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Computational Sustainability

Course developers

Prior knowledge

Citi1015, Fundamentals of Sustainable Development

Mate2010, Discrete Mathematics

Course abstract

The aim of the course is to give insights into tasks and solutions in multidisciplinary computation. Computational Sustainability course introduces computational models, methods and tools to support various disciplines in decision support and development of more effective policies for sustainable development. In this course students will identify common problems and computational solutions that can provide support solving for an effective resource management, improving access to education and developing digital literacy. Course introduces solutions including systems analysis and modelling, data processing and visualization, optimization algorithms, and statistical analysis.

Learning outcomes and their assessment

Knows about computational models, methods and tools for decision support for solving various interdisciplinary issues – assessment with practical assignments.
Have skills to analyse interdisciplinary field and find solutions using information technologies – assessment with practical assignments.
Aquire competences to individually analyse data and find reasoned information technology solution – assessment with students’ independent works in each model.

Course Content(Calendar)

Module: Business Analytics Tools for data collection and visualization. [Lectures - 4h, Practical works - 4h]
1. Introduction to Business Analytics Tools. Microsoft Power BI. Import data into Power BI. MS Excel as Power BI data source.
2. Data pre-processing. Creating a data model. Model calculations using DAX.
3. Creating reports with Power BI. Advanced data analysis with Power BI.
4. Interactive and transparent data visualization. Pie chart, line chart, map, and KPI visuals. Interactive graphs, complex data analysis.

Module: E-learning technologies. [Lectures - 6h, Practical works - 6h]
1. What is e-learning - tasks, types, benefits.
2. E-learning planning and development - principles of instructional design, storyboarding, multimedia elements, interactive content.
3. E-learning platforms and tools - learning management systems, authoring tools, web conferencing tools, mobile learning technologies.
4. Knowledge assessment - types of tests, tests, performance measurement, feedback.
5. Emerging Trends in e-Learning - gamification, virtual and augmented reality, artificial intelligence, personalized learning.
6. E-learning project management - project planning, time and cost management, collaboration with stakeholders, quality assurance.
Module: Data, Tools, and Processes for Computing Sustainability Outcomes [Lectures - 6h, Practical works - 6h]

1. The Notion of Sustainability [Lectures - 2h]
Ecosystem services (ES). Categories of ES. Ecosystem services assessment methods. Provisioning, Regulating, Supporting, and Cultural services. Functions and benefits of ecosystem services. The ecosystem services cascade framework. Ecosystem service values and valuation methods. Stakeholder dialog. SCRUM.

2. Geospatial Information Infrastructure [Lectures - 2h, Practical works - 2h]
Interoperability. Service-Oriented Architecture (SOA). Spatial Data Infrastructure (SDI). Infrastructure for Spatial Information in the European Community (INSPIRE). Web Map Services (WMS).

3. Data Sources and Tools [Lectures - 2h, Practical works - 4h]
What is open data? Creative Commons licenses. European Data Portal Licensing Assistant. The 5-star Open Data concept. Machine-readable formats. Metadata harvesting. Data sources. CKAN API. Tools. Python and Google Colab.

Requirements for awarding credit points

Test. All practical work must be passed.

Description of the organization and tasks of students’ independent work

Students prepare independent work in each model.

Criteria for Evaluating Learning Outcomes

The mark of the test depends on the cumulative assessment of the semester. The works performed within each module are evaluated with 10 points.
The study course is passed if the average mark of modules is above 6 points.

Compulsory reading

1.Kirk A. Data visualisation: a handbook for data driven design. Los Angeles: SAGE, 2019. 312 p.
2. Nash S., Rice W. Moodle 4 E-Learning Course Development: The definitive guide to creating great courses in Moodle 4.0 using instructional design principles, 5th Edition. Packet Publishing, 2022. 436 p.
3. Chavez C. Adobe Photoshop Classroom in a Book 2024 Release. Adobe Press, 2023. 416 p.

Further reading

1. Corr L., Stagnitto J. Agile Data Warehouse Design: collaborative dimensional modelling, from Whiteboard to Star Schema. UK: Decision Press, 2014. 304 p.
2. The Accidental Instructional Designer, 2nd Edition: Learning Design for the Digital Age Paperback. Association for Talent Development, 2023. 288 p.
by Cammy Bean (Author)
3. Šmits A. Digitālā fotogrāfija. Adobe Photoshop Lightroom lietotāja rokasgrāmata. Rīga, Zvaigzne ABC, 2011. 320 lpp.

Periodicals and other sources

1. Power BI: Microsoft. Pieejams: https://www.microsoft.com/es-es/power-platform/products/power-bi

Notes

Bachelor (undergraduate) level study program “Information Technologies for Sustainable Development”