Be an independent thinker!

An introduction to the indoor environment and the role of buildings and building systems from a human-centred perspective.

What the course covers

• The four categories of indoor environment — thermal, lighting and acoustic environments, and indoor air quality
• Systems of the human body
• Relationships between indoor environment and humans — comfort, productivity, stress, and health
• The roles of buildings and building systems

The course introduces state-of-the-art research on the interaction between humans and buildings, and puts energy on the table to address the importance of building energy efficiency and sustainability. Students also learn to set up environmental monitoring systems and analyse indoor environmental quality.

An applied introduction to probability, statistics and data analysis for civil and architectural engineering. Students learn to characterise data distributions, apply probability models to engineering design decisions, build regression and simulation models, and communicate uncertainty in building performance analysis.

Three 50-minute sessions per week — lecture, guided tool tutorial, and hands-on lab. Excel in weeks 1–8, Python and Google Colab in weeks 9–15.

What the course covers

• Characterise distributions — descriptive statistics, histograms, box plots, design percentiles
• Probability fundamentals — classical rules, conditional probability, Bayes' theorem
• Model with distributions — Poisson and Normal, return periods, design thresholds
• Compare empirical and parametric models for design applications
• Build and interpret regression models — t-tests, ANOVA, confounding
• Quantify uncertainty by simulation — Monte Carlo and bootstrap confidence intervals
• Communicate probabilistic design recommendations to a technical client audience
• Use professional analysis tools — Excel, and Python with pandas, scipy, statsmodels, matplotlib

Weeks 1–8 develop statistical foundations through Excel analysis of real climate and building energy datasets. Weeks 9–15 extend those foundations into predictive modelling, hypothesis testing, sensitivity analysis, simulation and uncertainty quantification in Python. The course closes with a capstone probabilistic design report and presentation, in which teams choose their own real-world civil or architectural engineering dataset.