data scientist
In the making
I am a data science enthusiast who hopes to bridge the gap between computational methods and applicable results. I am primarily focused on developing AI solutions for healthcare and making these insights accessible to all. I am passionate about translating complex data into actionable insights that create meaningful societal impact.
About me
My journey in data science began with a leap of faith in high school when I chose to study computer science despite having no prior experience. That decision sparked a passion that led me to earn a bachelor's degree in Cognitive Science and Artificial Intelligence from Tilburg University.
Throughout my academic career, I've worked on diverse projects from predicting whale migration patterns using regression models to designing deep learning systems for audio analysis. My thesis research on how second language acquisition affects native language processing demonstrated the power of interdisciplinary approaches.
Currently, I am pursuing a Master's in Applied Data Science at Utrecht University, I'm developing expertise in Python, machine learning frameworks, and statistical analysis. My vision is to bridge the gap between raw data and clinical applications, particularly in neuroscience, where early detection of conditions like Alzheimer's could significantly improve patients' quality of life.
With experience in research and practical project implementation, I'm passionate about creating data-driven solutions that address real-world challenges, particularly in healthcare and environmental sustainability. I believe in developing technologies that are not only advanced but also accessible to everyone, contributing to a healthier society for future generations.
SKILLS
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I have completed a Bachelor's degree in Cognitive Science and Artificial Intelligence at Tilburg University with a strong academic performance, achieving a judicial average of 7.15. My coursework combined foundational programming skills (Python, data structures, algorithms) with advanced Al techniques including machine learning, deep learning, and computational linguistics. I have gained expertise in statistics through two sequential courses using R, and developed strong analytical capabilities through courses in knowledge representation, multi-agent systems, and autonomous systems. My interdisciplinary training combined with cognitive neuroscience, biological psychology, and language processing, culminating in a bachelor's thesis (grade: 7.5) that investigated bilingualism's effects on native listening abilities. Additionally, I have completed specialized courses in Al ethics, human-computer interaction, and applied my skills to environmental challenges through the "Artificial Intelligence for Nature and Environment" course, demonstrating both technical proficiency and awareness of Al's real-world applications.
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I am currently pursuing a Master's in Applied Data Science at Utrecht University, building upon my strong technical foundation with specialized courses. My program currently includes Data Wrangling and Analysis, where I have been mastering data retrieval from relational and non-relational databases using SQL, Python, and R, along with advanced techniques in data cleaning, feature selection, and supervised/unsupervised machine learning algorithms. In Epidemiology and Medical Data Science, I have been developing expertise in mixed models, missing data imputation methods, and individual participant data meta-analysis (IPD-MA) for medical research applications. The Text and Media Analysis course focuses on computational methods for digital communication research, including social media data mining, natural language processing, and visual media analysis applied to platforms like Instagram, TikTok, and YouTube. This interdisciplinary program uniquely positions me to apply data science techniques to real-world challenges in healthcare and digital media, combining technical rigor with practical applications across multiple
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Proficient in Python, SQL and R, with hands-on experience in writing code for data science anlaysis, machine learning models, and experimental analysis.
Skilled in using tools like Google Colab, Visual Studio Code, and Jupyter Notebooks.
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Hands-on experience building and evaluating machine learning models for classification and regression tasks.
Skilled in feature engineering, model selection, hyperparameter tuning, and interpreting results using tools like scikit-learn and TensorFlow.
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Strong analytical skills with a focus on extracting insights from structured and unstructured data.
Experienced in data cleaning, visualization, and statistical analysis using R, Python, pandas, and matplotlib.
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Practical experience with text preprocessing, tokenization, vectorization, and building NLP models for tasks like language modeling and text classification.
Familiar with multilingual data and tools such as NLTK, spaCy, and Hugging Face Transformers.
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https://www.datacamp.com/certificate/DSA0017632230473
This comprehensive certification covers a wide range of essential data science topics in Python, including everything from foundational data manipulation and advanced visualization techniques to machine learning concepts and hands-on projects. With this certification, I have gained the skills and knowledge necessary to excel in the increasingly competitive field of data science.
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During my gap year I took on website designing as a hobby and currently continue doing it freelance.
Some of my designs include www.kybeleinvest.com , www.sipandbite.capital ,www.babygreen.com.tr and https://www.psikologmisragurol.com/ -
Completed the ‘Introduction To Consumer Neuroscience And Neuromarketing’ on Coursera from Copenhagen Business School