果冻影院

XClose

果冻影院 Module Catalogue

Menu

Mining Social and Geographic Datasets (GEOG0051)

Key information

Faculty
Faculty of Social and Historical Sciences
Teaching department
Geography
Credit value
15
Restrictions
Please note that where spaces are limited priority will be given to Geography students. The module is designed to have a large practical component where the coursework will be based on a programming project in Python. Even though the knowledge of Python or another programming language (in addition to R) is not a pre-requisite, the module requires a considerable amount of practical programming work in Python. In 2024-25 this module will not be open to undergraduate students, those undergraduates wishing to learn more about Python should take module GEOG0178 Machine Learning for Social Sciences with Python.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

We constantly leave 'digital traces' in our daily lives, both in online and offline worlds; for example our posts in online social networks. Often, this information is associated to specific geographic locations. Examples are GPS trajectories collected using mobile devices or geolocalised posts in online social networks. This data can be collected, analysed and exploited for many practical applications with high commercial and societal impact. This course will provide an overview of the theoretical foundations, algorithms, systems and tools for mining and for discovering knowledge from social and geographic datasets, and, more in general, an introduction to the emerging field of Data Science. The module aims to equip student with the foundations as a data analyst/scientist to be able to analyse a wide array of social and geographic data in the future.

Lecture topics will possibly include: introduction to key concepts of data mining; an introduction to computing in Python; spatial network analysis for urban planning/design; mobility analysis and modelling; and an introduction to machine learning techniques on social media and sensing data with real-world case studies and applications.

The course will usually consist of approximately 10 lectures and 10 practical sessions.

References

Anaconda (2022). Anaconda website. https://www.anaconda.com/products/individual

John V. Guttag (2013). Introduction to Computation and Programming Using Python. MIT Press 2013. Chapter 2&3

McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.鈥�

VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.".

听

听

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
100% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
20
Module leader
Dr Stephen Law
Who to contact for more information
geog.office@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

听

Useful links

Sustainability

For a list of modules related to climate change as well as social and environmental sustainability at 果冻影院, type 'climate' or 'sustainability' into the search. Visit Sustainable 果冻影院 for information on extra-curricular activity on sustainability.

Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

听

听