Summary of module for applicants:

This module aims for students to conduct individual practical experience evaluation and academic research work around their own interest in the field of Data Science. Students are expected to demonstrate their ability of mastering the knowledge and skills acquired in taught modules. Students can negotiate with the team one of two alternative routes to completing a dissertation: either an evaluation of practical work experience (part 1, up to 50% of the mark as agreed with the teaching team) plus a related piece of academic research (part 2), or a single, extended, individual piece of academic research.

Main topics of study:

• Identification of a suitable topic within the Data Science research agenda
• Literature search and review, and defining research questions
• Research design and choice of appropriate methods; ethics approval
• Practical experience in evaluating: data management issues, data ethics, workflow and data pipeline, data quality, model quality, potential for bias/discrimination in data products
• Academic writing for documenting outcomes of work experience and/or research
• Plagiarism, citation and referencing
• Oral presentation

Learning Outcomes for the module

• Digital Proficiency - Code = (DP)
• Industry Connections - Code = (IC)
• Emotional Intelligence Development - Code = (EID)
• Social Intelligence Development - Code = (SID)
• Physical Intelligence Development - Code = (PID)
• Cultural Intelligence Development - Code = (CID)
• Community Connections - Code = (CC)
• UEL Give-Back - Code = (UGB)

At the end of this module, students will be able to:

Knowledge

1 Demonstrate an advanced knowledge of a specific, chosen topic in the field of Data Science and to communicate this knowledge through both a written dissertation and an oral assessment.

2 Understand how research works, particularly how literature review, research design and choice of methods are important in establishing the validity and potential impact of a project.

Thinking skills

3  Use critical thinking in assessing the work of others in order to assimilate their methods and results within a new project (IC, EID)

4  Evaluate key aspects of Data Science projects and/or work experience according to established and evolving standards and practices: data management issues, data ethics, workflow and data pipeline, data quality, model quality, potential for bias/discrimination in data products (DP, IC, SID, CC)

Subject-based practical skills

5  Write a substantial dissertation/reports containing a practical element representing at least 30% of the overall contribution to the work (IC, PID, EID)

Skills for life and work (general skills)

6  Have a critical awareness of knowledge production through research  (IC, PID, EID)

7 Plan and manage the production of an extended piece of work to specification and to a fixed delivery date (IC, PID)

8 Have an informed view of potential doctoral research in relation to career choices (UGB)