2024/25 Taught Postgraduate Module Catalogue

GEOG5302M Data Science for Practical Applications

15 creditsClass Size: 120

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

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2024/25

Module replaces

GEOG5402M

This module is not approved as an Elective

Module summary

This module will provide a foundation in data science training, introducing concepts in data handling, exploratory data analysis, machine learning, and visualisation. The course will provide students with opportunities to work with a variety of spatial and spatiotemporal datasets relating to multiple systems (e.g. both urban and wider environmental). The course will embed good practice in data science production through code notebooks, and in open science methods (e.g. through GitHub). The course will aim to become language agnostic but beginning by providing code and opportunities to submit assessments in a specified programming language, which will allow students to shape their learning to match optional course requirements.

Objectives

This module aims to:

1. Introduce students to data science in urban and environmental contexts, providing a thorough foundation of data sets, analytics and applications across a diverse range of contemporary urban and broader environmental contexts.

2. Develop students’ understanding of the appropriate steps and techniques for effective data processing and visualisation, fostering independence and confidence in coding though practical approaches, utilising notebooks and open science methods.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:

1. Select and demonstrate all steps of data handling and analytics, including wrangling, description, analysis, machine learning and visualisation
2. Evaluate the core concepts and applications of regression, clustering, classification, and machine learning techniques, to identify and perform the appropriate implementation of each
3. Investigate a range of non-spatial, spatial, and temporal datasets, demonstrating their application to a range of real-world systems


Skills Learning Outcomes

On successful completion of the module students will have demonstrated the following skills learning outcomes:

1. Develop technical proficiency through the study, selection, and application of programming and data analytics skills
2. Demonstrate critical skills by gathering information, analysing and interpreting data to aid understanding and anticipate problems
3. Develop digital skills to solve problems, make decisions, and answer questions

Syllabus

Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lectures	11	1.00	11.00
Practicals	11	2.00	22.00
Private study hours			117.00
Total Contact hours			33.00
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Follow up tasks will be provided during each lab for self-study. Three of these will be practice tasks and/or reading, to gain practice and build confidence, and four of these will be short data exercises which will be submitted the following week for formative feedback. Students will have chance to ask any questions and begin these during the labs. The remainder of the weekly tasks will be summative.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Coursework	30.00
Assignment	Coursework	70.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 01/10/2024

Disclaimer

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