Marketing Data Analyst | Specializing in insights-driven growth, segmentation, and predictive analytics.
I’m a Marketing Data Analyst with a strong foundation in Python, SQL, and data visualization tools like Streamlit and Dash. I specialize in extracting actionable insights from complex data to drive customer segmentation, performance optimization, and marketing strategy.
My expertise spans ETL pipelines, product clustering, revenue forecasting, and real-time analytics, leveraging tools such as Scikit-learn, Pandas, and Apache Spark. I build full-stack analytics solutions from data modeling in PostgreSQL to interactive dashboards designed for business decision-making.
I'm currently completing a Master’s in Data Analytics (NFQ L9) at CCT College Dublin. My academic and freelance projects focus on real-world marketing use cases, including e-commerce segmentation, stock price forecasting using sentiment, and AI-driven visual enhancement. With a background in software development and business analytics, I’m passionate about creating data-driven marketing solutions that scale.
A full-stack marketing analytics pipeline simulating a real-world e-commerce business case. Using Brazil’s Olist marketplace dataset, I built a PostgreSQL database, engineered KPIs, and performed product segmentation using PCA + KMeans. The result is a Streamlit dashboard that enables strategic decisions for marketing and inventory optimization.
Skills: PostgreSQL, Docker, ETL (Pandas), PCA, KMeans, Streamlit, Plotly
This project forecasts 1-, 3-, and 7-day stock prices by combining sentiment analysis from Twitter with market data. It uses statistical models (ARIMAX, SARIMAX) and deep learning models (LSTM, GRU), all powered by a scalable big data architecture.
Built on a Lambda architecture with Apache Spark and HDFS, it benchmarks four databases and delivers real-time predictions via a Streamlit dashboard.
Skills: PySpark, HDFS, Spark NLP, ARIMAX, LSTM, GRU, YCSB, Dashboarding
This Master’s project compares two AI models SRCNN and Real-ESRGAN for enhancing retro game visuals by reconstructing pixel details. These models upscale low-res images while reducing storage needs producing images up to 10× smaller in file size.
A big data pipeline using Hadoop HDFS and Apache Spark enabled scalable processing of game screenshots and high-resolution training data (DIV2K).
Skills: PyTorch, HDFS, Spark, OpenCV, Image Upscaling, PSNR/SSIM, Neural Networks
This project focuses on analyzing production-related factors that influence agricultural export values in Ireland. It employs statistical and predictive modeling techniques to uncover trends and provides actionable insights for optimizing production and pricing strategies.
Skills: Data analysis, predictive modeling, dashboard development, CRISP-DM methodology
Deploy the Dashboard View on GitHub
This project applies clustering techniques to an e-commerce dataset to segment customers based on their behavior. Customer segmentation enables businesses to tailor marketing strategies, improve customer engagement, and increase overall profitability.
Skills: Customer Segmentation, Clustering
View on GitHub
Designed and normalized a relational database schema for employee management, achieving 3NF. Wrote advanced SQL queries for data retrieval, aggregation, and manipulation to support operational analysis.
Skills: Database normalization, SQL (joins, views, stored procedures), ER modeling
View on GitHub
CCT College Dublin
Sep 2024 - Jun 2025
Skills: Advanced Data Analytics, Programming for Data Analytics, Predictive Modeling, Machine Learning, Research and Professional Ethics, Big Data Storage and Advanced Data Analysis.
CCT College Dublin
Sep 2023 - Jun 2024
Skills: Strategic Thinking, Machine Learning, Statistical Techniques for Data Analysis, Data Preparation, Data Visualization Techniques, Machine Learning for Business.
CCT College Dublin
Sep 2022 - Jun 2023
Grade: Distinction
Skills: Java, HTML, CSS, JavaScript, MySQL, Agile Methodologies.
Access my CV to see a concise summary of my skills, experience, and achievements.
View CV
