2023/24 | Engineering, Mathematics and Physical Sciences Intranet

MODULE TITLE	Individual Research Project	CREDIT VALUE	60
MODULE CODE	COMM424DA	MODULE CONVENER	Unknown

DURATION: TERM	1	2	3
DURATION: WEEKS	0	13	13

Number of Students Taking Module (anticipated)	90

DESCRIPTION - summary of the module content

This module will allow you to apply the knowledge learned in other modules in a significant data science project. The project report must be based on a real research project carried out in the employer’s workplace as part of the apprentice’s day to day activities

The module will develop your project planning, management and implementation skills as well as those in independent learning, presentation and writing. This will also be an opportunity to gain experience in the implementation of a data science project within your organisation and to develop your understanding of the business requirements of such projects.

Pre-requisites: None

Co-requisites: None

This module is a part of the dual-qualification MSc Data Science (Professional) / Level 7 Research Scientist Apprenticeship programme. It cannot be taken as an elective by students on other programmes.

The apprenticeship standard and other documentation relating to the Level 7 Research Scientist Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.

AIMS - intentions of the module

The project report is designed to demonstrate the application of knowledge and skills as it would in the occupation. It collates the raw unsynthesised research findings and demonstrates analysis (statistical and scientific), synthesis, evaluation, literature review and produces recommendations for the business to consider. The report is a significant and complex project in itself and thoroughly tests both higher and lower order knowledge and skills.

The module has several aims:

To develop skills in project specification, planning and management
To develop an understanding of appropriate data science tools to apply
To consider the ethical issues associated with collecting, processing and analysing data
To consolidate skills in the implementation of data science techniques
To develop skills in the understanding and visualisation of the outputs of data science techniques
To develop a greater understanding of where data science fits within the organisation
To develop presentation and writing skills

Most work will be independent study, but you will be supported by a supervisory team through regular meetings and progress reports. The supervisory team will normally consist of relevant academic faculty members from the MSc Data Science programme and a nominated individual at your employer organisation. Ordinarily, the employer organisation will provide guidance on the business relevance of the project and together with the academic supervisor will identify project goals. Exceptionally, you may choose an academic data science project based on previously taught module content. In this scenario, the supervisory team will consist solely of an academic supervisory from the MSc Data Science programme.

Work towards project goals will be carried out independently over a 9-month period, supported by regular supervision meetings conducted using online tools (e.g. video conferencing). A number of “project days” will bring all the cohort together on campus to present progress and hold meetings with academic supervisors. Interim and final presentations of project findings will form part of the project assessment, which will be completed with a substantial report.

In addition to its academic aims as part of MSc Professional Data Science, this module has specific aims as part of the Level 7 Research Scientist Apprenticeship. The full list of Knowledge, Skills and Behaviours that must be demonstrated to complete the Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.

This module will deliver content that may be used to evidence the Knowledge, Skills and Behaviours set out below. Primarily: K1, K4, K5 and K6. Secondarily: S1, S2, S4, S7 and B6.

Knowledge (K), Skill (S) or Behaviour (B)

K1: Subject specific knowledge: A deep and systemic understanding of a named / recognised scientific subject as found in an industrial setting, such as biology, chemistry or physics, found in the nuclear, food manufacture, pharmacology or energy production sectors, at a level that allows strategic and scientific decision making, while taking account of inter relationships with other relevant business areas / disciplines.

K4: Research methodologies: Methodologies appropriate to the sector and how to formulate and apply a hypothesis. Appropriate application of scientific process. The unpredictability of research projects and the need to adapt and adjust daily planning needs to accommodate new developments.

K5: Data analysis and evaluation: Statistical analysis techniques, numerical modelling techniques and how they are applied in context. How to interpret and categorise data to make informed and objective decisions against the goals and targets of the project. How to evaluate and interpret the data and associated analysis against company objectives.

K6: Data management: How to safely store and handle data in line with national and international data protection and cyber security regulations that apply to the role. How to manage and store data in line with employer processes and security approach. How to create an appropriate data management plan.

S1: Scientific Knowledge: Apply a range of advanced, new and emerging practical and experimental skills appropriate to the role (e.g. chemical synthesis, bio analysis, computational modeling).

S2: Data Collection and Reporting: Capture and evaluate data critically drawing a logical conclusion, e.g. Case Report Forms, Data Management Plans, Data Review Plans, edit checks and User Acceptance Testing Plans.

S4: Communication Skills: Write extended reports and critique others' work across a range of documentation, e.g. protocols, consent forms and scientific reports. Deliver oral presentations and answer questions about their work and/or the work of their team. Utilise interpersonal skills, communication and assertiveness to persuade, motivate and influence. Discuss work constructively and objectively with colleagues customers and others; respond respectfully to and acknowledge the value of alternate views and hypothesis.

S7: Research and dissemination: Frame research questions and methodology drawing from current sources e.g., literature and databases. They can produce intellectual insight and innovations in their own discipline to be shared with colleagues, peers and wider stakeholders internal and external to the business.

B6: Planning, Prioritisation and Organisation: Effective time management.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

Module Specific Skills and Knowledge

1 Use and explain research methodologies and scientific processes appropriate to your sector and applies these to form a hypothesis. (K4)

2 Explain any unpredictability of the research project undertaken and any adaptations made as a result of new developments. (K4)

3 Use statistical analysis and numerical modelling techniques and explain how they were applied. (K5)

4 Explain the application of this analysis clearly and coherently, including how data interpretation informed decisions against the goals and targets of the project and company objectives. (K5)

5 Explain how they have handled data in the project in-line with GDPR and the employer’s processes, including how to create a data management plan. (K6)

Discipline Specific Skills and Knowledge

6 Make strategic and scientific decisions based on a deep and systemic understanding of data science and demonstrate the use of a range of advanced, new and emerging practical and experimental skills to support these decisions. (K1 & S1)

7 Capture, analyse and critically evaluate data utilising at least one statistical tool or analytical technique to draw logical conclusions. (S2)

8 Use research methodology based on current sources and present intellectual insight and innovations suitable for internal and external stakeholders.(S7)

Personal and Key Transferable / Employment Skills and Knowledge

9 Structure a project report clearly and includes critique of others' work across a range of documentation.(S4)

10 Explain how best to present and communicate key content and messages, whilst respecting and acknowledging the value of alternative views. (S4)

11 Present the research project plan and explain how deadlines were achieved and how the project fits into business objectives. (B6)

SYLLABUS PLAN - summary of the structure and academic content of the module

Activities related to this module will include:

Introduction to the module.
Identification of suitable projects from within the student’s organisation or based on previous taught content.
Creation of a project plan and specification with your supervisor team.
Implementation of the project
Presentation of progress updates to student and supervisor cohort.
Meetings with academic supervisor (face-to-face and/or online).
Final submission: a report and final presentation.

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	14.00	Guided Independent Study	586.00	Placement / Study Abroad	0.00

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning Teaching Activities	2	Introductory Session
Scheduled Learning Teaching Activities	12	Supervisory meetings
Guided Independent Learning	300	Project work
Guided Independent Learning	286	Background reading and private study

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Supervisor Meetings	12	All	Oral

SUMMATIVE ASSESSMENT (% of credit)

Coursework	70	Written Exams	0	Practical Exams	30

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Interim presentation	10	15 minutes	1, 7, 9	Oral
Final presentation	20	25 minutes	1-9	Written
Final Report	70	4000 words	2-7,10	Written

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
Interim presentation	Presentation	1, 7, 9	Within 8 weeks
Final presentation	Presentation	1-8, 10-11	Within 8 weeks
Final Report	Resubmitted final report	1-9, 11	Within 8 weeks

RE-ASSESSMENT NOTES

Re-assessment will be conducted on a single presentation (30%) and final report (70%)

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Not applicable

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE	60	ECTS VALUE	30

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Monday 11 October 2021	LAST REVISION DATE	Tuesday 24 January 2023

KEY WORDS SEARCH	None Defined

Computer Science

COMM424DA - Individual Research Project (2023)