Module code: COMM063

Module Overview

This module supports students' development of personal and professional attitudes and abilities appropriate to a Professional Training placement. It supports and facilitates self-reflection and transfer of learning from their Professional Training placement experiences to their dissertation and their future employment. The module is concerned with Personal and Professional Development towards holistic academic and non-academic learning. Development and learning may occur before and during the placement. However, the graded assessment takes place primarily towards the end of, or after, the placement. Additionally, the module aims to enable students to evidence and evaluate their placement experiences and transfer that learning to other situations through written skills. Through the professional placement students will refine both their subject specific and general soft skills as a Data Scientist, and gain practical experience of the use of Data Analytics and domain knowledge in the application of Data Science methods in a business or research context. The assessment is aligned with the Edison Data Science Competence Framework and includes a technical component describing the work undertaken, as relates to Data Science competence group Data Analytics (DSDA). Further it encourages students to reflect both on their ability to think and act like a Data Scientist (Edison skill DSPS) and their 21st Century Workplace skills (Edison skill SK21C), and on the experience of Business Process or Scientific Research gained during the placement (Edison competence DSBPM). This includes but is not limited to:

  • Effective use of a variety of data analytics techniques, such as Machine Learning (including supervised, unsupervised, semi- supervised learning), Data Mining, Prescriptive and Predictive Analytics, for complex data analysis through the whole data lifecycle (DSDA01).

  • Understand and use different performance and accuracy metrics for model validation in analytics projects, hypothesis testing, and information retrieval (DSDA04).

  • Visualise results of data analysis, design dashboard and use storytelling methods (DSDA06).

  • Being aware of the power and limitations of the main machine learning and data analytics algorithms and tools (DSPS08).

  • Working in a multi-disciplinary team, with the ability to communicate with domain and subject matter experts (DSPS11).

  • Being aware of ethical issues around the use of data and insight delivered (DSPS15).

  • Critical Thinking: Demonstrating the ability to apply critical thinking skills to solve problems and make effective decisions (SK21C01).

  • Collaboration: Working with others, appreciation of multicultural differences (SK21C03).

  • Planning & Organizing: Planning and prioritizing work to manage time effectively and accomplish assigned tasks (SK21C05).

  • Dynamic (self-) re-skilling: Continuously monitor individual knowledge and skills as shared responsibility between employer and employee, ability to adopt to changes (SK21C09).

  • Translating unstructured business problems into an abstract mathematical framework (DSBPM01).

  • Using data to improve existing services or develop new services (DSBPM02).

  • Providing scientific, technical, and analytic support services to other organizational roles (DSBPM04).

Module provider

Computer Science and Electronic Eng

Module Leader


Number of Credits: 60

ECTS Credits: 30

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 591

Lecture Hours: 2

Tutorial Hours: 7

Module Availability

Crosses academic years

Prerequisites / Co-requisites


Module content

The module focuses on achieving the learning outcomes by offering, via the placement experience, the opportunity for students to nurture the employability skills that employers look for and to develop the professional identity, competencies and attributes that support the future employability outcomes for students. This development takes place in specified professional environments where work undertaken is directly relevant to the Data Science Masters degree, with the support of an academic and professional supervisor.

Assessment pattern

Assessment type Unit of assessment Weighting
Project (Group/Individual/Dissertation) Placement Report Pass/Fail

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate the learning outcomes regarding the successful acquisition of a Professional Training placement, and the acquisition of the employability skills and competencies that support students¿ graduate employability outcomes.

The summative assessment for this module consists of a report that assesses LO1 through LO8. The report is completed at the end of the placement this report describes the student¿s placement, the technical work they undertook as relevant to the Masters in Data Science, analyses their professional skills and work environment and provides a critical reflection on their personal and professional development. The Placement Report contains four elements; (a) a technical section describing the work undertaken as relevant to Edison Data Science competence Data Analytics (DSDA), (b) a section discussing the placement in the context of the competencies described in the Edison Data Science Competence Business Process Management (DSBPM) (c) a section describing how the student applied their Data Scientist skills with reference to the ¿Thinking and acting like a Data Scientist¿ (DSPS) skill group in the Edison Data Science Framework, and (d) a section about the student¿s reflection on their personal and professional development as relates to the ¿21st Century workplace skills¿ (SK21C) of the Edison Data Science Framework.

Formative assessment

Students will receive ongoing feedback from their employer, and from the University¿s Visiting Professional Training Tutor during the three placement visits. This feeds into the development of the Reflective element of the Placement Report.

Module aims

  • Enable students to acquire and develop knowledge as it occurs in professional practice.
  • Apply academic knowledge to work activities and processes in practice.
  • Enable students to mature through the evaluation of their placement experiences.
  • Support students to develop and apply new skills appropriate to their professional setting in which they are working.
  • Enable students to develop the employability skills and attitudes/approach that employers look for and are required of a person working in a professional capacity.

Learning outcomes

Attributes Developed
001 Identify personal strengths CPT
002 Identify key personal and professional objectives in relation to PTY CKPT
003 Understand the organisation/s and how they themselves fit within it CPT
004 Apply academic knowledge to professional practice CKT
005 Understand and demonstrate appropriate professional behaviour PT
006 Evaluate their personal and professional development CKPT
007 Use domain knowledge (scientific or business) to develop relevant data analytics applications, adopt general Data Science methods to domain specific data types and presentations, data and process models, organisational roles and relations. CKPT
008 Apply and further develop their Data Science Professional skills. 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 provide students with the knowledge, skills, and practical experience covering the module aims and learning outcomes. The learning and teaching methods include:

  • The learning and teaching methods are predicated on experiential learning through the placement experience itself.

  • The mentoring, coaching and assessment role of both the workplace supervisor and the University's Visiting Professional Training Tutor are focused on ensuring that students achieve the learning outcomes for the module; these relate to (1) personal and professional development, (2) evaluation of placement learning and (3) transfer of placement learning.

  • The learning and teaching is supported by placement visits to the students on placement by a Visiting PT Tutor to support students' critical self-reflection and learning and regular mentoring support via phone, email or teleconference.

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: COMM063

Other information

Digital Capabilities

Over the course of the placement, students will have the opportunity to apply the digital tools introduced over the course of the Masters programme in a practical setting. Further to this employers may offer bespoke training in domain specific digital technologies.


This module provides students with a year working in an industrial context and applying the skills that they have learned during their degree programme. They will learn to work within a larger team and within the workflows of their placement organisation. The year experience working in industry provides strong evidence to future employers of the student's ability to contribute in a technical role.

Global and Cultural Skills

During their placement, students will work within a team with people from different cultures and backgrounds. Computer Science is a global language and the placement opportunity allows students to build skills that will allow them to develop applications with global reach and collaborate with their peers around the world.

Resourcefulness and Resilience

Students taking a placement year must adapt to the challenges of working in an industrial context and working within a team to meet deadlines. This challenging experience will build skills that can only be learned in the workplace. Students will build experience working within company that will be invaluable in their future careers.


Computer science can be applied in many different contexts and placements are available in companies that specialise in sustainable solutions. Students will get the opportunity to select a placement provider that allows them to apply their Data Science knowledge to problems related to the UN Sustainability Goals and contribute to solutions.

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 2025/6 academic year.