Module code: BMSM033

Module Overview

The fields of health and biomedical informatics is grounded on proper modelling and analysis of health and clinical data. The purpose of this module is to provide students with the key concepts and principles of statistics, and data analytics, as applied to healthcare and biomedical research. The module will guide students through the variety of statistical techniques commonly used in health and biomedical research and practice, and in improving the efficiency, productivity and quality of healthcare processes and systems.

Students will take a series of lectures and laboratory (computer-based) practical sessions to develop digital capabilities and employability skills such as choosing and implementing statistical methods on modern statistical software and reporting of statistical techniques and analysis results. Students will learn and practice handling real-world health data, and will design, implement, and report their own statistical analyses as parts of the in-class workshops and module assessments.

Module provider

School of Health Sciences

Module Leader

JIN Haomiao (Health Sci.)

Number of Credits: 30

ECTS Credits: 15

Framework: FHEQ Level 7

Module cap (Maximum number of students): 35

Overall student workload

Workshop Hours: 6

Independent Learning Hours: 190

Lecture Hours: 50

Tutorial Hours: 8

Laboratory Hours: 24

Guided Learning: 16

Captured Content: 6

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

The module will cover the following topics:

  • Types of studies carried out in health, clinical and medical settings.

  • Types of variables and how to handle each type

  • How to prepare datasets for analysis (data pre-processing)

  • Statistical hypotheses and testing

  • Distributions

  • Univariable analyses

  • Multivariate analysis and regression modelling

  • Longitudinal data modelling and time series analysis

  • Survival analysis

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Analysis coursework 1 10
Coursework Analysis coursework 2 10
Coursework Analysis coursework 3 10
Oral exam or presentation Workshop presentation 10
Project (Group/Individual/Dissertation) Written and statistical programming assessment 1 30
Project (Group/Individual/Dissertation) Written and statistical programming assessment 2 30

Alternative Assessment

Workshop presentation may be handed in as a pre-recorded video.

Assessment Strategy

The assessment strategy is designed to allow students to demonstrate their acquired knowledge and skills to appropriately choose, implement, report, and critically appraise statistical techniques and methods, as applied to healthcare and biomedical research and practice.

Through both the formative and summative assessment, students will demonstrate their understanding and exploration of key concepts, principles and applications of statistical techniques in healthcare and biomedical research and practice, while the summative assessment will allow students to demonstrate their gained knowledge and skills in properly choosing statistical methods, implementing the chosen methods on statistical software, reporting the methods and results, and critically appraising the limitations of their analyses.

 Thus, the summative assessment for this module consists of:

  • Three analysis coursework reports submitted at three timepoints during the module, following practical workshops, as PDF file and statistical programming code. The coursework involves submission of the statistical programming code and a summary report of the methods and results of the analyses conducted during the practical sessions (each accounting for 10% of the final grade, totalling in 30% of the final grade, individual work). The coursework may include additional probing questions that are designed to allow students to demonstrate their understanding of key statistical concepts and principles learned in the weekly lectures (addressing learning outcomes 1-6).

  • Workshop presentation and participation in discussions (10%, individual work). This will include a 10 minutes presentation followed by 5 minutes of questions and discussion (addressing learning outcomes 3, 6, and 7).

  • Two written and statistical programming assessments (30% each, individual work) submitted as PDF file and statistical programming code. Each assessment will involve one large or several smaller piece(s) of statistical analysis work. Each piece of work will involve a general description of the task and its specific aim(s)/goal(s) and the raw or intermediate data that are necessary to carry out the work. Each of the assessment is expected to be ~1500-2000 words and include a number of figures, plots and tables. Students will be required to choose and implement appropriate statistical method(s) to achieve the aim(s)/goal(s), properly report their chosen method(s) and analysis results, and critically appraise the limitation of their method(s) and analytical work (addressing learning outcomes 1-6).


Formative assessment

Students will submit an outline of their planned presentation for workshop; feedback and guidance will be provided to ensure students are on the right track.



Students will receive written feedback from the module lead and/or teaching assistant on each of the assessment elements; this includes the weekly coursework, workshop presentation slides, and the writing and statistical programming assessments. Verbal, formative, feedback will also be provided following the workshop presentation as well as during the practical sessions.

Module aims

  • Introduce students to the key concepts and principles of statistics, and data analytics, as applied to healthcare and biomedical research.
  • Introduce the variety of statistical techniques commonly used in health and biomedical research, and practice.
  • Provide students with skills in handling real health data, including the design, critical appraisal, and implementation of statistical analyses,
  • Provide students with the skills to assess analyses and appropriately report analysis results.
  • Equip students with the knowledge and skills that will directly support learning across following modules in the programme (e.g. Machine Learning and AI, and Reporting and Visualisation).

