Aims and Fit of Module

The aim of the module is to help students develop skills that should allow students to thrive during the UG studies, and as well later during their professional career. Students will be introduced to general transferable skills, general scientific skills and pharmaceutical sciences-related skills.

Learning outcomes

A Understand the basic concepts of pharmaceutical sciences and use a range of mathematical and numerical tools and artificial intelligences to address pharmaceutical sciences-related problems
B 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
C Retrieve, understand and manage web-based and artificial intelligence-derived knowledge and information related to pharmacy
D Find information through literature searches and use IT or artificial intelligences effectively to analyse and report findings and summarise and interpret advanced data using graphs and tables
E Use appropriate software for macromolecular visualization
F Competently utilise a range of skills to design scientific projects in order to address real pharmaceutical questions
G Structure and communicate professional projects effectively, either orally or in writing

Method of teaching and learning

The theoretical aspects of this module will be delivered through lectures that will be supported with course work consisting of tasks. The coursework will be The theoretical aspects of this module will be delivered through lectures that will be supported with course work consisting of tasks. The coursework will be summative assessments that could include problem solving exercises, presentation and quizzes that students will need to complete at home, or in class.

Students will attend a 2-hour lecture for 13 weeks, 1-hour tutorial for 13 weeks and 2-hour lab practicals for 6 weeks. Students will also be given guidance and opportunities to practice the various skills mentioned in the specifications. The module will also be delivered through computer practicals to allow students to gain hands-on experience and practice with specific bioinformatics tools, software and artificial intelligences. The learning outcomes associated to these practicals will be assessed with summative assessment tasks.

Self-study activities will be extensions or consolidations of work carried out in the lecture and lab practical. Assessment components of this module will include: 1) questions or tests in lectures or tutorials; 2) coursework to review the topics; 3) group discussion or report that summarizes the lab practicals. The feedback will help improve the teaching quality and ensure the quality of summative assessment. Timely, relevant and specific, constructive and actionable feedback will be provided to students in-class, on-paper, via artificial intelligences and/or in-person on for each assignment.