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 | Data Science in Ecology and Environmental Sciences will teach quantitative skills including data management, data visualization, programming, simulation and statistical analysis. The course will teach about the field of data science and how it applies to the disciplines of ecology and environmental science. Students will learn about best practices in data science and will contribute to peer learning. Skills will be taught using an online problem-based learning approach and in class tutorials and discussions. |
| Course description |
Key skillsets in ecological and environmental sciences include quantitative skills such as data manipulation, data visualization, coding, statistics, simulation and more- together this skillset can be called data science. With a growing emphasis on the importance of data science in ecological and environmental fields, students are seeking out these quantitative skills for their current academic programmes including dissertation research and future careers.
Week 1: Version control and collaborative coding
Week 2: The basics of functional and object-oriented programming
Week 3: Development of workflows for quantitative analysis
Week 4: Data manipulation and organisation
Week 5: Data visualisation and graphics
Week 6: Big Data in Ecology and Environmental Sciences
Week 7: Statistics revision and the linear model/ An intro to hierarchical linear models
Week 8: An intro to Bayesian statistics
Week 9: Computing intensive research
Week 10: Careers in Data Science
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2019/20, Available to all students (SV1)
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Quota: 27 |
| 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 %
|
| Additional Information (Assessment) |
100% coursework:«br /»
Maintenance of individual online repository and peer feedback on other students' work- 20%«br /»
Weekly challenges (5% per challenge x 8 challenges) -40%«br /»
Development of a new tutorial- 40% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand key quantitaive skills in the disciplines of ecology and environmental sciences including data management, data visualization, programming, simulation and statistical analysis.
- Usa data science tools to address research questions and challenges in ecology and environmental sciences.
- Implement version control to back up work, code collaboratively and write reproducible workflow reports.
- Practice teaching quantitative skills and develop an online tutorial.
- Learn about the field of data science and future careers in this area.
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Reading List
http://ourcodingclub.github.io/
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Data Science,Ecology,Environmental Science,Coding,Programming,Version control,Statistics |
Contacts
| Course organiser | Dr Isla Myers-Smith
Tel: (0131 6)50 7251
Email: Isla.Myers-Smith@ed.ac.uk |
Course secretary | Mrs Nicola Clark
Tel: (0131 6)50 4842
Email: nicola.clark@ed.ac.uk |
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