I am building a data analyst portfolio from an 8-year background in tech media and content, with current graduate study in Applied Statistics. My focus is business analysis, clear communication, and practical data work that turns raw evidence into decisions.
Status: portfolio-ready case study with cleaned README, notebook, executive summary, and dataset.
- Goal: Identify likely drivers of employee turnover and recommend practical retention actions.
- Tools: Python, pandas, scikit-learn, XGBoost
- Techniques: exploratory data analysis, feature engineering, logistic regression, XGBoost classification, model evaluation
- Artifacts: Project README | Jupyter Notebook | Executive Summary | Dataset
Status: planned.
- Goal: Uncover revenue trends, cohort retention, and business insights from a raw transactional dataset.
- Planned tools: SQL, PostgreSQL or DuckDB
- Planned techniques: CTEs, window functions, date logic, cohort analysis
Status: planned.
- Goal: Design an executive-level KPI dashboard with clear metric definitions and a short business insight memo.
- Planned tools: Tableau or Power BI
Status: planned.
- Goal: Apply hypothesis testing and effect size evaluation to a business decision.
- Planned tools: Python or R
- Planned techniques: statistical inference, confidence intervals, practical decision memo
- Python analysis: HR retention capstone with EDA, feature engineering, and model evaluation.
- Machine learning: logistic regression and XGBoost classification on an HR attrition problem.
- Excel analysis: Macquarie University Excel for Data Analysis coursework.
- Statistics: Applied Statistics master's coursework in progress.
- Master of Applied Statistics (MAS) - Candidate
- Google Advanced Data Analytics Professional Certificate - View Credential
- Excel Data Analysis: Mini-projects from the Macquarie University Excel for Data Analysis course, covering formulas, lookup functions, pivot-style analysis, and dashboard basics. View files
- GitHub: @Ospeto