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The purpose of this module is to introduce the fundamental concepts and methods that are essential for social and geographic data science. The module will also provide practical data science skills for a variety of application domains.

The module will provide an introduction possibly on: python computing, data preparation/wrangling, exploratory data analysis (EDA), fundamental concepts applied machine learning and applied social network analysis. The module aims to equip student with the foundations as a data analyst/scientist in the future.

The module is designed to have a significant practical component where the coursework will be based on a programming project in Python using the Ipython Jupyter notebook installed via Anaconda. Even though the knowledge of Python or another programming language (in addition to R) is not a prerequisite, the module requires a considerable amount of practical programming work in Python. To get the most of the module, we recommend students to take a look at the references in Python Computing before the start of the class.

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.鈥�

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