MSc Data Analysis for Business Intelligence · Data Analyst · Manchester, UK 🇬🇧

I'm a data analyst with an MSc in Data Analysis for Business Intelligence (University of Leicester), passionate about turning complex datasets into clear, actionable insights.

• 🔍 Specialising in time series analysis, clustering, data migration, and business intelligence dashboards
• 🗄️ Strong SQL skills (intermediate–advanced) — joins, window functions, CTEs, query optimisation, stored procedures
• ⚡ Currently learning PySpark on Databricks (Azure) — building big data processing skills
• 🤖 Recently exploring AI agents and their real-world applications in social impact
• 📊 Comfortable working across the full analytics pipeline — from raw data to polished visualisation
• 🌍 Based in Manchester, UK — open to hybrid and remote roles
• 💼 Actively seeking Data Analyst roles

Languages & Libraries

Visualisation & BI

Tools & Platforms

MSc Dissertation · University of Leicester

Analysed 2 years of FTSE 100 price data using time series clustering and PCA to uncover hidden stock behavioural patterns. Applied KMeans, Hierarchical, and DBSCAN clustering with full validation metrics.

Python yfinance Scikit-learn PCA KMeans DBSCAN Hierarchical Clustering Time Series

Power BI · IEA Open Data

Interactive dashboard visualising global electric vehicle adoption trends — sales, stock, and market penetration across countries and years using IEA open data.

Power BI Data Visualisation IEA Data Business Intelligence

Tableau · Python · Sales Analytics

Analysed 6 months of coffee shop transaction data to uncover revenue drivers, peak trading hours, and category performance. Built an interactive Tableau dashboard enabling management to plan promotions and resource allocation.

Key findings: £504K total sales · 10 AM peak hour · Espresso drinks top category · Morning hours drive ~35% of daily revenue

Python Pandas Tableau Data Cleaning Sales Analytics Dashboard

SQL · MSSQLSERVER · SSMS · Business Intelligence

End-to-end SQL analysis of Brazil's Olist e-commerce dataset (2016–2018) covering 97,919 orders. Investigated sales trends, delivery performance, and customer satisfaction to surface actionable business insights.

Key findings: 76% on-time delivery · 4/5 avg review score · Electronics & beauty drive ~35% of revenue · 15% repeat customer rate

SQL MSSQLSERVER SSMS Data Analysis E-commerce Business Intelligence

AI Agents · Social Impact

Multi-agent AI pipeline that assesses risk and generates personalised action plans for vulnerable individuals — integrating postcode lookup, service recommendations, and HTML/PDF plan generation.

Python Google ADK AI Agents pdfkit Social Impact

Python · Exploratory Data Analysis · Healthcare

In-depth exploratory analysis of the UCI Heart Disease dataset (920 records, 14 variables) to identify clinical and demographic risk factors across four disease severity levels — including univariate, bivariate, and multivariate analysis with a full visualisation suite.

Key findings: Severity rises sharply after age 60 · Max heart rate strongly inversely correlated with severity · ST depression is the most predictive feature

Python Pandas Matplotlib Seaborn EDA Healthcare Analytics

Python · Data Wrangling · Pipeline Design

Professional-grade data cleaning pipeline for a real-world Brazilian healthcare appointments dataset. Standardised schema, corrected datatypes, handled datetime formatting with timezone awareness, and produced an analysis-ready dataset for downstream EDA, ML, and BI workflows.

Python Pandas Data Cleaning Data Engineering Healthcare

I'm actively looking for Data Analyst roles — if you're hiring or just want to chat data, feel free to reach out!