GRS-35306 Data Science for Smart Environments

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Studiepunten 6.00

OnderwijstypeContacturen
Individual Paper20
Lecture16
Practical56
Course coordinator(s)dr. L Grus
dr. ir. A Ligtenberg
Lecturer(s)dr. S Kloppenburg
dr. ir. A Ligtenberg
dr. L Grus
dr. I Athanasiadis
LW de Vos
Examiner(s)dr. L Grus
dr. ir. A Ligtenberg

Language of instruction:

EN

Assumed knowledge on:

Data Science Concepts or equivalent. Python knowledge is preferred

Continuation courses:

Thesis

Contents:

New sources of data available from all kind of ‘smart technologies’ such as sensors, tracking-devices, crowd sourcing and social media open possibilities to create information and gain knowledge about our environment beyond that what is possible with ‘traditional’ sources of data. Especially analyses of spatial-temporal processes and interactions between people and their environment are accelerated by these new sources of data. Examples are the movements of people (tourists) through a city and the consequences for its accessibility or the perception of people about certain places.
The drawback is that these data often comes in high volumes, are often ill structured, and often are collected with a different purpose than that of environmental analyses. This means that (pre) processing, analyses, and visualization of such data requires specific skills. This includes, for example skills to create meaningful patterns from the data by applying (spatial) classification and clustering techniques, or applying sentiment and topic analyses techniques on for example social-media data. Knowing how to visualize these often-complex type of data is essential to effectively share and communicate the outcomes of analyses.
Moreover, making sense of these data and transform it to information useful for design, participation, decision-making and governance processes requires a critical attitude and good knowledge about the quality of the data, as well as critical reflections on the social and political implications of using smart technologies in environmental policy and decision-making. This course will pay ample attention to societal aspects such as citizen engagement in data gathering, ethical questions around big data and automation, and implications of using smart technologies on social and power relation in (urban) environmental policy. 
To successfully follow this course knowledge about modern data-science concepts and techniques such as treated in Data Science Concepts (INF-xxxxx) or a data science minor is assumed.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- understand the specific aspects of applying data-science for the environmental science domains;
- evaluate the quality and understand the limitations of data-sources from ‘smart technologies’;
- design procedures to solve an information need using data-science and visualization techniques;
- extract meaningful patterns/knowledge and synthesize it in an appropriate way such that is can be understood and used within an environmental design or planning process;
- apply appropriate data visualization techniques to complex environmental data;
- develop an attitude of responsibility by reflecting on the societal implications of using smart technologies and big data.

Activities:

This course is set-up around a so-called atelier concept. In this ateliers groups of students work jointly on a data-science project. The groups are preferably composed of students with different skills. Besides the goal of the project each student defines individual learning goals and indicates how he/she will accomplish these. The goals depend on the background, interest and skills of an individual student. At least one learning goal related to learning outcome 3, 4, and 5, should be present  These goals are formulated in the first week and will be discussed, aligned and fine-tuned during group meeting jointly with one of the supervisors.
Prior to the course each student carries out a formative assessment (a quiz) to identify the current knowledge and knowledge gaps. This information is input for both the supervisors and student for formulation of the personal learning goals and group formation.
Apart from the atelier, presentations and workshops will be provided to offer students the necessary knowledge needed to carry out essential data-science task within the environmental science domains. The presentations and workshops are focused on:
- acquiring and wrangling data;
- enriching and analyzing data;
- social and ethical aspects of data;
- visualizing and communicating data.

Examination:

- project report/portfolio;
- personal portfolio and reflection meeting.

OpleidingFaseSpecialisatiePeriode
Keuze voor: MEEEarth and EnvironmentMSc5AF
MFNForest and Nature ConservationMSc5AF
MESEnvironmental SciencesMSc5AF
MGIGeo-Information ScienceMSc5AF
MTOTourism, Society and EnvironmentMSc5AF
MCLClimate StudiesMSc5AF