A selection of academic projects completed during engineering school, spanning applied statistics, machine learning, data visualization, and web application development. Most were team projects; the summaries below focus on the problem space, methods, and tools used.
M2 Projects
Technologies: R, Statistical Modeling, Signal Processing
Analysis of eye-tracking data to study visual attention patterns from end to end: protocol design, data acquisition in Tobii Pro Lab, fixation metric computation, and statistical analysis in R.
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Technologies: R, Multivariate Statistics, ade4
Application of co-inertia analysis to study relationships between paired data tables with ade4 and related multivariate methods. The deliverable included both the statistical study and a xaringan slide presentation.
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Technologies: R, JavaScript, HTML/CSS
Development of interactive visualizations to communicate complex results more clearly, with emphasis on usability, narrative flow, and exploratory interaction.
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Technologies: R, Shiny, shinydashboard, ggplot2
Development of an interactive R Shiny application for data exploration and visualization, with dynamic filtering, reactive summaries, and user-facing plots.
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Technologies: Redis, Python, NoSQL
Implementation of a small Redis-backed data workflow to explore key-value operations, persistence, caching strategies, and real-time data handling from Python.
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Earlier Master’s Work
Technologies: Python, Scikit-learn, Pandas, NumPy
Course project completed during the first year of the Master’s programme. It covers the full ML pipeline: data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation across regression, classification, and ensemble methods.
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Skills Developed
Frameworks & Tools
- Shiny / shinydashboard
- Scikit-learn
- ggplot2
- R Markdown
Methods
- Machine Learning
- Statistical Modeling
- Multivariate Statistics
- Data Visualization
- NoSQL Systems
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