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monbiote/README.md

Hi πŸ‘‹, I'm Ed Monbiot

I am a Data Scientist skilled in Python, R, and SQL, passionate about deriving insights from data. Currently based in Barcelona, I have extensive experience in hedge funds and electronic trading roles in London, where I worked on demanding, time-sensitive software projects using Python and SQL. I am graduating in July 2024 from the Barcelona School of Economics with a master's degree in Data Science for Decision Making.

My GitHub portfolio showcases a wide range of data science projects, both solo and collaborative, demonstrating expertise across Machine Learning, Natural Language Processing, Data Visualization, and Deep Learning.

I am actively seeking new a data-focused role and I am eager to apply the cutting-edge skills honed during my degree, coupled with my extensive work experience in Fintech, to tackle your data challenges.

About Me

  • 🌍 Location: London, UK
  • πŸ“š Education:
    • MSc in Data Science for Decision Making, Barcelona School of Economics (2023-2024)
    • BEng in Mechanical Engineering, Trinity College Dublin (2011-2015)
  • πŸ“ˆ Professional Experience:
    • Trading Support Engineer at Caxton Associates (2019-2023)
    • Technical Project Manager at Traiana (2016-2019)

Technical Skills

  • Programming Languages & Libraries:

python pandas numpy scikit-learn Matplotlib - Python plotting library seaborn Keras - Deep Learning API tensorflow r selenium

  • Database Technologies:

sql mysql MongoDB Logo pyspark

  • Cloud Computing:

aws

  • Data Visualisation:

tableau

  • Development Tools & Environments:

jupyter Anaconda VS Code Git

  • Large Language Models (LLM):
    • BERT
    • OPENAI API

Master's Thesis

Used state-of-the-art large language models to build a cacophony index for the European Central Bank (ECB). Objective was to measure the discordance of ECB Governors, as perceived by the media, against official ECB communications.

Summary of Findings

  • First Contribution to Research: Application of advanced LLMs to a novel dataset comprising media coverage on central banking. Sentiment analysis was applied to 30,000 media articles from July 2022 to March 2024 using a fine-tuned RoBERTa model, with 78% of the articles classified as "hawkish".
  • Second Contribution to Research: Analysis of the dataset after applying LLMs. An interquartile range (IQR) approach was used to measure the diversity of opinions among ECB Governing Council members. The National Bank Governor from Germany was consistently seen as the most hawkish, whereas Southern European governors from Portugal, Greece, Italy, and Spain were more dovish.

Data Projects

Explore my repositories below for a detailed look at my work, where each project comes with a README that details the objectives, techniques used, and insights uncovered.

Get In Touch

I'm open to discussions and collaborations on data science, financial technology, or any related fields.

Pinned Loading

  1. India_Used_Car_Price_Predictor India_Used_Car_Price_Predictor Public

    Utilising Python, this project evaluates various machine learning models to predict second-hand car prices in the Indian market.

    Jupyter Notebook

  2. NLP_CEO_CFO_Sentiment_Analysis NLP_CEO_CFO_Sentiment_Analysis Public

    Forked from mtbrr26/Natural_Language_Processing_project

    This Python project analyzes sentiment in CEO and CFO earnings calls to guide investors on whose insights to prioritize during economic crises.

    Jupyter Notebook

  3. ICU_Length_Of_Stay ICU_Length_Of_Stay Public

    In Python, this project utilizes neural networks and ensemble methods to construct a predictive model for estimating the length of a patient's stay in the Intensive Care Unit.

    Jupyter Notebook

  4. Music_Store_SQL Music_Store_SQL Public

    This SQL project performs an in-depth analysis of a music store database, providing insights into customer behaviors, sales patterns, and music trends through a series of strategically crafted quer…

  5. WWII_Aerial_Bombing_Operations WWII_Aerial_Bombing_Operations Public

    The purpose of this R project is to conduct a comprehensive analysis of World War II aerial bombing operations, uncovering patterns, strategies, and impacts through data-driven insights.

    Jupyter Notebook

  6. Youth_Player_Football_Position_Predictor Youth_Player_Football_Position_Predictor Public

    Forked from arimiche/football_projectML

    The project's goal is to develop a classification model to accurately recommend the best playing positions for youth football players

    Jupyter Notebook