Undergraduate Course: Biology 2A: Data Exploration in Biology (BILG08024)
Course Outline
| School | School of Biological Sciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course will involve interactive class teaching with coding and data analysis. Students will be taught data science using example datasets, and this learning will be reinforced by application to student-selected datasets from a variety of biological disciplines. |
| Course description |
This course aims to give students the skills to work with biological datasets to present, summarise and explore patterns in a wide range of datasets using python, pandas and seaborn. We will take the student through guided examples, supported exploration and on to independent work. For each course topic, student will apply the concepts they have learned to complex research datasets. Student groups will choose a 'real world' dataset to work with at the start of the course, with each being taken from a broad range of biological disciplines to ensure that students can select a topic with which to develop and test hypotheses. The course will be taught in workshops with short lectures introducing the topic then group practise with example and complex datasets using Jupyter notebooks.
Topics covered include: Cleaning and organising datasets gathered in field or lab or downloaded; Summarise data series using descriptive statistics; Explore datasets to look for associations and inter-group differences; Use appropriate data summaries and visualisations for the presentation of data analyses; Collate information from many groups and discuss data analysis results in a biological context.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% in course assessment |
| Feedback |
A course survey will be undertaken towards the end of every semester. Mid-semester
feedback will also be sought. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand and critically evaluate data collection methods, appreciating ethical considerations, scientific integrity and sources of bias
- Write computer scripts to load, filter, visualise, and analyse data sets
- Use appropriate data science techniques to interpret biological data within a chosen area of modern biology
- Document and justify analytical workflow and present data-driven biological findings
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Additional Information
| Graduate Attributes and Skills |
Curiosity for learning: The course will provide strategies for enquiry and lifelong learning through teaching skills for
independent data analysis.
Aspiration and personal development: We will support the development of a reflective approach to learning whilst building personal
responsibility using the assessed portfolio. This will include reflection on problem solving.
Research and Enquiry: The main focus of the course is developing research and enquiry
skills. The course teaches the biological and societal context of data collection, analysis and
presentation to ensure the students contribute positively, ethically and respectfully.
Communication: Students will learn how to communicate thoughts, ideas and discoveries
clearly and concisely. Communication skills will be taught in relation to group work and the
formal poster presentation.
Technical and Practical Skills: Computing skills will be developed.
Personal Effectiveness: Students will develop skills in recording information in a way that
captures the key points; field trips and practicals will help with this.
Personal and Intellectual Autonomy: The course requires both individual and group work
which enhances personal and intellectual autonomy and personal effectiveness. |
| Keywords | datasets |
Contacts
| Course organiser | Dr Catherine Kidner
Tel: (0131 6)51 3316
Email: Catherine.Kidner@ed.ac.uk |
Course secretary | Ms Karen Sutherland
Tel: (0131 6)51 3404
Email: Karen.Sutherland@ed.ac.uk |
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