👋 Hi, I’m Donatien Konan — Machine Learning and Data Science | Exploring Scientific Applications
Here’s a breakdown of my education:
- 🎓 Data Engineering Bootcamp @ Le Wagon (Paris)
- 🎓 MSc in Data Science @ CentraleSupelec / OpenClassrooms (Paris)
- 🎓 MSc in New Technologies for Energy — specializing in storage materials and characterization @ Université de Tours (France)
- 🎓 Maîtrise in Chemistry @ Université Félix-Houphouët-Boigny (Ivory Coast)
Together, they form the foundation of my approach: bridging fundamental science, data intelligence, and robust engineering to accelerate R&D — especially in energy and industrial applications.
- 🔬 Deep knowledge of battery physics & chemistry :
- materials
- aging
- performance modeling
- lab characterizations (e.g., CV, EIS, cycling tests, galvano)
- 📊 Skilled in data science :
- Python
- Scikit-Learn
- Predictive Modeling (Deep Learning / Machine Learning)
- ⚙️ Build production-grade data pipelines :
- Airflow
- Spark, dbt
- Docker
- FastAPI
- CI/CD
- 📈 Turn complex lab/field data into actionable insights :
- Plotly
- Streamlit
- SQL, PostgreSQL, ClickHouse DB
I thrive where science meets software — in R&D environments that value rigor, reproducibility, and impact.
- 📬 Let’s collaborate:
- 🧑 Pronouns: He/Him
- 🔗 Projects: github.com/donat-konan33
