Data Analysis Portfolio
Welcome 👋
Here, you’ll find projects I’ve built using a diverse toolkit, tackling interesting problems with datasets from Kaggle,Real World Data, or fictional data created by AI. All projects are available for exploration on GitHub. Feel free to click on the project titles to dive deeper into my work!
Tools:
- MySQL (database design, querying, data validation, data manipulation)
- Power BI (data modeling, DAX, dashboard development)
- Excel (data preparation, normalization)
Data & System Focus:
- Relational data design and structured data modeling
- Query-based data transformation (SQL)
- Data quality assurance and consistency checks
- Integration between database systems and reporting tools
Key Implementation
- Built a structured data pipeline from Excel to a relational MySQL database and connected it to Power BI
- Performed data validation, normalization, and consistency checks
- Developed SQL queries for calculated fields (e.g. sales, profit)
- Created a dashboard for monitoring key metrics and data quality
Structure: COLAB > BigQuery > Looker Studio
Keywords: Python (pandas, yfinance, pandas-gbq), SQL (BigQuery Standard SQL), Data Modeling, Cloud ETL, Financial Analytics, Looker Studio (Dashboard)
- End-to-End Process: Developed a full cloud-based analytics pipeline from data ingestion in Google Colab to SQL modeling in BigQuery, using live financial and FX data to simulate a 3-year investment portfolio.
- Core Focus: Applied advanced SQL for time-series portfolio valuation, performance attribution, and benchmark comparison directly in BigQuery.
- Practical Application: Designed a dynamic portfolio tracking system calculating daily holdings, PnL, and returns in EUR; integrated with Looker for KPI and performance visualization.
- Analytical Insights: Delivered investment-level performance metrics versus the S&P 500, demonstrating diversification effects and showcasing SQL-driven financial data modeling.
Keywords: Python (pandas, matplotlib, seaborn), Exploratory Data Analysis (EDA), Data Visualization, Business Case Study
- Capstone Project: Created for the Google Data Analysis Certificate, it follows six phases: Ask, Prepare, Process, Analyze, Share, and Act.
- In-depth Analysis: Examined wellness data from Fitbit devices, identifying patterns through exploratory data analysis (EDA) techniques.
- Actionable Insights: Provides recommendations for business cases, leveraging data-driven insights to optimize product offerings and user experiences.