For BA/BSc Geography (International) students only. The aim of this module is to allow students to reflect on the progress they have made on their final year...

Study Abroad Year Dissertation Report (GEOG0068)

For BA/BSc Geography (International) students only. The aim of this module is to allow students to reflect on the progress they have made on their final year dissertation.

We constantly leave &#039;digital traces&#039; in our daily lives, both in online and offline worlds; for example our posts in online social networks. Often,...

Mining Social and Geographic Datasets (GEOG0051)

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.

Please note that the module description is subject to change due to staff changes.

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.”

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

 

 