6 EC
Semester 1, period 3
5264ANML6Y
Timely science supporting our future planet requires well developed data handling, analysis and modelling skills. We therefore offer a modular course where students can develop these skills in the field of their choice.
Modules from which can be made a choice:
These modules are all illustrated with examples from earth science and ecology.
Manual
Data
Exercises
By the time the course is completed, the student is able to:
Each module is started with an introductory lecture to explain the matter. Students work on the modules during scheduled computer lab sessions.
|
Activity |
Number of hours |
|
| Lectures |
16 |
|
| Self-study |
152 |
|
| Total |
168 |
|
The programme does not have requirements concerning attendance (OER-B).
| Item and weight | Details |
|
Final grade | |
|
25% Module 1 | Must be ≥ 5.5 |
|
25% Module 2 | Must be ≥ 5.5 |
|
25% Module 3 | Must be ≥ 5.5 |
|
25% Module 4 | Must be ≥ 5.5 |
During this course ten modules are available. The student has to select four modules, according to their field of interest. At the end of each week the module will be graded by handing in a project, presenting the results or by a written exam. Each module weights 25% for the final grade.
The ability to write your own computer programs, and understand those of others, gives you more flexibility when analyzing and understanding data; in particular, MatLab is widely used to perform data calculations and manipulations in scientific and engineering work. With this module you will acquire basic MatLab programming skills needed for manipulating data, writing scripts, and visualizing results in graphs, figures and movies. As you learn, you will apply the MatLab skills to short research projects focused on erosion, pollution, flood simulation, drainage patterns, and other environmental problems.
Simulation modelling is frequently used in scientific research to explore and analyse a system that may be too complex or risky to work with in the real world (eg. weather and climate, transportation, pollution). This module will teach you how to create simulation models in MatLab, so you should already have experience in MatLab programming. You will work with a model designed to calculate rainfall interception by a forest canopy using ordinary differential equations and forward integration. Practical MatLab exercises will also teach you to model how temperature fluctuations at the soil surface propagate in deeper soil. You will also build a simulation model that describes heat flow through soil using MATLAB and learn to model diffusion processes in two dimensions.
Inverse modelling is a technique that is used to improve hydrological, environmental and ecological models by calculating causal factors from a set of observations. This module introduces you to inverse modelling methods such as HYMOD and DREAM. You will learn to perform model calibrations, and visualize and interpret model results. You will complete practical exercises in MatLab designed around case studies in hydrology, rainfall interception and population dynamics, so you should already have experience in MatLab programming.
Basic programming skills are useful or even required when managing data and performing statistical analyses, and the free/open source R programming environment is becoming an increasingly popular tool. The aim of this course is to introduce you to R using textbooks, online resources, and the R program itself. You will perform basic calculations in R, use pre-defined functions, install and load packages, produce graphs, and write simple scripts. At the end of the course, you will work on a case study which will demonstrate that you have a foundation in R programming upon which you can continue to build independently.
Statistics is the science (and art) of learning from data, and experts in many fields use statistics to understand and analyze large data sets. In this module the emphasis is on statistical thinking by teaching key concepts, and applying them to case studies in R. This module covers basic probability, descriptive statistics, hypothesis testing (2 means or proportions, association among two variables, goodness-of-fit), analysis of variance, and simple and multiple regression. This module focuses on conceptual understanding and interpretation, and assumes that you have no experience with R.
Statistical learning encompasses many methods, including regression modelling, which are used to make predictive models from complex data sets and systems, and for exploring cause-effect relationships between variables. The focus here is on understanding the techniques at a conceptual (non-mathematical) level. You will complete practical exercises in R, so you should already have basic R programming and statistics skills.
Classification techniques (logistic regression, linear discriminant analysis and K-nearest neighbors) are the models which are primarily used when predicting categorical outcomes (e.g. species distribution modeling, land use classification medical applications). Hands-on exercises in this module will teach you to use R to develop and fit classification models, and compare and experiment with different modelling techniques, data sets and methods. By the end of the module, you will be able to apply what you have learned by creating your own R classification models.
The complexity and size of the data used in science and engineering today has created a demand for researchers with advanced computational and analytical skills. Future scientists will need to have working knowledge of a core set of computational approaches in order to work with data efficiently and effectively in both scientific and professional tasks. In this introductory module, the focus is on practical computational skills applicable to real-life problems and on conceptual understanding of important technologies including Git, the command line, regular expressions, databases and SQL.
Land use is a reflection of interactions between humans and their environment. Through land use modelling, researchers can make help society to improve agricultural practices, design more sustainable cities, better manage natural resources, and promote biodiversity. This module aims to increase your knowledge about different land use models and their practical applications, and gain practical experience with land use modelling and geographic information systems software. Hands-on exercises will require you to apply statistical methods to land use change analysis, explore data sets, and create simulations of land use change using case studies. In the final exercises, you will create and test your own model application, and compare and validate results of different land use models.
Matrix population models are an important analytical tool to explore the patterns of kinship and family structures of some kinds of animals: primates, whales, some birds, and social insects in particular. In this module you will work directly with Prof Hal Caswell, an expert in population models for plants, animals, and humans. You will work with customized material providing instruction in basic population modelling theory and the application of that theory, especially to evolutionary questions and to environmental questions related to climate change.
The student selects a total of four modules from this list.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
The schedule for this course is published on DataNose.
Basic knowledge on statistics and mathematics as can be expected from students with the BSc in Earth Sciences, Biology, Environmental Sciences or Future Planet Studies.