Environmental Data Analysis
This course provides a theoretical and practical analysis and interpretation of data, including using statistical techniques, later evolving into diverse and innovative methodologies that provide a better understanding of environmental data.
At the end of this course the student will have acquired knowledge, skills and powers to:
- Understand and learn data collection techniques and visualization of environmental data;
- Be able to analyse data using complex statistical techniques;
- Apply innovative methods of research and interpretation of data including the use of artificial intelligence, also using applications that use environmental information for multiple purposes.
António da Nóbrega de Sousa da Câmara, Francisco Manuel Freire Cardoso Ferreira
Weekly - 4
Total - 56
- Berthouex, P. M. and L.C. Brown, 1994. Statistics for Environmental Engineers, Lewis Publishers, Boca Raton, 335 pp.
- Camara, A., 2002. Environmental Systems, Oxford University Press, New York.
- Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold, New York.
- Hsieh, W.W., 2009. Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels, Cambridge University Press, Cambridge.
- Montgomery, D.C. and Runger, G. C., 2018. Applied Statistics and Probability for Engineers, 7th edition, John Wiley & Sons Inc., New York,
- Moore, D.S., G.P. McCabe and B. Craig, 2016. Introduction to the Practice of Statistics, 9th edition, W.H. Freeman and Company, New York,
The teaching method is supported by lectures and practical classes. The teaching methods are mainly conducted to support several aspects: a) individual/group technical and scientific skills; b) debate skills and coherent analysis in the interpretation of the studied subjects. Classes are complemented with a tutorial system, using e-learning tools.
The course evaluation is made through a test (25% weighting in the final grade) and two group works on environmental statistics component (25% weighting in the final grade) and a set of small group work and individual projects covering the remaining areas, based on guided readings and available internet software have to solve a problem with the environmental field (50% weighting of the final grade). It is necessary that the weighted average of the different components is equal to or greater than 9.5.
Probabilities, Random Variables and Their Distribution, Important Discrete and Absolutely Continuous Distributions, Central Limit Theorem, Point and Interval Estimation, Hypothesis Testing, Sampling, Data Visualization, Linear Regression, Analysis of Variance; Principal Component Analysis, and Time Series.
Methods of data processing
Typology of environmental and health data; public and / or consumer-generated data sets; uni, bi, tri and multidimensional data visualization systems; augmented and virtual reality systems; Methods for knowledge discovery based on data sets; Methods based on neural networks; Software systems: data, logic and presentation layers; Other methods of data analysis using artificial intelligence: genetic algorithms; genetic programming; Applications in the environment: access to environmental data, infrastructure localization, ecosystem modelling, ecological design, events and cities.
Programs where the course is taught: