Undergraduate Course: Data-driven chemistry (CHEM08031)
Course Outline
| School | School of Chemistry |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | A lecture course providing an introduction to processing and analysing chemistry-derived datasets using computer programming. The course comprises of an introduction to the Python scripting language and its applications within chemistry, including topics such as classifying data, performing statistical analyses, 3D visualisation and curve fitting. This workshop-based course will be based around chemically-relevant problems. |
| Course description |
This course will provide training in the aspects of computer programming necessary for the repeated, reproducible analysis of chemical data. It will provide the student with skills widely used by chemistry graduates, both in industrial and academic settings. The focus of the course is not to teach programming per se, but rather to use programming to solve complex problems. By meeting the learning outcomes, students will have acquired skills useful across a wide range of chemical disciplines, and applicable throughout their degree.
The course will be delivered through ten workshops (30 hours total). Each three-hour workshop will involve a blended mixture of background information and hands-on programming tasks, focussed around a specific chemical topic. Such topics will include classification of data from the periodic table, visualisation and numerical analysis of experimental data, and manipulation and geometrical analysis of chemical structures. Students will be expected to complete additional problems outside of the timetabled workshops, extending their knowledge of the methods and providing formative feedback. Weekly assignments related to the workshop content will assess the student¿s achievement in relation to the learning outcomes, while a final mini project will draw together the different course topics in a single assessed problem-solving exercise.
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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: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 10,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Summative Assessment Hours 40,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
116 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The course will be continuously assessed through 100% coursework. This will comprise a mixture of quizzes and online coding assignments during the course, and a problem-based coding project at the end. Formative assessment will be incorporated throughout the course materials as self-tests.
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| Feedback |
Returned weekly assignments will include standardised feedback for common errors and specific comments.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Perform numerical operations such as vector algebra and calculate simple statistics on data sets.
- Write readable, well-documented and modular code.
- Break a problem into logical steps, and use loops and decision operations to solve tasks.
- Import and clean experimental data, and choose the appropriate variable types to hold information.
- Fit models to numerical data, and plot the results in a number of different formats.
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Reading List
| Reading lists pertaining to each course unit will be given by the lecturers. |
Additional Information
| Graduate Attributes and Skills |
The course will develop creative problem-solving skills as well as transferrable skills in programming and data analysis. In addition, you will develop your skills in research and critical evaluation of results through in-course assessments. Work during sessions will also improve your skills in communication, numeracy and time management. |
| Keywords | programming,python,data analysis,automation |
Contacts
| Course organiser | Dr James Cumby
Tel: (0131 6)50 4761
Email: james.cumby@ed.ac.uk |
Course secretary | Ms Morag Munro
Tel: (0131 6) 51 7258
Email: Morag.Munro@ed.ac.uk |
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