REPORTING AND DATA VISUALISATION - 2024/5

Module Overview

In the era of Big Data, effective data visualisation and reporting, that bridges the gap between the data and the user, by providing accessible ways to view and digest data, are essential for our ability to analyse and understand trends and patterns within massive amounts of information being generated. Data visualization and reporting is thus an increasingly important part of data science skills and capabilities that make a successful data scientist. It encompasses a skillset that is highly valued by future employers. Reporting of disease risk models and predicative models, reporting of machine learning outputs, plotting of results from statistical analyses, as well as graphical representation of complex systems, are vital for constructive communication within healthcare and biomedical research.

This module introduces students to key concepts and best practices in data visualization and reporting. The module will cover both the theoretical background on different data visualisation techniques and approaches, as well as practical hands-on experience with writing code, generating graphs and plots, and generating written analysis reports. The aim of the module is to develop an understanding of how information and data should, and can, be best represented both in graphical means as well as in written reports. The module will also address issues related to poor data representation, and how these can have adverse consequences with global impact. This module directly builds on learning and experience from earlier modules in statistics, and advanced analytics and machine learning. Using real-world datasets and illustrative examples, students will develop a theoretical understanding of the science behind data visualization and reporting, as well as practical experience in applying novel computational techniques. Many of the techniques and methods learned and acquired through this module are likely to assist students with the dissertation projects as well as their future careers.

Module provider

School of Health Sciences

Module Leader

GEIFMAN Nophar (Health Sci.)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): 35

Module Availability

Semester 2

Prerequisites / Co-requisites

NA

Module content

Data visualisation is the graphical representation of information and data; it is a key digital capability for future data scientist to possess. Good data visualisation supports our perception of the data and the story it tells, by making sense of data analytics' outputs. The module will cover a range of current topics related to data visualization and reporting. These include:

• Good practice in scientific reporting


• Reporting on statistical models, existing guidelines for model reporting, and gaps in the field


• Reporting on methods in data science


• Interpreting common plot types


• Best practices for using colours and shapes


• Appropriate use of plots and graphs


• Advanced techniques in data visualization


• Open resources for data visualisation

The module will address both the theoretical aspects of the above topics as well as practical aspects. Students will be provided with opportunities to practice their learning through hands-on workshops, where techniques learned through reading materials and presentations will be applied to real world data; namely date from the UK Biobank, but from other resources also.

Assessment pattern

Alternative Assessment

NA

Assessment Strategy

The assessment strategy is designed to allow students to demonstrate a hands-on ability to generate data visualisations and reports, underpinned by the theoretical knowledge gained throughout the module. Thus, the summative assessment for this module consists of: ¿ Six mini reports, guided through worksheets and workshops, will be completed and submitted for assessment. Each report will account for 10% of the final grade (adding up to 60% of the final grade). These will guide and assess students through a range of different techniques covered by the module (addressing learning outcomes 3, 4, and 5).

For the final report, students will choose a topic and dataset to work with, guided by a specific research question (or set of questions), students will generate a visual report that will consist of a range of different visualisations to the data, as well as interpretations of these. Students will need to demonstrate their understanding, by using the correct types of plots/graphs for each data type/analysis, as well as ability to put these together into a consistent narrative. The final report will consist of 40% of the final grade (addressing learning outcomes 1, 2, 3, 4, and 5). Formative assessment will comprise of feedback provided verbally on outputs produced within the workshops. Feedback on summative assessments will be provided in writing.

Module aims

• Equip students with hands-on skills in visualising data and information from analyses
• Provide students with the knowledge and understanding of good practice
• Give students the experience in using a range of purpose-designed tools
• Enable students to develop their own visualisations and reporting
• Explore with students a range of creative ways to disseminate scientific findings
• Enable students to critically assess good practices

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to equip students with the skills needed for effective and appropriate data and information visualisation and reporting in the context of health and biomedicine. The objectives and learning outcomes of this module are designed to enable the students in implementing learning from other modules delivered through the MSc programme, namely Statistics and Modelling for Health Data, and Machine Learning and AI.

The module will take a flipped classroom approach, where students are provided with materials to digest independently as well as for guided learning, followed by group discussions and formative assessment in class to gauge understanding. Allowing contact time to focus on practical learning, the module will consist of practical hands-on workshops in which student will be able to implement their learning on real-world data, primarily from resources used throughout the course, such as the UK Biobank. The module will also focus on problem-based learning, through the experience of solving an open-ended problem found in the trigger material.

Trigger material will consist of a range of different formats to increase student accessibility; this includes, but is not limited to, short videos, online tutorials, visualisation galleries, scientific papers, and other reading material.

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.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: BMSM038

Other information

The MSc Health and Biomedical Informatics programme 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:

• Digital Capabilities: The primary purpose of this module is to enhance students’ digital capabilities in the context of informatics and data science, by providing them with real-world hands-on skills required for a successful career in the field. Students will acquire skills in a range of digital tools that enable data visualisation and reporting.
• Employability: The knowledge gained through this module as well as the skillsets gained through workshops and assessments, can be deployed across health and biomedical research and innovation, but are also highly transferable to a range of other areas of academia, research and industry. This will benefit and enhance students’ employability as will it open doors to a range of roles in data sciences across sectors.
• Resourcefulness and Resilience: Through workshops, working in small groups, and problem-based learning, the module will enhance students’ resourcefulness and self-assurance. This module will especially allow students to tap into their creative side, which they can express in the use of colours, shapes, fonts etc. to create unique and novel ways to present data.
• Global and Cultural Capabilities: Graphical visualization of data is a global language, where a picture is worth a thousand words.  The tools and skillset covered by this module can be used internationally and to traverse cultural differences. Students will learn work together in groups with other students from different backgrounds to solve a problem. This programme allows students to develop skills that will allow them to build applications with global reach and collaborate with their peers around the world.
• Sustainability: Through effective digital data representation, visualization and reporting, research outputs become more sustainable in the long run; students will be encouraged to reflect upon this throughout the module.

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 2024/5 academic year.