Ian Wood

• Merged my past culinary experience with current data science interest
• Executed comprehensive aggregation of diverse datasets from the Michelin Guide, INSEE, and geospatial sources.
• Developed advanced data processing and visualisation techniques, including the development of an interactive application at www.michelin-guide-france.net using Dash and Plotly.

This project illustrates proficiency in data manipulation to provide actionable insights into the intersection of high-end gastronomy and economic patterns.

• Employed pattern matching and regex for species classification within a large, open-source categorical dataset.
• Developed a robust visualisation pipeline to synthesize complex information across parks and states.
• Formed insightful visualisations and case studies, including a focus on the Californian Condor, a critically endangered species.

This study demonstrates a sounds application of data analysis techniques and use of complex data structures.

• Analysed time-series data from credible sources probing trends of life expectancy and GDP across nations.
• Applied statistical analysis to track the evolution of life expectancy, visualizing trends and anomalies.

This initial foray into data analysis was instrumental in shaping my approach to visual storytelling, emphasizing the impact of well-crafted visualisations in unravelling narratives.

• Derived equations of motion symbolically using Lagrangian mechanics which are abstracted as a series of dependent functions.
• A simple conditional logic structure controls which model is derived.
• Optimisation strategies include pre-computation of positions and vectorised operations.

This application offers both an in-depth view into the physics of chaotic systems and an engaging visual representation of the double pendulum's unpredictable behavior.

• Robust string parser translates plain-text equations into computable expressions.
• Classifies conics automatically from their general equation, enabling smooth transitions between forms.
• Records a detailed transformation history for traceability and educative purposes.
• Intuitive visualisation tools in the class allows users to see and interact with conic sections graphically.

Accompanied by accessible, low-code tutorials that highlight linear algebra operations promoting interactive learning..

• BiVarPoly: Supports bivariate polynomials, enabling partial differentiation, stationary point analysis, and advanced plotting options like surface and contour plots.
• TriVarPoly: Extends functionality to tri-variate polynomials, suitable for even more complex algebraic forms and multiple partial derivatives.

Designed to address the complexity of polynomial representations in standard libraries. Detailed documentation supports new programmers in understanding the computational processes involved.