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DRPS : Course Catalogue : School of Biological Sciences : Biology

Undergraduate Course: Quantitative Skills for Biologists 1 (BILG08019)

Course Outline
SchoolSchool of Biological Sciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThe 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 Quantitative Skills for Biologists 1 is designed to apply selected topics in mathematics, basic exploratory data analysis, data handling and programming to an understanding of biological issues.

The lectures, tutorials and workshops will use biological data and examples to illustrate key concepts and to develop quantitative skills. The important quantitative skills required in biology will be delivered through collaborative learning activities in collaborative and tutorials, plus self-directed study.

The course will consist of 3 parallel-running modules: (a) mathematics, (b) programming for analysis of biological data and (c) exploratory data analysis (EDA). Mathematics will be delivered through assigned maths problems and optional tutorials, and programming and data analysis will be taught through collaborative workshops.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2021/22, Available to all students (SV1) Quota:  None
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 )
Additional Information (Learning and Teaching) Other study hours include: scheduled Computing drop-in sessions.
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
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.

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:
  1. Use basic quantitative skills in the analysis and interpretation of experimental data.
  2. Think quantitatively about biological problems.
  3. Analyse data using appropriate statistical methods and present it in a clear format.
  4. Create and execute Python programs to extract, manipulate and summarise data from large biological datasets.
  5. Perform calculations necessary for data analysis.
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.


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).
Keywordsbiology,mathematics,statistics,programming,data analysis
Course organiserDr Ramon Grima
Course secretaryDr Caroline Aspinwall
Tel: (0131 6)50 5521
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