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2022/23 Taught Postgraduate Module Catalogue

GEOG5405M Urban Data Science Project

60 creditsClass Size: 50

Module manager: Vikki Houlden
Email: v.houlden@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2022/23

Pre-requisite qualifications

N/A

Module replaces

N/A

This module is not approved as an Elective

Module summary

The Project will take the place of the traditional dissertation. The Project submission will incorporate a data science output, in the form of a ‘notebook’-style analysis, and an associated written report, which outlines prior work and context, the analysis process, the results, and associated policy or practical implications. The submission will be expected to be similar to a dissertation in the expected level of work and quality. We encourage students to organise co-supervision between academics in urban analytics (Geography, LIDA), Maths, and Computing. We also seek to leverage connections with CDRC for project partners

Objectives

This module draws together all of the skills and knowledge gained throughout the year. This large project will see students become a more independent researchers and analysts, presenting their results in a practical format to provide societal impact and applications. They will build collaborations with multiple supervisors and industrial partners to tackle contemporary challenges. They will produce a research proposal and develop accessible outcomes in code notebooks and written reports for various real-world audiences.

Learning outcomes
1. Collaborate with industrial partners and multidisciplinary supervisors to study a real urban challenge

2. Work independently, with the support of their supervisors and partners, to lead their own research project

3. Integrate the technical and subjective-specific knowledge gained throughout their previous modules to deliver a solution to their chosen data science challenge

4. Develop a data science notebook output documenting their data, approach and findings

5. Present and narrate the contextual findings in an accessible practical report for diverse audiences

6. Demonstrate good project management, planning, research and communication skills 1. Collaborate with industrial partners and multidisciplinary supervisors to study a real urban challenge

2. Work independently, with the support of their supervisors and partners, to lead their own research project

3. Integrate the technical and subjective-specific knowledge gained throughout their previous modules to deliver a solution to their chosen data science challenge

4. Develop a data science notebook output documenting their data, approach and findings

5. Present and narrate the contextual findings in an accessible practical report for diverse audiences

6. Demonstrate good project management, planning, research and communication skills


Syllabus

- Identify project partners and supervisors from across urban analytics, maths, computing

- Alongside their partners, develop a plan to address a real-world urban data science issue

- Lead and manage an independent project

- Conduct a literature/contextual review

- Identify and collate data

- Clean and pre-process data

- Plan stages of analysis/investigation

- Perform exploratory investigations, analyses and machine learning

- Visualise and present findings

- Interpret and contextualise results

- Derive implications of findings

- Record all stages through a notebook-style analysis

- Prepare a written report to present project findings and impact in an accessible, contextual and practical format

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lectures12.002.00
Supervision Meetings50.502.50
Seminars31.003.00
Practical12.002.00
Private study hours590.50
Total Contact hours9.50
Total hours (100hr per 10 credits)600.00

Private study

Most of this module will be independent study with the support of supervisors and further support available in regular drop-in sessions with academic staff.

Opportunities for Formative Feedback

Formative feedback available during supervisor meetings and optional project drop-in sessions. Feedback will be returned with the Project Proposal forms, which will be submitted to assist with matching students and supervisors, while ensuring appropriate project scope. Students will also submit an interim poster (5%) (research rationale, literature summary and research design); formative feedback will be provided during the Poster Showcase as well as from supervisors.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
Poster PresentationEquivalent to 500 to 1000-word, forms interim progress report5.00
Total percentage (Assessment Coursework)5.00

The poster (created from a provided template) is a progress check during the Project and will be presented at a Poster Showcase which will be attended by students, supervisors, and other interested members of the School. It will provide experience of poster presentation, networking, and act as a showcase for the students’ dissertation topics to each other and the School. The digital poster will be submitted for assessment.

Reading list

The reading list is available from the Library website

Last updated: 06/05/2022

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