Analysis, Pandas, Excel Analysis, KPI Analysis, Sales Analysis, Profit Analysis, Data Cleaning, Data Visualization, Business Intelligence, Performance Analysis, Retail Data Analysis, Dashboard & Reporting, Actionable Insights
