DATA ANALYSIS - 2023/4
Module code: CHE1046
This module is intended to introduce basic data handling knowledge and practice combined with introductory programming skills (digital capabilities) to give students confidence in handling chemical data which they can apply to all modules throughout the course (resourcefulness and resilience) and will also provide a basis for future employment (not only in chemistry) where they will need these skills generally (employability). The hands-on workshop approach rather than formal lectures gives them the chance to solve set problems whilst building their confidence.
Chemistry and Chemical Engineering
HOWLIN Brendan (Chst Chm Eng)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 4
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 22
Independent Learning Hours: 108
Guided Learning: 10
Captured Content: 10
Prerequisites / Co-requisites
Indicative content includes:
- Introduction to Chemometrics Basic statistical concepts e.g. error handling Introduction to Multivariate Data Analysis
- Principal Component Analysis
- Multivariant Regression: MLR (Multiple Linear Regression), PCR (Principal Component Analysis) and PLS (Partial Least Squares)
- Graph Plotting and Graphics using current software
- Examples of applications to current research
- A presentation on modern robotics and automation in chemistry (Dr Malcolm Crook)
- Basics of Python programming
- Assignment, variables
- Making decisions
- Graphing in a programming language
- Extended assignment involving writing a python programme to solve a chemical problem
|Assessment type||Unit of assessment||Weighting|
|Coursework||Data Handling Exercise||50|
The assessment strategy is designed to provide students with a hands-on workshop approach to data handing and programming in a computing laboratory to successfully demonstrate that they have achieved the learning outcomes for the module and have the ability to deploy these skills to all of the other chemistry modules (in particular the final year project modules).
Thus, the assessments for this module consists of: Students will solve and submit 1 summative exercise in data handling (learning outcomes 1 and 2) and an extended coursework in writing a python program to solve an unseen chemical problem (meets learning outcomes 1,2,3, 4). Formative assessments will be given each week as in-class exercises on data handling and programming - solved on an individual basis with in-class feedback. Why are we doing this? The assessment strategy is designed to allow students to gradually develop and test their knowledge and their skills in a manner that not only enhances their understanding of the topic, but also allows them to situate it within the wider context of the subject area, thereby contributing to the coherency of their learning journey. The assessments therefore contains valuable employability components and tests a range of transferable skills. The assessment strategy also allows for assessments to take place in a supportive context through individual work that is assessed both formatively and summatively and that can be applied to assessments in other modules. Such an approach contributes to the development of students as independent learners by empowering them to reflect on their own performance. Other elements of the assessment strategy allow students to test their performance in relation to real-life scenarios and in the case of the programming assessment to gain valuable life skills in digital capabilities and resilience.All aspects of the assessment strategy further allow students to receive feedback from staff. An additional aspect of this module is that it allows students to demonstrate desired skills to an employer, thereby enhancing their employability.
- To present a selection of modern data handling methods and techniques.
- To provide the background necessary for students to comprehend and criticise the results of data analysis.
- To give students the opportunity to carry out and comment on a variety of practical data handling examples
- To cover a range of selected topics in a computer programming language.
- To cover a range of selected topics in robotics and automation.
|001||Confidently carry out and comment on the results of data handing exercises.||CKPT|
|002||Comprehend and analyze the results of data handling exercises.||CK|
|003||Systematically understand the process of computer programming||CP|
|004||Can apply appropriate programming skills to solve multivariate and complex data analysis problems.||CKP|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The Indicated Lecture Hours (which may also include seminars, tutorials, workshops, and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organized separately to taught content and will be published on to student personal timetables, where they apply to taken modules as soon as they are finalized by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester. Specifically, a workshop based, hands on approach will be taken to this module. Students will work through set problems in a computer workshop with individual advice from academics and demonstrators available on request. For the programming sessions simple problems will be tackled first to gain experience of the language before the assessed exercise is set and solved by the students. This approach enables students to gain confidence by doing actual problems in a supervised environment which will enable them to apply these techniques in all other modules but in particular the final year project modules where they will have to analyze and present their own data.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: CHE1046
The School of Chemistry and Chemical Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: The Digital capabilities of students will be enhanced using computers and software throughout the module with hand on advice from the staff available from the workshop-based approach. The Resourcefulness and resilience of students will be built upon by the hands-on workshop approach which concentrates on students doing rather than just listening. Again, hands-on advice will be available from staff and demonstrators throughout the course to build confidence and help students to master the problems set. The Employability of students is a key focus of this course as it provides skills in data analysis that are particularly attractive to employers. An external tutor is particularly interested in employing students with these skills in his Local Robotics and Automation company so there is a clear link to employability
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.