| Unit name | Aerosol Science: Computational and Data Tools |
|---|---|
| Unit code | CHEMM0042 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
| Unit director | Dr. Edwards |
| 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) |
Core Aerosol Science I |
| Units you may not take alongside this one |
None |
| School/department | School of Chemistry |
| Faculty | Faculty of Science |
Why is this unit important?
The ability to visualise and analyse scientific data sets using computational methods is a crucial transferable skill, applicable in a broad range of careers. This unit will equip you with the knowledge and skills required to identify how to use data science and machine learning in aerosol science applications, to choose appropriate models, algorithms and data structures for specific situations, and to write, test, and run your own scientific programmes to produce models or data visualisation.
How does this unit fit in to your programme of study
This unit provides an introduction to data science and scientific computing, specifically designed for students with minimal prior experience of computational methods. You will learn a range of computational and data tools which will be combined with your knowledge of aerosol science from Core Aerosol Science I and II to produce aerosol models and analyse and visualise data.
An overview of content
This unit will cover the key concepts and techniques required to visualise and analyse scientific data sets using computational methods, as well as construct models relevant to aerosol science systems. The topics covered are:
1. Hardware and software
2. Programming languages and software carpentry
3. Artificial Intelligence
4. Data science and ethics
5. Building aerosol models, including dynamic and single particle models
6. Machine learning
How will students, personally, be different as a result of the unit
The ability to code is becoming an increasingly important transferable skill. Through this unit you will gain a solid foundation in computational skills, enabling you to use computational methods to analyse and visualise your research data and to produce aerosol science models related to your PhD research. Such skills will allow you to tackle complex or time-consuming tasks more effectively, increasing your productivity and employability.
Learning outcomes
At the end of the unit a successful student will be able to:
1. Compose and test basic scientific programmes using a modern programming language, producing code which matches software engineering best practice.
2. Discuss the basic principles of data science and machine learning, including being able to justify the choice of model, algorithm, or data structure best suited for a specific aerosol science application.
3. Construct simple computer models of aerosol science related systems, including visualising outputs.
4. Design and conduct experiments/models using computational models with technical mastery, and analyse and interpret data.
Initially material will focus on a theoretical underpinning, followed by practical application. The unit will be taught through a mixture of seminars, interactive workshops and challenge-led exercises. In workshops, you will be provided with interactive coding worksheets (e.g., Jupyter Notebooks) which you can complete with support from the academic facilitator. You will also participate in group tasks to allow you to benefit from peer-to-peer learning.
Tasks which help you learn and prepare you for summative tasks (formative):
As a practice-based course, formative assessment will take place continuously through use of interactive coding worksheets. These will allow you to receive instant feedback on your understanding, with opportunities to learn from the academic facilitator as well as your peers.
Tasks which count towards your unit mark (summative):
The unit will be assessed through a group programming project (end of TB1, 40% of unit mark), and an individual inquiry-led, open-ended programming project (end of TB2, 60% of unit mark). For the group programming project, you will be expected to submit the annotated code for assessment and feedback, as well as an individual reflective statement describing your contribution to the project. For the inquiry-led, open-ended project, you will need to draw on material from across the unit and apply it to solve an aerosol science related problem within your research context. You will be expected to submit your code for assessment, along with a report which outlines your chosen method, its application, and your results.
When assessment does not go to plan
If you are unable to participate in the group programming project due to exceptional circumstances, you will instead undertake an individual programming project which covers the same course objectives. If you are unable to submit your open-ended project report by the deadline due to exceptional circumstances, you will be given a limited extension. In the case of academic failure, opportunities to re-sit will be according to University of Bristol regulations. Reassessment will take the same format as the original assessment.
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. CHEMM0042).
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.
