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Quantitative Methods 2: Data Science and Visualisation
This module is designed to lead on from knowledge gained in Quantitative Methods in Year 1 and prepare you for your final year dissertation.
Links to First Year Quantitative Methods (BASC1003)
This new course is strongly complementary to the first year BASc Quantitative Methods course, which builds modeling approaches around real-world questions. Here, we put society under a data-driven lens in an evidence-based approach to understanding and explaining the world. We will work closely with first year QM module director Hannah Fry to find common and complementary elements in tools and methodology that can be amplified for our module. Students from all pathways who enjoyed Hannah’s course are welcomed.
Links to the final year undergraduate dissertation
Students who have completed the first year Quantitative Methods course will have an independent, project-based approach to learning, with some experience in practical programming and basic mathematical methods. Students who study our module will also develop their programming skills, understand where and how to access and work with data, and have a more mature perspective on the applications and impacts of data-led research. Together these will form a strong foundation for an analysis-, model- or data-led undergraduate dissertations, especially in interdisciplinary work in the quantitative social sciences or humanities.
Please note that, for the BASc final-year dissertation, the highest marks will be awarded to creative interdisciplinary projects.
How is data structured and how does this affect the ways in which we work with it? Covering numerical data, text analysis, geographical data, network science, linked data (and semantic web), normalization and scaling, and social media data.
How is data accessed, analysed and communicated, and what are its impacts? Principles of design for mapping and data visualization, graphing, the Open Data movement, Government and Census data, private data and ethics, aggregation and uncertainty.
These strands are covered by weekly lectures and colloquia based on set readings, giving students the chance to discuss and develop an understanding of where to find data, what techniques to use, what this tells us, and issues around communication and extraction in a societal context.
Students will be taught the use of Python libraries (matplotlib, Pandas, basemap and nltk) for importing and working with raw data to produce tables, scatter graphs, histograms, summary statistics (range, mean, median, mode, IQR, standard deviation, geometrical mean, centroids of geographical data) and maps, and how these techniques facilitate insight and understanding. Through IPython notebooks, we build a literate programming framework which allows narrative, explanation, image, weblinks and equations to complement the exercises.
This will be delivered through two hours of workshops and associated homeworks.
At the end of this module, we want students to have a bedrock of technical skill paired to the context of social data; practical, political, communicative, ethical and transformative.
To ensure the delivery of quantitative skills, there will be a weekly 2-hour Python workshop for all students, and we will develop additional out-of-class exercises to give students additional experience with the Python programming language and the underlying concepts with which they will be working.
NB. This new BASc Core course is an adaptation and development of BASC2022 Digital Literacy and Data Visualisation. The original BASC2022 course was offered in 2013-14 to 2nd years as one of the interdisciplinary electives. Current 1st years who have previously been thinking of taking BASC2022 Digital Literacy and Data Visualisation may now take this course instead plus an additional interdisciplinary elective, provided that they are aware that the new QM2 course has a stronger quantitative element.
This module is taught in Term 1 of Year 2. There are 4 (all are compulsory) contact hours each week, split as follows:
||Dr Martin Austwick from CASA (Centre for Advanced Spatial Analysis) and Dr Oliver Duke-Williams from Digital Humanities|
||12-2pm on Fridays|
100% coursework, split as follows:
- Project - 60%
- Essay - 30%
- Presentation - 10%