Materials Engineering
The beginning of my academic path and an introduction to engineering thinking, problem solving, and structural analysis.
About me
About meAn interdisciplinary path with a clear direction
From engineering and health sciences to data analysis and machine learning: a path shaped by curiosity, research, and problem-solving.
A short introduction
I'm Ali, a researcher and learner on the path to data science.
My work sits at the intersection of data analysis, health, nutrition, and sports physiology.
My academic background in materials engineering and health sciences has taught me to approach problems from several angles: What is the structure of the problem? What does the data say? And how can the result support a more precise decision?
I am now developing my professional skills along the pathData Analyst → Data Scientist → Machine Learning, and this site documents the projects, research, and real experiences that shape that journey.
View projectsEducation
Three fields, one problem-oriented perspective
Bachelor's in Sports Nutrition
A deeper move into health, nutrition, and the factors that shape performance and well-being.
Master's in Sports Physiology
A focus on nutrition, research, and closer examination of physiological responses and health data.
Research interests
Questions I continue to explore
- Analyzing data and turning it into explainable insight
- Machine learning with health data
- Nutrition, obesity, fatty liver disease, and sports physiology
- Research design and transparent evaluation of results
- Automating repetitive workflows with n8n
- Deploying and presenting data-driven projects
Working principles
Accuracy before presentation
My goal is to present an honest picture of my skills and experience. Every project should clearly explain its problem, method, limitations, and result.
Open to collaboration
Start a conversationIf you have data or a problem, let's talk about it.
I am open to research collaborations, data analysis projects, and automation ideas, starting with a careful look at the problem and a practical path forward.