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amberwalker-ds/README.md

Hello 👋, I'm Amber!

I'm an aspiring Data Scientist currently studying at the Barcelona School of Economics, set to graduate with a Master's degree in July 2024. With a solid foundation in Python, R, and SQL, I am passionate about leveraging data to drive insights and innovation. I discovered my passion for the technical side of projects while working as a project coordinator at a company specializing in automation technology. Throughout my academic career, I have tackled numerous projects, both solo and collaboratively, sharpening my skills and deepening my understanding of the theory, the math, and the application of data science. I'm excited to bring my skills to a dynamic company with a meaningful mission, where I can continue to grow and make a tangible impact. Always curious and constantly learning, and committed to advancing in the field of data science beyond graduation!

About Me

  • 🌍 Location: Barcelona, Spain
  • 🎓 Education:
    • MSc in Data Science, Barcelona School of Economics (2023–2024)
    • BBA in Marketing, The University of Texas at San Antonio (2018–2021)
  • 📝 Professional Experience:
    • Project Coordinator at AVRL (2021–2023)
      • Led a team to develop and implement automation solutions for 3PL companies and assisted the Director of Operations in improving operational efficiency.
      • Coordinated cross-functional projects, ensuring timely delivery and adherence to quality standards.
  • 💼 Skills:
    • Programming Languages: Python, R, SQL
    • Data Analysis and Visualization: Pandas, NumPy, Matplotlib, Seaborn
    • Machine Learning and Neural Networks: Scikit-learn, TensorFlow, Keras, PyTorch
    • Natural Language Processing: NLTK, SpaCy, Gensim,Transformers (Hugging Face)
    • Database Management: MySQL, PostgreSQL
    • Tools and Technologies: Git, Jupyter Notebooks, Google Collab
  • 🚀 Projects:
    • Developed and implemented a forecasting model to predict the onset of a Peace Agreemnt signing in countries
    • Collaborated on a project using sentiment analysis to analyze the relationship between earnings calls and stock prices
  • 🌱 Learning and Growth:
    • Continuously enhancing my skills through courses, workshops, and hands-on projects (DataCamp, Kaggle)
    • Passionate about applying data science to solve real-world problems and contribute to meaningful initiatives

Pinned

  1. Advanced_NLP_project Advanced_NLP_project Public

    This project involves analyzing and classifying the BoolQ dataset from the SuperGLUE benchmark. We implemented various classifiers and techniques, including rules-based logic, BERT, RNN, and GPT-3/…

    Jupyter Notebook

  2. Exploring-Advanced-Financial-Modeling-Techniques Exploring-Advanced-Financial-Modeling-Techniques Public

    Forked from claricemottet/MLF_HW1

    This project evaluates various methods for financial modeling and prediction using the EWMA-based variance, causality analysis, and predictive analysis of the S&P 500. We employed techniques such a…

    Jupyter Notebook

  3. forecasting_peace_agreements forecasting_peace_agreements Public

    Using the PA-X Database and LDA from news articles, this project predicts the onset of peace agreements in conflict zones to aid timely and informed decision-making by international stakeholders.

    Jupyter Notebook

  4. NLP-Sentiment-Analysis-of-Earnings-Call-Transcripts NLP-Sentiment-Analysis-of-Earnings-Call-Transcripts Public

    Forked from mtbrr26/Natural_Language_Processing_project

    This project examines sentiment shifts in CEO and CFO communications during economic crises. We analyzed earnings call transcripts from four companies, applying sentiment analysis and regressing se…

    Jupyter Notebook

  5. Parliamentary-Speech-Analysis-Using-Structural-Topic-Modeling Parliamentary-Speech-Analysis-Using-Structural-Topic-Modeling Public

    This project involves an analysis of parliamentary speeches made by UK House of Commons legislators in 2014. I focus on how speech topics vary by the gender of the MP using structural topic modelin…

    HTML

  6. Semantic-Segmentation-of-Building-Footprints-Using-U-Net Semantic-Segmentation-of-Building-Footprints-Using-U-Net Public

    This project performs semantic segmentation using a U-Net model to extract building footprints from aerial images

    Jupyter Notebook