Computer Science

COMM420DA - Professional Practice 1 (2023)

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MODULE TITLEProfessional Practice 1 CREDIT VALUE15
MODULE CODECOMM420DA MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 0 13 0
Number of Students Taking Module (anticipated) 90
DESCRIPTION - summary of the module content
The success of data science within an organisation depends not only upon a good understanding of the technical skills involved in data science and machine learning, but also on the ability of the data scientist to identify projects that offer business value. They must then be able to promote any data science projects to stakeholders, plan a project and negotiate with colleagues to ensure its successful delivery. This module will provide you with the business knowledge and awareness to achieve these goals effectively.
 
This module is compulsory for students on the MSc Data Science (Professional) / Level 7 Research Scientist Apprenticeship programme. 
 
AIMS - intentions of the module
The primary aim of this module is to provide you with the core knowledge and understanding needed to deliver data science projects in the context of your organisation. It will cover underpinning concepts for many of the “knowledge, skills and behaviours” associated with the Level 7 Research Scientist Apprenticeship standard.
 
The module consists of a number of lectures, seminars and associated activities. You will study a range of important topics related to the practice of data science in a modern business context. 
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Explain the organisational context for data science and how the role of a data scientist relates to organisational structure and objectives.
2. Describe some approaches to leadership within multidisciplinary teams and discuss the importance of negotiation and influence in relation to data science.
3. Identify the key internal/external stakeholders for data science projects and explain how their different priorities and perspectives might affect project planning and outcomes.
4. Describe some approaches to structured problem-solving in the context of data science.
5. Discuss how to evaluate the costs and benefits of a data science project, product or activity to an organisation, including use of market analysis tools such as SWOT, PESTLE and feasibility studies.
6. Describe the main elements of effective project management, including: project planning; experimental design; ethical and regulatory aspects; risk assessment and mitigation; managing expectations of stakeholders; uncertainty; and adaptation to external factors.
7. Discuss how communication strategies may differ between stakeholders/audiences and explain how mixed-media communication (e.g. technical/non-technical report-writing, presentations) can be effectively utilised to inform, persuade or influence.
8. Discuss how to work effectively with others, including different approaches to teamwork and collaboration, the importance of different perspectives, and the value of equality and diversity in the workplace.
9. Explain the importance of personal responsibility, trust and professional ethics, including internal/external codes of conduct, regulations and/or professional standards relevant to own role.
10. Describe the main aspects of intellectual property and how they apply to data science in a commercial or organisational context.
11. Discuss the importance of ongoing training and maintaining specialist knowledge in individuals and teams. Describe some approaches to continuing professional development for individuals. Describe some approaches to coaching and mentoring of other team members.]
12. Explain the importance of interpersonal skills, assertiveness, communication and influence within an effective organisation. Describe concepts of resilience and adaptation to change.

Discipline Specific Skills and Knowledge

13. Understand the role of data science in organisations.
14. Build knowledge of professional skills relevant for data scientists.
15. Appreciate the wider context for technical aspects of data science.

Personal and Key Transferable / Employment Skills and Knowledge

16. Critical evaluation of business/workplace concepts.
17. Communicate effectively in different forms.
18. Develop stronger interpersonal and professional skills for the workplace.

 

SYLLABUS PLAN - summary of the structure and academic content of the module
The module will provide teaching on the following topics:
 
  • Organisational strategy
  • Market analysis & product development
  • Data science for solving business problems
  • Leadership, teamwork and collaboration
  • Effective communication, negotiation & influencing
  • Project management
  • Coaching & mentoring
  • Problem solving and project planning
  • Professional conduct, ethics, regulations and personal responsibility
 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 18.00 Guided Independent Study 132.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching Activities 18 Lectures, seminars, workshops, other activities
Guided independent study 32 Coursework and assessment
Guided independent study 100 Background reading and reflective practice

 

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
Participation in group activities Varied 1-12 Oral
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 100 (e.g.) 4000 words 1-18 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
Coursework Coursework 1-18 Before end of Stage

 

RE-ASSESSMENT NOTES
Re-assessment can be applied to each assessment separately or to both assessments dependent on requirements.
 
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
Basic reading:
 
Web-based and electronic resources: 
  • ELE – College to provide hyperlink to appropriate pages

 

Reading list for this module:

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

CREDIT VALUE 15 ECTS VALUE 7.5
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 Apprenticeship, reflective practice, leadership and management, ethics, coaching and mentoring, effective communication, business skills