Uses
Last updated: 2025-08-17
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Data & ML
- Core languages: Python, SQL, R, JavaScript; basics in PHP, Java, C++.
- Python stack: pandas, NumPy, scikit-learn, TensorFlow, Keras, PyTorch; Jupyter.
- NLP & GenAI: classical NLP workflows; local LLM workflows with Ollama / Hugging Face (on-device where possible).
- Audio / Retrieval: CLAP embeddings + FAISS for vector search and offline recommenders (music/audio).
- Quantum / Optimization: Qiskit for quantum K-Means and experimentation.
- Experimentation: notebooks first; quick scripts packaged to jobs when stable.
Analytics & BI
- Dashboards & viz: Tableau, Power BI, matplotlib/plotly.
- Analytics: SQL-first analysis, KPI design, cohorting, experimentation (A/B), funneling.
- Stat packages: SPSS when needed.
Data Engineering & Orchestration
- Pipelines / scheduling: Apache Airflow.
- Distributed compute: Apache Spark (PySpark & Scala), MapReduce.
- Ecosystem: Hadoop / HDFS.
- Data modeling: dimensional modeling; dbt (learning basics).
Databases & Storage
- SQL engines: PostgreSQL (when available), MySQL, SQLite for quick local work.
Cloud
- Platforms: Google Cloud Platform, Microsoft Azure.
- Typical services: storage, compute, managed SQL/warehouse; infra kept minimal and cost-aware.
Dev & Infra
- Versioning: Git & GitHub.
- Containers: Docker & Kubernetes (practical basics).
- Editors & IDEs: Jupyter Notebook, VS Code, PyCharm, Spyder, CodeBlocks.
- Project tracking: Jira.
- Docs: Markdown.
Web / App
- Frontend: HTML/CSS/JS.
- Web/General: HTML/CSS/JS, Markdown, VS Code.
Profiles & links
- Portfolio (Google Sites): https://sites.google.com/view/philippeguerrier/
- GitHub Pages: https://philippe-guerrier.github.io/
- LinkedIn: https://www.linkedin.com/in/philippe-guerrier1/
- GitHub: https://github.com/Philippe-Guerrier
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