RESEARCH, ETHICS, AND SECURITY IN DATA SCIENCE - 2025/6

Module Overview

This module introduces students to the research skills required to engage in data science projects in both industry and academia, whilst also covering the relevant ethical and security considerations when designing and implementing data driven projects. Areas of specific concern for ethics and security in machine learning and statistical analysis are highlighted.

Module provider

Computer Science and Electronic Eng

Module Leader

THORNE Tom (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 2

Prerequisites / Co-requisites

N/A

Module content

Indicative module content is as follows:

Research skills, including literature searching and writing literature reviews.

Developing research proposals, and planning research projects.

Writing and structuring reports for a range of audiences, including academic writing.

Ethical principles and academic integrity.

Bias in machine learning.

Working with personal data and legal frameworks for data privacy.

An overview of potential security threats and security fundamentals.

Data science specific security threats and security in the data lifecycle.

Assessment pattern

Alternative Assessment

An individual design project covering the same learning outcomes

Assessment Strategy

The assessment strategy is designed to allow students to demonstrate their ability to work in a group to design a research project whilst taking into account ethical and security considerations. Thus, the summative assessment for this module consists of:

Group Research Project Design assignment ¿ This will involve working in a team to develop a research project proposal and plan. A group report will be submitted and will be clear about the group contributions made as well as allow each member to provide reflection on the awareness of their team skills. (LO1-5)

Individual Poster presentation ¿ Students will create individual posters for their group research projects and present them at a workshop session. (LO1-5)

Formative assessment:

Formative assessment will be provided in this module in the following ways:

Students forming reflective evaluation of their performance in team work.

Feedback:

Formative feedback will be provided in this module in the following ways:
Feedback through question and answer tutorials held at suitable points in the semester close to submission deadlines.
Enquiries through email.

Module aims

• The module aims to prepare students to carry out real world projects in data science, in terms of the research skills, and the knowledge of ethical and security concerns required.
  
  In research skills the module aims to prepare students for key competences they require in carrying out a research project. These include the ability to independently search and review the literature, and to plan a research project.
• In ethics, the module aims to expose students to the relevant considerations for data science projects, including areas specific to data science such as algorithmic bias. The module aims to engage students in applying critical thinking to these questions, as well as having a solid understanding of existing regulations around personal data and privacy.
• For security, students should be aware of the common threats and also those specific to data science projects. The module aims to familiarise students with possible threats and also the fundamentals of security, as well as developing students¿ ability to plan data science projects with end to end security in mind.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Provide students with practical experience in conducting data science research, and applying critical thinking around ethical and security considerations for data science projects.

The learning and teaching methods include:

Nine weeks of lectures covering topics in the module.

Online guided learning tasks delivered each week to enable self-paced learning via learning tasks.

Question and Answer Tutorials at appropriate points in the semester to help discuss and deepen the contextualisation of the material studied.

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: COMM072

Other information

The school of Computer Science and Electronic Engineering 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: Use of academic literature searching will be crucial in forming digital capability to find the relevant literature required and suitable books for reference. Furthermore referencing literature will be required. Employability: Ability to work within a diverse group of people to develop and plan a research project, will form a wide breadth of employability skills that are widely transferable. Global and cultural capabilities: Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world. Resourcefulness and Resilience: To develop resilience in completing a constant flow of work through a single semester in keeping to the task at hand regularly in order to meet the deadlines set. To ensure that this also is maintained where it involves dependency on working with others and to encourage one another in completing a task that will flourish with resourcefulness.

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.