Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Explores the propagation of uncertainty in correlated variables and extreme correlations, Tchebychev inequality, confidence intervals, and Taylor series development.
Covers the analysis framework for evaluating life cycle impacts, emphasizing climate change, environmental interventions, impact categories, and uncertainty in environmental data interpretation.
Explores uncertainty analysis in Life Cycle Assessment, covering sensitivity, probability functions, parameter estimation, pedigree approach, and uncertainty propagation.
Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.
