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Arda Üzer

About

Data Scientist & Business Analyst in Warsaw, Poland. Transforming complex data into meaningful insights through analytics, statistical modeling, and machine learning.

01About

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. 01

    Frame

    Translate a business question into a measurable analytical problem with explicit assumptions.

  2. 02

    Explore

    Clean, profile and visualize the data to understand its structure, quality and limits.

  3. 03

    Model

    Apply statistical and machine learning methods, engineer features and validate the results.

  4. 04

    Communicate

    Turn findings into clear visuals and recommendations that stakeholders can act on.

02Expertise

A toolkit for the full analytical cycle.

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

  • Python01

    Analysis, machine learning, notebooks

  • R02

    Statistics, modeling, visualization

  • SQL03

    Querying, joins, aggregation

AData Science & Machine Learning

  • Exploratory Data Analysis
  • Statistical Modeling
  • Machine Learning
  • Unsupervised Learning
  • Feature Engineering
  • Data Mining
  • Dimensionality Reduction

BLibraries & Frameworks

  • Pandas
  • NumPy
  • Scikit-learn
  • ggplot2
  • dplyr
  • arules

CVisualization & Tools

  • Matplotlib
  • Seaborn
  • Jupyter Notebook
  • RStudio
  • Git
  • GitHub

See the work, or start a conversation.

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