Module GEOL40615: Key skills – Data Science

Department: Earth Sciences

GEOL40615: Key skills – Data Science

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• This module aims to introduce students to, and equip students with, a comprehensive set of essential skills that are crucial for conducting research and addressing complex environmental challenges. By exploring qualitative methods, statistical analysis, spatial information and GIS, AI, and multi-criteria techniques, students will develop the ability to collect, analyse, and interpret data effectively, and to apply these skills to various environmental contexts.

Content

• Qualitative Methods:
• o Introduction to qualitative research
• o Data collection techniques (interviews, focus groups, observations)
• o Data analysis methods (coding, thematic analysis, narrative analysis)
• o Ethical considerations in qualitative research
• Statistical Analysis:
• o Descriptive statistics (mean, median, mode, standard deviation)
• o Hypothesis testing
• o Statistical software (e.g., R)
• o Sampling strategies
• Spatial Information and GIS:
• o Introduction to geographic information systems (GIS)
• o Data acquisition and management
• o Spatial analysis techniques (buffering, overlay, interpolation)
• o Remote sensing and image analysis
• AI:
• o Introduction to artificial intelligence
• o Machine learning algorithms (supervised, unsupervised, reinforcement learning)
• o Deep learning techniques (neural networks, convolutional neural networks)
• o Applications of AI in environmental sustainability
• Multi-Criteria Techniques:
• o Introduction to multi-criteria decision analysis (MCDA)
• o Weighting and scoring methods
• o Decision-making frameworks (e.g., Analytic Hierarchy Process, ELECTRE)
• o Applications of MCDA in environmental planning and management

Learning Outcomes

• Demonstrate understanding of various qualitative research methods, statistical techniques, spatial analysis tools, AI algorithms, and multi-criteria decision-making frameworks.
• Be able to apply these methods and techniques to address environmental problems.
• Understand the ethical implications of research and data collection.

• Collect and analyse qualitative and quantitative data effectively.
• Use GIS software to manipulate and analyse spatial data.
• Apply AI techniques to solve environmental problems.
• Conduct multi-criteria decision analysis to evaluate alternative solutions.

• Critical thinking and problem-solving.
• Data literacy and analysis.
• Technical proficiency in relevant software.
• Effective communication and presentation skills.
• Teamwork and collaboration.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• This module will be delivered through a series of flexible 2 hour workshops comprising both lectures and practicals, supported by surgeries.
• The practicals form an important component of the module allowing "hands on" learning and experience.
• Summative assessment is made up of an in-class test, and a mini, data project based on topic of choice.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

The formative assessment consists of classroom-based exercises on specific data topics of relevant to the learning outcomes of the modules. Oral feedback will be given on a group and/or individual basis as appropriate.

■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.