| Unit name | Quantitative and Computational Methods |
|---|---|
| Unit code | BIOL20020 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Christos Ioannou |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None. |
| Units you must take alongside this one (co-requisite units) |
None. |
| Units you may not take alongside this one |
N/A |
| School/department | School of Biological Sciences |
| Faculty | Faculty of Life Sciences |
Why is this unit important?
Being able to process and analyse data are now key skills across biological research, from genes to ecosystems. The aim of this unit is to introduce these skills, and to enable you to teach yourself more advanced data analysis skills in the future. Skills in study design, statistical analysis and basic programming will be useful in reports for your year 2 optional units and year 3 and 4 research projects. These skills will also make you more employable, as data analysis can be useful in a wide variety of jobs, not just those that are mostly data science or computer programming (although you might consider a career in these after getting a taster in Quantitative and Computational Methods!). The skills you will learn in this unit are so important that it is mandatory for all of our undergraduate students.
How does this unit fit into your programme of study
This unit builds on basic skills introduced at year 1. Skills in study design, statistical analysis and basic programming will give you a foundation for reports in your year 2 optional units and year 3 and 4 research projects; many supervisors of practical projects will use R for their own statistical analyses, and expect you to have the skills taught in Quantitative and Computational Methods. Being able to understand study design, statistical analysis and programming will make you better able to understand what is presented in published scientific literature, which will be useful in all of your remaining units.
An overview of content
The unit will give you an understanding of how to design experiments and data collection. How to then analyse this data is the major component of this unit, from the basic principles of statistical analysis to how these analyses are applied to real-world biological data sets. In the second half of the unit, loops will be introduced, enabling you to write your own randomisation tests. The unit will use the programming language R which is widespread and now the most common data-analysis program for biological research. A gradual introduction to the language over successive practical classes will give you supervised hands-on experience with the program, and how to use it to import and process, visualise, and analyse data.
How will students, personally, be different as a result of the unit
You will gain foundational knowledge of, and the ability to, design studies, manipulate and present data, and analyse data.
Learning Outcomes
1) You will understand the principles of designing experiments and data collection and how they underpin good statistical analysis (and hence good science).
2) You will understand the principles of linear modelling and non-parametric tests, and be able to apply these to real-world biological data sets, including being able to explain and evaluate the assumptions and limitations of the tests.
3) You will understand the basics of computer programming, specifically using R to run statistical tests, manipulate data and create simple plots.
Data processing and statistical analysis are practical skills that have wide applicability; thus, the ways you will learn on this unit are purposely hands-on. The lecture content is intentionally light, being the equivalent of 1 hour per week. These will be in the format of pre-recorded videos that you can view in your own time and at your own pace; through experience, students vary widely in their preferred pace of learning this subject, so we allow you to manage your own learning. You will have a 3-hour practical session each week to work through practical exercises relevant to biological research. These sessions are extensively supported by demonstrators who will answer your questions and help with any problems. The hands-on approach extends to the assessments, which are all continuous assessment (i.e. there is no exam in this unit) and designed to test your knowledge, ability to apply what you have learnt, and to problem solve using data relevant to biology.
Tasks which help you learn and prepare you for summative tasks (formative):
Weekly practical exercises; feedback will be provided in the format of working code and model answers, as well as verbal feedback provided by lecturers and demonstrators at the practical sessions. Attendance at practical sessions is mandatory.
Multiple choice questions with immediate and automated feedback (to be answered at the end of each week of teaching in weeks 1-5 and 7-9).
These exercises will prepare you for the summative continuous assessment that contributes to your grade in this unit.
Tasks which count towards your unit mark (summative):
Evolutionary biology data report (50% of unit mark).
Animal behaviour data report (50% of unit mark).
When assessment does not go to plan
You will have an opportunity to submit the assessments at a later time in the year if you have not been able to take or pass a summative assessment. These will be the same assessments as to be submitted by the original deadlines.
However, no further training or guidance will be given, as this would disadvantage students submitting by the original deadlines.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BIOL20020).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
