Undergraduate Course: Data Science in Ecology and Environmental Science (ECSC10038)
Course Outline
| School | School of Geosciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | We live in an increasingly data driven world, and knowing how to manage, visualise, analyse and interpret data is a key skill for ecological and environmental scientists. In this course, we give a crash course in the kinds of skills a student will need to take off in a data driven career. We focus our teaching around ecological and environmental science problems, but much of what we cover will be applicable in a wide range of disciplines. |
| Course description |
Data Science is a huge field, so our goal in this course is to give a taster of core skills. We cover coding; data wrangling, management and visualisation; statistical analysis; and some fun extras such as dash-board creation and working with Google Earth Engine. The final assessment is a Tutorial on a topic of the student¿s choice, allowing them to develop additional skills in the direction they find most interesting.
We place a heavy emphasis on the importance of critical thought in data science ¿ statistics can be easily manipulated, and many ¿objective¿ facts contain a lot more nuance than meets the eye. Our aim in this course is to help students learn how to think about numbers, and to not be scared by statistical representations. This critical thinking will extend to AI ¿ we will discuss when it is and isn¿t useful, and consider best practice for AI assisted coding. Students are allowed to use AI to support assessment, but our in-class sessions are an AI free zone ¿ consider it gym training for your brain.
In terms of coding languages, we teach mostly in ¿R¿ ¿ the preferred coding language of ecologists and environmental sciences. Students will also learn markdown and Git, and be introduced to JavaScript/Python in the Google Earth Engine session.
Online framework and flipped classroom
The course will be hosted online through the GitHub platform, used by professional data scientists, allowing for version control, reproducible workflows and submission of coding-based assignments (https://github.com/). Data science is learnt by doing, so we have a heavy emphasis on active engagement. Each week, class is taught in one three-hour session, consisting of an introductory lecture, then workshop time (sometimes individual, sometimes small groups) for students to engage in the content and practice their own work, supported by the teaching team. Students will also have access to ¿Data Camp¿ tutorials, linked to each week, which they are strongly encouraged to complete to develop their skills and keep up in class.
We will cover:
1. Functional and Object-Oriented Programming (how to use R usefully)
2. Version control (how to make sure you don¿t accidentally lose useful code)
3. Data Manipulation (how to rearrange your data into the format you want)
4. Data Visualisation (how to produce graphics that display your data beautifully and communicate messages effectively)
5. Statistics 1: Linear Models (a refresher on linear models)
6. Statistics 2: Generalised Linear Models (what to do if you break the assumption of normality?)
7. Statistics 3: Mixed Linear Models (what to do if you break the assumption of independence?)
8. Statistical Philosophy (is that result really saying what you think it is?)
9. Google Earth Engine (how to take advantage of this amazing resource to do spatial analyses and work with satellite data)
10. Dashboards (how to create interactive displays of your data)
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | This is a fourth-year honours level course; students are expected to have an academic profile equivalent to the first three years of this degree programme. Assessment of eligibility for honours level courses will be made on an individual basis.
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| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
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Quota: 40 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 22,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
174 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Feedback |
Students will receive peer feedback during the formative assessment, plus there will be regular opportunities for feedback from the teaching team during in-class sessions. Additionally, a forum within the class Github allows for students and staff to communicate, ask questions and discuss topics, providing more space for informal feedback. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand key quantitative skills in the disciplines of ecology and environmental sciences including data management, data visualization, programming and statistical analysis.
- Use data science tools to address research questions and challenges in ecology and environmental sciences.
- Critically evaluate statistical analyses and reports for reliability and bias.
- Implement version control to back up work and write reproducible workflow reports.
- Practice teaching quantitative skills and develop an online tutorial.
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Reading List
| Please see course outline on the Learn Page for a full breakdown of weekly reading requirements. |
Additional Information
| Course URL |
https://datascienceees.github.io/ |
| Graduate Attributes and Skills |
Not entered |
| Keywords | Data Science,Ecology,Environmental Science,Coding,Programming,Version control,Statistics |
Contacts
| Course organiser | Dr Hannah Wauchope
Tel:
Email: hwauchop@ed.ac.uk |
Course secretary | Miss Francesca Nadal Finnegan
Tel: (0131 6)50 4842
Email: Francesca.Finnegan@ed.ac.uk |
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