Education

Formal Education

• Ph.D. in Zoology and Ecology, James Cook University — Cum laude (2024)
  • Skills & Tools: Bayesian and frequentist inference, hierarchical modelling, machine learning, predictive modelling, spatial analysis (GIS, remote sensing), automated data pipelines, data cleaning, R programming, reproducible workflows.
  • Data Types: Multi-source ecological, biological, climatic, physiological, and biogeochemical datasets (soil, foliage chemistry).
  • Applications: Developed novel modelling frameworks to predict species vulnerability to extreme events and identify high-risk habitats.
• M.S. in Biology and Conservation of Biodiversity, Universidad de Salamanca (2016)
  • Skills & Tools: GIS, advanced statistics, applied statistical modelling, R programming, spatial analysis, workflow automation.
  • Applications: Designed and executed analytical workflows for biodiversity monitoring and conservation planning.
• B.S. in Biology, Universidad de Salamanca (2014)
  • Skills & Tools: Mathematics, algebra, biostatistics, physics, introductory statistical programming, ecological modelling.
  • Applications: Undergraduate research project integrating environmental and ecological data.

Additional analytical, statistical, and coding training

Bayesian and hierarchical modelling

• Statistical Rethinking: A Bayesian Course with Examples in R and Stan
• Bayesian Methods for Ecology by Michael A. McCarthy
• Applied Hierarchical Modeling in Ecology
• Integrated Population Models
• ‘Statistics in R’ workshop with Dr Murray Logan (biostatistician at the Australian Institute of Marine Science)

Coding in R

• R for Data Science

Coding in Python

• The Complete Python Bootcamp: From Zero to Hero in Python (Udemy)
  • Completed a full-spectrum, hands-on course designed to take learners from absolute beginner to proficient Python developer.
  • Covered Python fundamentals through to advanced concepts — building a professional-level understanding of syntax, data types, control flow, functions, and Object-Oriented Programming (OOP).
  • Developed the ability to write clean, maintainable, and efficient code through practical projects, games, and automation scripts.
  • Explored Python’s core and advanced modules, applied automation techniques, and built end-to-end mini projects demonstrating professional workflows.
  • Core skills acquired:
    • Python fundamentals: variables, data types, operators, conditionals, loops, and functions.
    • Intermediate and advanced topics: decorators, debugging, error handling, regular expressions, and iterators.
    • Functional and Object-Oriented Programming: classes, inheritance, encapsulation, and modular code design.
    • Advanced modules and libraries: collections, datetime, os, math, random, re, and file handling.
    • Working with files, images, PDFs, CSVs, and automating file I/O tasks.
    • Web scraping with BeautifulSoup and Requests; automating data extraction.
    • Email automation and task scripting for productivity workflows.
    • Building and debugging in both Jupyter Notebooks and standalone .py scripts.
    • Creating interactive games and small applications (e.g., Blackjack, Tic Tac Toe).
    • Introduction to GUI development and interactive elements within Jupyter environments.
    • Applying Python to real-world scenarios: automating tasks, managing data, and developing shareable portfolio projects.
• Python for Data Science and Machine Learning Bootcamp (Udemy)
  • Completed an extensive, project-based program covering data science, analytics, and machine learning using Python. Developed practical fluency across the modern Python data ecosystem — from data wrangling and visualisation to supervised, unsupervised, and deep learning.
  • Built end-to-end analytical workflows integrating data preprocessing, feature engineering, model building, and evaluation. Applied advanced concepts such as bias–variance trade-off, dimensionality reduction, and distributed big-data processing.
  • Core skills acquired:
    • Data manipulation and numerical computing with Pandas and NumPy.
    • Data visualization and storytelling using Matplotlib, Seaborn, and Plotly (static and interactive visualizations).
    • Machine learning with Scikit-learn — building and evaluating models for:
      • Supervised learning: Linear Regression, Logistic Regression, KNN, Decision Trees, Random Forests, Support Vector Machines, Neural Networks.
      • Unsupervised learning: K-Means Clustering, Principal Component Analysis (PCA) for dimensionality reduction.
    • Recommender Systems and Natural Language Processing (NLP) (text cleaning, vectorisation, and spam detection).
    • Introduction to Deep Learning (neural network fundamentals, activation functions, and training workflows).
    • Big Data and Spark with Python — handling large-scale distributed datasets.
    • Model evaluation, cross-validation, bias–variance analysis, and performance optimisation for real-world applications.