Aims and Fit of Module

1. Enable students to analyse and interpret scientific data and communicate results;
2. Enhance the employability prospects of students and career awareness.

Learning outcomes

A Find information through literature searches and use IT or artificial intelligences effectively to analyse and report findings
B Competently utilise a range of mathematical and numerical skills relevant to pharmaceuticals sciences, such as formulating and testing a hypothesis, incorporating randomisation and methods to assess error.
C Summarise and interpret advanced data using graphs and tables
D Within the context of experimental design and within a range of pharmaceutical science fields, select appropriate quantitative methods, such as linear and non-linear relationships, to answer questions
E Apply appropriate statistical and other analysis packages to analyse data
F Interpret and evaluate quantitative terms and approaches used in the scientific literature within a range of pharmaceutical science fields

Method of teaching and learning

The module will be delivered through standard lectures, which will be accompanied by relevant lecture handouts. Students will also be guided to sections of specific textbooks, and if reading of specific reviews or literature sources is required, then copies of these will be made available in the library for use by the students. At intervals during the module, tutorials will be held for the students to deeply understand the principle of this module. Animations and/or videos will be shown for some of the topics. Review sessions will be arranged towards end of the semester and the students will have opportunities to self-assess their understanding of the course.

Students will attend a 2-hour lecture and 1-hour tutorial for 8 weeks. Students will also be given guidance and opportunities to practice the various skills mentioned in the specifications. Self-study activities will be extensions or consolidations of work carried out in the lecture and tutorial. Assessment components of this module will include: 1) questions or tests in lectures or tutorials; 2) course work consisting of tasks to review the topics; 3) group discussion or report that summarises the lab practicals; and 4) presentation of a critique of the teaching topics. The computer practicals to allow students to gain hands-on experience and practice with specific bioinformatics tools, software and artificial intelligences. Timely, relevant and specific, constructive and actionable feedback will be provided to students in class, on paper, via artificial intelligences and/or in person for each assignment. The feedback will help improve the teaching quality and ensure the quality of summative assessment.