Learning outcomes

Attributes Developed
001 Become faimilar with a range of common study types that are used in health and biomedical research and practice. KT
002 Understand and distinguish between different types of variables. CK
003 Understand and describe the key concepts, principles, strengths and limitations of a range of statistical techniques covered in the module. CKT
004 Se and correctly apply and implement, statistical techniques that are covered in the module for real health and biomedical data. CKPT
005 Appropriately report and share statistical analysis results. PT
006 Critically appraise the limitations of statistical techniques. CKPT
007 Explore a statistics-related topic in greater depth, disseminating ideas and thoughts to peers, as well as receive and provide feedback to peers CKPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to ensure that students achieve the module’s learning outcomes and develop competencies in the corresponding aspects of the curriculum framework (employability, digital capabilities, global and cultural intelligence, and resourcefulness and resilience). The module is designed in a way that builds upon concepts parallelly taught by the Big Data in Biomedicine and Health module, by implementing these through real-world examples and data.


This module will be taught in a series of lectures and practical sessions. The lectures will introduce key concepts and principles of statistical methods commonly used in healthcare and biomedical research and practice, followed by tutorials on implementing the methods on statistical software (e.g., R) and discussion of real-world examples and limitations of applying the methods to evaluating/improving the outcomes of health interventions or the efficiency, productivity and quality of healthcare processes and systems. This strategy will ensure students gain both a firm grounding in the theoretical aspects of the covered topics, but also the practical implementation skills expected to be held by future data scientist and informaticians. Statistical software and digital resources will be fully integrated within the lectures, and students will have the opportunities to co-develop and test statistical programming code with the module lead and their peer students. The lectures will also provide students with the skills of reporting statistical methods and results, including the generation of high-quality tables and figures and appropriately writing the “Methods” and “Results” sections in reports and publications.


Students will further practice and develop their statistical skills in computer-based practical sessions. The sessions will provide students with the opportunity to handle real health data and experiment with statistical methods learned in the lectures. In addition, students can practice the reporting of their statistical analyses, including writing the codes to generate plots and figures.

As a major part of the weekly coursework, students will be required to summarise and report the methods and results of their works in the practical sessions and critically appraise the limitations of their analyses. Students will be encouraged to complete these sessions independently, utilising the statistical techniques, software and the digital and online resources introduced in the lectures.


Written feedback will be provided by the module lead and/or teaching assistant, and discussions will be organised weekly for students to reflect on their work. The practical sessions will facilitate students in developing key digital capabilities that are highly sought after by employers across sectors. These sessions will further empower students in learning and utilising online resources to help with solving applied statistical problems.


In addition to lectures and practical sessions, a workshop will be organized to provide students with the opportunity to teach-learn from each other and explore an interested topic with greater depth. The topic of the workshop will be voted on by students from a list of candidate topics prepared by the module leader. Students will then prepare a presentation, which will be delivered during the workshop followed by questions and discussions.


The learning and teaching methods will therefore include:

  • Lectures

  • Computer-based laboratory practical sessions

  • A workshop

  • Weekly homework

  • Reading and self-learning under the module leader’s guidance

  • Written assessments as described below.

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
Upon accessing the reading list, please search for the module using the module code: BMSM033

Other information

The MSc Health and Biomedical Informatics 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:

  • Employability: Statistical knowledge and skills covered by this module will be of value when students progress to the next stage of their careers in data sciences and informatics. Students will develop the capabilities in handling real health data using basic and advanced statistical methods and learn and practice reporting and presentation skills, which may be particularly valued by prospective employers. Through lectures, practical sessions, and workshop, students will be equipped with various statistical resources that are transferable and could be re-used in their future career across a range of sectors.
  • Digital Capabilities: Statistical software and digital resources will be fully integrated in this module. Students will have the chance to learn and develop their statistical programming capabilities not only in the practical sessions but also in the lectures through co-developing and testing statistical codes with the module leader and their peer students and in the workshop through teach-learning from peers.
  • Global and Cultural Capabilities: Students will gain experience in global resources and be introduced to online communities that are playing a key role in supporting modern statisticians. In particular, students will explore the vibrant R community that has developed across the globe, and shared numerous statistical packages.
  • Resourcefulness and Resilience: This module is designed to allow students to work through the problem-solving cycle, addressing every step with the knowledge, understanding, and hands-on practical tools that will be covered by the core material. These skills will enhance students’ resourcefulness going into following modules within the course (for example in Machine Learning and AI) as well as into their future career development. Further, a better grasp of statistics, which some students may initially find intimidating, will enhance students’ confidence and self-assurance.

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.