Data Analytics | Statistics | Finance & Business Insights
I am a BSc Statistics graduate with a strong interest in data analytics, financial analysis, and business reporting. I work with data to extract actionable insights, solve practical problems, and support decision-making in finance, audit, and business environments.
My workflow emphasizes data accuracy, analytical thinking, and clear communication of insights to both technical and non-technical stakeholders.
- Data extraction using SELECT and WHERE
- Table joins: INNER JOIN, LEFT JOIN
- Aggregations: GROUP BY, COUNT, SUM
- Basic subqueries and data validation
- Pivot Tables
- Lookup functions: VLOOKUP, XLOOKUP, INDEX-MATCH
- Logical and aggregation functions: IF, SUMIF, COUNTIF
- Data cleaning, filtering, and visualization
- Libraries: pandas, numpy
- Data visualization: matplotlib, seaborn
- Machine learning: scikit-learn
- Data cleaning and exploratory data analysis (EDA)
- Git and GitHub
- Basic web scraping
- Reporting and dashboard creation
- Performed customer segmentation using Recency, Frequency, and Monetary (RFM) analysis
- Identified high-value, loyal, and at-risk customers
- Translated analytical results into business-focused insights
Tools: Python, pandas, data visualization
- Built a predictive model to analyze key health indicators
- Applied data preprocessing, feature analysis, and model evaluation techniques
Tools: Python, scikit-learn
- Collected and analyzed online data using automated scripts
- Demonstrated data collection, scraping logic, and basic data pipeline skills
Tools: Python, Playwright
Additional projects are available in my GitHub repositories.
- Data Analyst / Junior Analyst roles
- Finance and audit analytics
- Banking, insurance, and consulting environments
SQL is used for data extraction and validation.
Excel and Python are used to analyze data and communicate insights effectively.