Undergraduate Course: Earth Science Data Analysis 2 (EASC08033)
Course Outline
| School | School of Geosciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Geoscientific modelling and data analysis are critical components of many geoscientific studies and are cornerstones of careers in Earth and environmental sciences today. This course will develop programming and data analysis skills in the geosciences. Students will be introduced to geoscientific modelling, and will have hands-on experience with an array of computational tools used in geoscientific study. |
| Course description |
This course will build on the framework of ESDA1 and introduce the students to computing, mathematical modelling and data analysis in Earth and environmental sciences. Focus will be placed upon: (i) Gaining confidence in programming and computing with Python; (ii) Quantitative analysis of geoscientific data and processes; (iii) Awareness of uncertainty; and (iv) Transferability of skills learned to more specialised applications in honours years. Types of geoscientific data considered will include meteorological, bio/geochemical, topographical, and glaciological data, among others.
Through the use of Jupyter Notebooks, students will build on the foundational material of ESDA1 to learn more sophisticated methods of data analysis and computation. The notebooks will also form a computational/numerical 'toolbox' to build on in honours years. The methods taught will be of general utility to all degree streams (Earth Science, Geology and Physical Geography, and Environmental Geoscience). Components of practicals will necessarily focus on specific applications but the applications will be balanced across degree streams. Emphasis will also be placed on the generalisation of methods taught (e.g. reading large data files; visualising 3D and 4D data; simulation of rate processes).
The course will be practical-based, with the lecturer leading with demonstrations and discussing the approaches and material throughout the practical.
Topics covered include:
i) General descriptive and relational statistics
ii) Calculus of one and several variables, e.g., calculating rates of change, areas, and volumes
iii) Mathematical modelling of geoscience processes, e.g., ordinary differential equations and simulation of chemical diffusion, cooling of an igneous intrusion, or fluid flow around a well
iv) Time series analysis, e.g., separating components of a signal, trend detection, interpolation/extrapolation, filtering, upsampling/downsampling
v) Spatial analysis, e.g., working with geospatial data, coordinate systems, application of filtering, interpolation, sampling in 2D, 3D, and 4D (including time)
Where possible, the use of widely used python libraries will be introduced (e.g. the scipy library will be used for statistical and mathematical modelling). This will emphasise the skills necessary in modern geo-data analysis and geoscientific computing: how to locate the tools needed and adapt their use, rather than building computational tools anew.
The course will recognise that students will enter the university with different mathematical backgrounds. Assessments will be designed to test strengths over a range of research skills that aid in modelling, data analysis, synthesis, and presentation, as well as mathematical skills.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Earth Science Data Analysis 1 (EASC08028)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | It is recommended that students have passed ESDA 1 or the equivalent at the discretion of the Course Organiser. |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: 120 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 44,
Seminar/Tutorial Hours 10,
Other Study Hours 40,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
102 )
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| Additional Information (Learning and Teaching) |
There will be 40 hours of group work.
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assessment will be 100% coursework.
20% assessed practicals
40% Group data analysis project. Students will be provided a dataset containing actual geoscientific data (e.g,. a combined physical and chemical ocean dataset) and asked to carry out analysis in groups. The groups will work together on coding, analysis and visualisation but each group member will hand in their own ~1000 word interpretation of results.
40% Individual data analysis project. Students will choose from one of a selection of datasets which is provided from across the ES degree along with some suggestions for investigation. Hand-in will consist of data analysis code and write-up.
All three components need to be passed independently (40% or above). |
| Feedback |
Formative feedback will be provided in the course via practicals. This will be provided by a combination of academic staff and postgraduate tutors. Summative feedback will be provided via written comments from the coursework assessments. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Use open-source computational packages for scientific investigation and data analysis, choosing appropriate packages and techniques for different tasks
- Manage and manipulate large data sets for the purpose of modelling and data analysis
- Visualise 3- and 4-dimensional data sets for interpretation and presentation
- Explain how physical problems involving rates, areas, and volumes translate to equations and how these equations can be solved with a computer
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Additional Information
| Graduate Attributes and Skills |
KNOWLEDGE AND UNDERSTANDING: Demonstrate a critical understanding of a range of the principles, principal theories, concepts and terminology concerning geoscientific data analysis.
PRACTICE: APPLIED KNOWLEDGE, SKILLS AND UNDERSTANDING: Apply knowledge, skills and understanding in using a range of the principal skills, techniques and practices associated with code development, data analysis, and dealing with data uncertainties
NUMERACY SKILLS: Presentation and expression of quantitative information derived from raw data; Demonstrate knowledge of the best format in which to handle data for a given problem, and how to transfer data between different formats; Practice of techniques in applying mathematical formulae to large data sets
AUTONOMY AND AWARENESS: Demonstrate ability to choose appropriate analysis for a given problem, and ability to lead in analysis to address high-level questions without strong guidance of low-level details; show awareness of deeper meaning and robustness of analysis results |
| Keywords | Data handling,data visualisation,data analysis,coding,GIS,modelling |
Contacts
| Course organiser | Dr Lara Kalnins
Tel:
Email: lkalnins@exseed.ed.ac.uk |
Course secretary | Mr Johan De Klerk
Tel: (0131 6)50 7010
Email: johan.deklerk@ed.ac.uk |
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