Undergraduate Course: Earth Science Data Analysis 1 (EASC08028)
Course Outline
| School | School of Geosciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 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 a cornerstone of careers in Earth and environmental science today. This course will develop underpinning pre-calculus mathematical competencies and introduce fundamental programming and data analysis skills in the geosciences. Mathematics will be taught within a data analysis and coding context, rather than focusing on manual calculation. Spreadsheet, GIS, and Python coding skills will use a mix of resources to allow students to progress at their own pace.
This course is NOT available as an option course, and is only available to students on the Earth Sciences Degree Programmes. |
| Course description |
Focus will be placed upon ensuring students: (i) Have the fundamental pre-calculus mathematics skills required for Earth and environmental scientists; (ii) Have a common foundation of spreadsheet skills (taught using Excel), GIS skills (taught using QGIS), and Python coding skills (taught using Jupyter notebooks and online resources); (iii) Use the collaborative coding tools in the Noteable service to encourage peer support. The course will: (iv) Provide the skills to allow students to analyse a wide range of datasets; (v) Encourage a quantitative and critical analysis of Earth Science data and processes.
The methods taught will be of general utility to all degree streams (Earth Sciences, Geology and Physical Geography, and Environmental Geoscience). Components of practicals will necessarily include specific applications, but the applications will be balanced across the degree streams. Emphasis will also be placed on the generalisation of methods taught (e.g. reading and visualising data, determining tractable questions, being critical of how well data can answer a specific question).
The course will be delivered through two 2-hour combined lecture and practical sessions per week, incorporating both the mathematical concepts and coding techniques.
Topics covered include:
Meaningful measurements: units, dimensions, significant figures, and scientific notation
Statistics: measuring the distribution of data, correlations between variables, propagation of error
Trigonometry: working with triangles on a plane and on a sphere, modelling cyclical behaviour
Modelling growth and decay: exponentials, logarithms, and asymptotic behaviour
Working with geospatial data: map coordinates, representing point, vector, and area data on a map
Fitting models to data, determining a suitable model, optimisation
What questions can my data answer, and how well? Determining tractable questions, understanding randomness and uncertainty
The course will include compulsory online coding exercises, using online resources such as DataCamp. These will allow students to progress at their own pace and will provide a strong foundation in coding skills. The Jupyter notebooks used will then provide Earth science specific applications. These will be primarily worked through in class and completed thereafter. The course will emphasise how to find and use existing tools rather than re-inventing the wheel, e.g., using online documentation, google, etc. to work out how to use functions, mimicking real world practice.
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 |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: 87 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 54,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
142 )
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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
Assessment 1: Group Project with Peer Moderation 40%
Assessment 2: Individual Project 40%
Completion of core online Python material 20% |
| Feedback |
Formative feedback will be provided during the practical sessions by both academic staff and postgraduate demonstrators. Each project group will have a specific feedback session at both the planning stage andon the final project. Feedback on the individual project will also be provided during practical and the weekly drop-in sessions. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply fundamental mathematical methods to solve geoscientific problems and implement those methods using a range of software.
- Conduct a statistical analysis of data, including choosing suitable measures of distribution of data and correlation between variables, using Excel, Python, and QGIS.
- Import, visualise, and analyse geospatial data using Python and QGIS.
- Fit simple models to data, assessing goodness of fit, uncertainties, and the underlying assumptions and limitations of the model.
- Identify scientifically relevant questions that can be answered with a particular dataset, and identify what additional data might be needed for other questions.
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Additional Information
| Graduate Attributes and Skills |
Research and Enquiry: Apply a range of mathematical methods to geoscience problems and implement those analyses in a spreadsheet, GIS package, or python. Use critical and analytical thinking to interpret and assess the results of data analysis, including goodness of fit, an understanding of uncertainties or ambiguities, and the limitations and assumptions of the underlying model or analysis.
Personal and Intellectual Autonomy: Determine appropriate mathematical, geospatial, and software/coding tools to work with a range of common geoscience data and perform routine analysis. Independently use publicly available resources to learn new coding skills, software tools, or analysis techniques.
Personal Effectiveness: Work as a team to design and manage a project, dividing work according to individual strengths and interests. Adapt project plans to emerging results and setbacks.
Communication: Choose and design effective visualisations for a range of geoscience data, including geospatial data. Use a mix of text, visualisations, images, and mathematical analysis to effectively communicate the research questions, data types, analysis methods, results, and limitations for a geoscience problem. |
| Keywords | Data handling,data visualisation,data analysis,coding,GIS |
Contacts
| Course organiser | Dr Mark Naylor
Tel: (0131 6)50 4918
Email: Mark.Naylor@ed.ac.uk |
Course secretary | Mr Johan De Klerk
Tel: (0131 6)50 7010
Email: johan.deklerk@ed.ac.uk |
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