Undergraduate Course: Quantitative Skills for Biologists 1 (BILG08019)
This course will be closed from 31 July 2023
Course Outline
| School | School of Biological Sciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | The course is designed to apply selected topics in mathematics, basic statistical analysis, data handling and programming to an understanding of biological issues. The lectures and workshops will use biological data to illustrate key concepts and to develop student quantitative skills. |
| Course description |
NOTE THAT THIS COURSE WILL NOT RUN AFTER 2021/2022. NO NEW STUDENTS CAN BE ENROLLED ON THIS COURSE.
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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 | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 3,
Seminar/Tutorial Hours 6,
Supervised Practical/Workshop/Studio Hours 16,
Other Study Hours 7,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
164 )
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| Additional Information (Learning and Teaching) |
Other study hours include: scheduled Computing drop-in sessions.
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The course is assessed by a combination of in-course work.
The in-course assessed work will consist of four components:
(1) Python Weekly Quizzes (4 in total) contribute 20% of final mark.
(2) EDA Quizzes (3 in total) contribute 20% of final mark.
(3) Data Analysis project contributes 30% of final mark.
(4) Maths Worksheet contributes 30% of final mark.
There is no final examination.
The Python and EDA Quizzes will be delivered online, automatically marked and thus the results (and some feedback) will be given immediately on finishing the tests.
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| Feedback |
Marks will be communicated via Learn and EUCLID. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Use basic quantitative skills in the analysis and interpretation of experimental data.
- Think quantitatively about biological problems.
- Analyse data using appropriate statistical methods and present it in a clear format.
- Create and execute Python programs to extract, manipulate and summarise data from large biological datasets.
- Perform calculations necessary for data analysis.
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Reading List
| Easy Mathematics for Biologists, by Peter C. Foster |
Additional Information
| Graduate Attributes and Skills |
Development of Graduate Attributes
The University has identified a set of four clusters of skills and abilities (see headings below) that we would like students to develop throughout their degree programme to strengthen your attitude towards lifelong learning and personal development, in addition to future employability. The graduate attributes we hope to develop with the Quantitative Skills for Biologists 1 course are indicated below.
Research and Enquiry
This course aims to increase your understanding of the subject area and also obtain the specific skills as outlined in the learning objectives above. The knowledge obtained, and the development of research and technical skills will be of benefit to you in completing of your degree and beyond. The course will develop your research and problem solving capabilities through workshops on the use of Python and statistics and mathematics tutorials. In-course assessment will enable you to improve your data manipulation skills, evaluate scientific information and make critical judgements and considered conclusions from your scientific enquiries.
Personal and Intellectual Autonomy
We encourage students to work independently to meet the challenges of the course but also to strengthen your views by discussion and debate with other students. By exploring textbooks you can not only expand your knowledge of the topics covered in the lectures, but this will allow you to broaden your own personal scientific interests outside of the specific subjects in the course. The assessed Exploratory data analysis project allows you to reflect on what you have learned from the programming & statistics workshops, and to apply your skills to solve various biologically relevant problems. We hope this also stimulates your creativity in developing critical thinking and new approaches to the solution of complex problems.
Communication
Through discussion and collaboration with students in tutorial & workshop groups you will be able to communicate your views and ideas and to learn from your peers. You are also encouraged to ask questions from your lecturers, practical demonstrators and tutors to expand your knowledge and clear up any misinterpretations you might have. There is also a discussion forum on Learn which can be used to obtain feedback and to discuss various aspects of the course.
Personal Effectiveness
Throughout your degree programme you will learn transferable skills which will benefit you not only across the courses you are enrolled in but in future employment and further study. In this course, as in others, time management is an important skill you will learn as you must develop ways to organise your work and meet deadlines. Group work in tutorials and workshops is also an important transferable skill and by interacting with fellow students you will become aware of your own skills and talents (and your possible limitations) and appreciate those of others. |
| Additional Class Delivery Information |
Three lectures (recordings).
Self-directed study for Maths.
Eight workshops for Programming and Exploratory Data Analysis (compulsory).
Three Maths tutorials (optional).
Drop-in help sessions for programming/EDA (optional).
Drop-in help sessions for Maths (optional). |
| Keywords | biology,mathematics,statistics,programming,data analysis |
Contacts
| Course organiser | Dr Ramon Grima
Tel:
Email: Ramon.Grima@ed.ac.uk |
Course secretary | Dr Caroline Aspinwall
Tel: (0131 6)50 5521
Email: Caroline.Aspinwall@ed.ac.uk |
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