Undergraduate Course: Quantitative Skills for Biologists 1 (BILG08019)
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 |
The important quantitative skills required in biology will be delivered through collaborative learning activities in workshops and tutorials, plus individual and group self-directed study. The course will consist of 3 modules; a) exploratory data analysis, b) mathematics and c) programming for analysis of biological data.
Mathematics: Delivered in lecture format. Students will be given significant homework to hone their mathematical skills. Tutorials will address specific problems with homework and reinforce lecture content.
Statistics and Programming: Both components will be taught throughout the course via workshops delivered in teaching studios. Students will be encouraged to bring their own laptops and work in groups of at least three, which will allow interaction with tutors and self-directed group learning.
Pre- and post- workshop learning activities will be provided, with students expected to work on these in their own time. In order to maintain engagement, students will be set multiple, low-stakes, summative, time-restricted tests on material that they must have covered before the next workshop.
Drop-in help sessions will be available to students throughout the 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 2019/20, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 12,
Seminar/Tutorial Hours 6,
Supervised Practical/Workshop/Studio Hours 18,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
160 )
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| Assessment (Further Info) |
Written Exam
30 %,
Coursework
70 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The course is assessed by a combination of in-course work and an examination.
The in-course assessed work will consist of four components:
1) Python Quizzes 1-3 contribute to 10% of final mark.
2) EDA Quizzes 1-3 contribute to 10% of final mark.
3) Group project contributes to 25% of final mark.
4) Maths Worksheet contributes to 25% of final mark.
The examination contributes 30% of the final course mark.
The examination paper will consist of multiple choice questions testing the material covered in the 5 maths tutorials and in the exploratory data analysis covered in the workshops 4, 5 and 6. Note that the exam will not test the programming section of the course.
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. In the group project, you will work together in groups to analyse a biological data set. This component will be assessed by electronic submission of a Jupyter notebook from each group.
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| Feedback |
Marks will be communicated via Learn, Autograding, and EUCLID. Students will be provided with formative guidance prior to summative assessments. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S1 (December) | | 1:30 | |
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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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. |
| Keywords | biology,mathematics,statistics,programming,data analysis |
Contacts
| Course organiser | Dr Ramon Grima
Tel:
Email: Ramon.Grima@ed.ac.uk |
Course secretary | Mr Edward Lithgow
Tel: (0131 6)50 8638
Email: Edward.Lithgow@ed.ac.uk |
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