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DRPS : Course Catalogue : School of Chemistry : Chemistry

Undergraduate Course: Data-driven chemistry (CHEM08031)

Course Outline
SchoolSchool of Chemistry CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryA 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 is based around chemically-relevant problems.
Course description This course provides training in the aspects of computer programming necessary for the repeated, reproducible analysis of chemical data. It provides 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 is 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. 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites Students MUST also take: Chemistry 2 (CHEM08019)
Prohibited Combinations Students MUST NOT also be taking Programming and Data Analysis (PHYS08049) OR Quantitative Skills for Biologists 1 (BILG08019) OR Experimental Physics 2 (PHYS08058) OR Chemistry for Chemical Physics 2 (CHPH08002)
Other requirements This course is compulsory for students enrolled on the full year CHEM08019 Chemistry 2 course. Only UoE students enrolled on CHEM08019 are able to enrol on Data Driven Chemistry.
Information for Visiting Students
Pre-requisitesThis course is suitable for visiting students who have completed at least 1 year of undergraduate level Chemistry.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) 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 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
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.

Feedback Returned assignments will include standardised feedback for common errors and specific comments.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Perform numerical operations such as vector algebra and calculate simple statistics on data sets.
  2. Write readable, well-documented and modular code.
  3. Break a problem into logical steps, and use loops and decision operations to solve tasks.
  4. Import and clean experimental data, and choose the appropriate variable types to hold information.
  5. Fit models to numerical data, and plot the results in a number of different formats.
Reading List
A full Resource List for this Course is available through Library Services, not limited to:
Kenneth Reitz and Tanya Schlusser, The Hitchhiker's Guide to Python, 2nd Ed, Sep 2016
Christian Hill, Learning Scientific Programming with Python, 2nd Ed, Dec 2020
Al Sweigart, Automate the Boring Stuff with Python, 2nd Ed, Oct 2019
Eric Mathes, Python Crash Course, 2nd Ed, May 2019
Alex Martelli, Anna Ravenscroft and Steve Holden, Python in a Nutshell, 3rd Ed, 2017
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.
Additional Class Delivery Information Weeks 1-10 Semester 1 every Thursday 09:00 to 12:00.
Keywordsprogramming,python,data analysis,automation
Course organiserDr James Cumby
Tel: (0131 6)50 4761
Course secretaryMs Morag Munro
Tel: (0131 6) 51 7258
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