Welcome to my GitHub!

I'm a data scientist with a foundation in Finance and Economics. When I'm not working, you’ll find me enjoying music 🎶, skiing 🎿, hiking 🥾, and exploring the great outdoors 🏔️.

Feel free to explore my projects, learn more about my background, and get in touch as I'm always keen to expand my skill set.

• 💼 6 years of professional experience in the biotechnology (startup), educational and green energy sectors.
• 🎓 Background in economics and finance (undergraduate) and a master’s in Business Administration and Data Science.
• 💡 Always driven by curiosity, I'm constantly seeking to leverage data science for innovative solutions and continuous growth.
• 🌍 Fun fact: I hold triple citizenship — Australian 🇦🇺, Polish 🇵🇱, and American 🇺🇸 — bringing a global outlook to everything I do.

• CNNs Multi Class Brain Tumor Detection

  • This project explores the use of Convolutional Neural Networks (CNNs) for classifying brain tumors from MRI images. As part of the Machine Learning & Deep Learning course at Copenhagen Business School
  • You can find the code and accompanying paper here

• VizTracks - Full-stack web app to visualise your entire Spotify history.

  • A full-stack web app that transforms users' entire Spotify listening history into animated insights you can share in a social-media-friendly format.
  • It combines a Streamlit frontend with a Flask + Gunicorn backend, processes uploads into a session-isolated DuckDB database, and renders Matplotlib animations colored by album art palettes.
Frames are streamed to a GPU-accelerated FFmpeg (NVENC) service for fast, smooth encoding.
  • You can find the code in the GitHub Repository
  • Try the app: www.viztracks.com

• Spotify Listening History - Interactive Dashboard:
  This project involves analysing my personal Spotify listening history data to gain insights into my listening patterns.

  • You can find the code in the GitHub Repository.
  • You can explore the interactive visualisations and insights through the Tableau Dashboard.

• E-Commerce Tracking System:
  This project involves a Python based e-commerce tracking system that can track and analyse key metrics of an e-commerce company

  • You can find the code in the GitHub Repository.

• Programming Languages: Python, SQL, R
• Data & ML: NumPy, Pandas, Polars, Scikit-learn, PyTorch, Keras, TensorFlow, spaCy, NLTK
• GenAI & LLMs: LlamaIndex (RAG), Azure OpenAI, pgvector
• Visualisation: Matplotlib, Seaborn, Plotly, Tableau, Power BI, Quarto
• Web Frameworks: Flask, Streamlit, Shiny
• Databases: PostgreSQL (pgvector), MySQL, DuckDB, Supabase
• Data Engineering: ETL pipelines, data warehousing, AWS S3, REST APIs
• DevOps & Deployment: Docker, Kubernetes (AWS EKS), GitHub Actions (CI/CD), Fly.io, Gunicorn
• Version Control: Git, GitHub, Azure DevOps
• IDEs: VS Code, Jupyter, RStudio, Google Colab

• 📧 Email: mxbernard54@gmail.com
• 🔗 LinkedIn: linkedin.com/in/maxwell/bernard/