George Skartados
Data Analyst
I build forecasting models, fraud detection systems, and dashboards that turn complex data into clear operational insights.
SQL • Python • EXCEL • BI
LinkedIn
About Me
I analyze structured data to support operational and financial decisions.
My projects focus on forecasting demand, detecting risk patterns, and building dashboards that make performance trade-offs clear. I start with a business question and aim to end with insights that improve planning, reduce risk, or increase efficiency.
I value practical analysis, the goal is to reduce uncertainty and support better decisions.
Core Skills
Analytics
  • Forecasting
  • Classification
  • Modeling
  • Anomaly Detection
Tools
  • Power BI
  • Excel
  • Git
Languages
  • SQL
  • Python
Projects
Each project is built around a clear business question and ends with measurable outcomes.
  • Reduced false positives by 15% in a fraud detection model while maintaining high recall on imbalanced transaction data
  • Modeled short-term energy demand variability to support operational capacity planning
  • Analyzed 10,000+ app records to identify high-performing revenue and rating patterns
  • Built interactive dashboards to monitor energy usage trends and support efficiency-focused decisions
Approach
Start with the business question →
structure the data →
test assumptions →
deliver insights that inform decisions.
Energy Demand Forecasting
Business Question:
How can energy demand variability affect short-term capacity and cost planning in data center operations?
Approach:
Modeled baseline vs. renewable adoption scenarios using historical energy data. Structured datasets for comparative analysis and built interactive reporting views.
Key Insight:
Scenario modeling indicated up to ~70% potential emissions reduction at 80% renewable penetration, with clear regional demand differences impacting planning assumptions.
Outcome:
Delivered an interactive Power BI dashboard enabling comparison of demand patterns and supporting structured planning discussions.
Solar Energy Forecasting
Business Question:
How predictable is short-term solar generation, and how can forecasts improve operational monitoring?
Approach:
Cleaned and structured hourly generation data, removed outliers, and built a 7-day Prophet forecast model.
Outcome:
Designed a Power BI dashboard comparing forecast vs. actual output, enabling rapid identification of underperformance patterns.
Credit Card Fraud Detection
Business Question:
How can fraud risk be detected reliably in highly imbalanced transaction data without overwhelming investigation teams?
Dataset:
284,807 transactions (0.17% fraud rate)
Approach:
Trained and compared multiple classification models (LogReg, Random Forest, SVM), applied SMOTE balancing, tuned hyperparameters, and evaluated using F1-score and ROC metrics.
Key Result:
Random Forest achieved ~90.6% F1-score, balancing precision and recall to reduce false positives while maintaining fraud detection sensitivity.
Outcome:
Produced a structured performance summary showing where financial risk controls could be tightened.
Google Play Store Analytics
Business Question:
Which app categories consistently outperform others in downloads and ratings?
Dataset:
10,000+ applications
Approach:
Performed structured SQL analysis and trend segmentation across pricing, ratings, and download metrics.
Outcome:
Built dashboards highlighting high-growth categories and monetization patterns to support product strategy insights.
Certifications
1
IBM Data Science Professional Certificate
2
IBM Data Analyst Professional Certificate
3
SQL Associate — DataCamp
Let’s Connect
If you’re hiring a data analyst, I’d be glad to connect.
You can reach me via LinkedIn or email, or explore my work on GitHub.
Made with