The walls between art and engineering exist only in our minds -- Theo Jansen
After years of dreaming, planning, and more self-nudges than I’d like to admit, this project is finally live! This website is my step out of my comfort zone—an attempt to share my blend of the technical, the reflective, and the personal journey I’ve been on for years. Here, I’m sharing more than just insights; I’m sharing pieces of my journey and lessons that I’ve taken with me along the way.
Communicating uncertainty in energy data is crucial for making informed decisions, as it highlights the range of possible outcomes rather than presenting a single, potentially misleading value. By using techniques like confidence intervals and prediction bands, alongside visualizations such as error bars and fan charts, we provide a clearer picture of data variability. This approach ensures stakeholders understand the limitations of the data, leading to more resilient and accurate decision-making.
Pandas, NumPy, SQL, Geopandas, Shapely, scikit-learn, scipy
Matplotlib, Seaborn, Plotly, Folium, Grafana
Mathematical Modeling, Predictive Analytics, Statistical Testing
Docker, Travis, CircleCI, GitHub
PySpark, Databricks