# Arda Oğulcan Üzer — Data Scientist & Business Analyst > Transforming complex data into meaningful insights through analytics, statistical modeling, and machine learning. Warsaw, Poland · Open to Data Science, Data Analytics and Business Analysis opportunities in Poland and across Europe. ## About ### Engineering rigor, applied to questions that matter. I’m a data scientist and business analyst with a foundation in computer engineering. My work sits where rigorous analysis meets practical decisions: exploring data carefully, building statistical and machine learning models, and explaining what the results mean for the people who need to act on them. Engineering taught me to break problems down, question assumptions and build solutions that are reproducible. I bring the same discipline to analytics — from data cleaning and feature engineering to model selection, validation and interpretation. I’m currently completing my MSc in Data Science & Business Analytics at the University of Warsaw, with a particular interest in statistical modeling, unsupervised learning and machine learning applied to real business questions. - **Focus:** Statistical modeling · Machine learning · Business analytics - **Background:** Computer Engineering (BSc), Izmir University of Economics - **Currently:** MSc Data Science & Business Analytics, University of Warsaw - **Based in:** Warsaw, Poland ### How I approach a problem 1. **Frame** — Translate a business question into a measurable analytical problem with explicit assumptions. 2. **Explore** — Clean, profile and visualize the data to understand its structure, quality and limits. 3. **Model** — Apply statistical and machine learning methods, engineer features and validate the results. 4. **Communicate** — Turn findings into clear visuals and recommendations that stakeholders can act on. ## Expertise From querying and cleaning data to modeling, interpretation and visualization — the languages, methods and tools I use to move from raw data to insight. ### Programming & Query Languages - **Python** — Analysis, machine learning, notebooks - **R** — Statistics, modeling, visualization - **SQL** — Querying, joins, aggregation ### Data Science & Machine Learning - Exploratory Data Analysis - Statistical Modeling - Machine Learning - Unsupervised Learning - Feature Engineering - Data Mining - Dimensionality Reduction ### Libraries & Frameworks - Pandas - NumPy - Scikit-learn - ggplot2 - dplyr - arules ### Visualization & Tools - Matplotlib - Seaborn - Jupyter Notebook - RStudio - Git - GitHub ## Projects ### Anime Preference Analysis with Association Rules *Unsupervised learning · Data mining* An unsupervised learning project in R that applies the Apriori algorithm to anime rating data to discover which titles tend to be liked together — the same idea behind market basket analysis and recommendation systems. - Modeled each user as a transaction and each highly rated title (rating ≥ 8) as an item - Focused on the 200 most popular titles to keep the discovered rules interpretable - Mined association rules with arules and explored them visually with arulesViz **Stack:** R, arules, arulesViz, dplyr, Association Rule Mining, Data Mining **Links:** [Code](https://github.com/ardaogulcanuzer/anime-association-rules) ### Coffee Sensory Analysis with PCA & MDS *Dimensionality reduction · Statistical analysis* An exploration of the multivariate structure of Arabica and Robusta coffee sensory scores — aroma, flavor, acidity, body and more — using correlation analysis, Principal Component Analysis and Multidimensional Scaling. - Examined the correlation structure between sensory attributes - Used the scree plot and variable loadings to interpret the principal components - Compared Euclidean and Manhattan MDS to see how the distance measure shapes the result **Key finding:** The first principal component explains about 58.8% of the variance, and the projections show a noticeable separation between Arabica and Robusta samples. **Stack:** R, PCA, MDS, ggplot2, dplyr, factoextra, Statistical Analysis **Links:** [Code](https://github.com/ardaogulcanuzer/dimension-reduction) ### ATP Tennis Player Similarity Analysis *Similarity analysis · Feature engineering* Turns match-level ATP data (2000–2025) into player-level performance profiles and uses cosine similarity to identify comparable players — with SQL for storage and aggregation, and an interactive Streamlit dashboard for exploration. - Engineered player-level features by season and surface from match records - Stored and aggregated the data in SQLite and computed similarity with scikit-learn - Built a Streamlit dashboard to explore similar players interactively **Key finding:** Similarity depends strongly on season and surface — single statistics alone are not enough to describe comparable players. **Stack:** Python, SQL, SQLite, Pandas, Scikit-learn, Cosine Similarity, Streamlit **Links:** [Code](https://github.com/ardaogulcanuzer/tennis-player-similarity) · [Report (PDF)](https://github.com/ardaogulcanuzer/tennis-player-similarity/blob/main/Tennis%20Similarity%20Project.pdf) ### Symptom Evaluation System *Software engineering · Digital health* · Team project · co-authored IEEE paper Undergraduate final thesis: a web and mobile application that supports communication between patients and healthcare professionals. Patients book appointments and report symptoms with severity levels and comments, so doctors can review them before the visit. - Cross-platform client for web and mobile built with Flutter - REST API service written in Go - Presented at the 2023 Medical Technologies Congress (TIPTEKNO 2023), with an IEEE publication **Stack:** Flutter, Go, REST API, Web & Mobile Development **Links:** [IEEE paper](https://ieeexplore.ieee.org/document/10359191) · [App](https://github.com/ardaogulcanuzer/sep_app) · [API](https://github.com/ardaogulcanuzer/sep-app-api) ## Contact - Email: [ogulcanuzer@gmail.com](mailto:ogulcanuzer@gmail.com) - LinkedIn: [in/ardaogulcanuzer](https://linkedin.com/in/ardaogulcanuzer) - GitHub: [@ardaogulcanuzer](https://github.com/ardaogulcanuzer) - Website: [ardauzer.com](https://ardauzer.com) --- **Pages:** [Home](https://ardauzer.com/) · [About](https://ardauzer.com/about/) · [Contact](https://ardauzer.com/contact/) · [Privacy](https://ardauzer.com/privacy/) · [Terms](https://ardauzer.com/terms/)