Data Science Across Disciplines
Welcome
This is the handbook for IM939: Data Science Across Disciplines, taught at the Centre for Interdisciplinary Methodologies at the University of Warwick and taught1 by Cagatay Turkay (Module leader), Kavin Narasimhan, Carlos Cámara-Menoyo and Busola Oronti.
1 For a comprehensive list of past and current staff members, refer to Section 1
What this module is about?
This module introduces students to the fundamental techniques, concepts and contemporary discussions across the broad field of data science. With data and data related artefacts becoming ubiquitous in all aspects of social life, data science gains access to new sources of data, is taken up across an expanding range of research fields and disciplines, and increasingly engages with societal challenges. The module provides an advanced introduction to the theoretical and scientific frameworks of data science, and to the fundamental techniques for working with data using appropriate procedures, algorithms and visualisation.
Students learn how to critically approach data and data-driven artefacts, and engage with and critically reflect on contemporary discussions around the practice of data science, its compatibility with different analytics frameworks and disciplinary, and its relation to on-going digital transformations of society. As well as lectures discussing the theoretical, scientific and ethical frameworks of data science, the module features coding labs and workshops that expose students to the practice of working effectively with data, algorithms, and analytical techniques, as well as providing a platform for reflective and critical discussions on data science practices, resulting data artefacts and how they can be interpreted, actioned and influence society.
Course contents
Introduction and historical perspectives (Chapter 1)
This week discusses data science as a field that cuts across disciplines and provides a historical perspective on the subject. We discuss the terms Data Science and Data Scientists, reflect on examples of Data Science projects, and discuss the research process at a methodological level.
Thinking Data: Theoretical and Practical Concerns (Chapter 5)
This week explores the cultural, ethical, and critical challenges posed by data artefacts and data-intensive scientific processes. Engaging with Critical Data Studies, we discuss issues around data capture, curation, data quality, inclusion/exclusion and representativeness. The session also discusses the different kinds of data that one can encounter across disciplines, the underlying characteristics of data and how we can analytically and practically approach data quality issues and the challenge of identifying and curating appropriate data sets.
Abstractions and Models (Chapter 10)
This week discusses ways of abstracting data. We start by visiting statistics as a means of representing data and its inherent characteristics. The session moves on to discuss the notion of a “model” and visit the different schools of thought within model-ing, as well as a tour of fundamental statistical models that help abstract data and its inherent relations.
Structures and Spaces (Chapter 14)
This week explores the notion of structures and how data science can enable the extraction of “hidden” underlying groups – clusters – and hierarchical structures from data. We discuss the different techniques to surface and generate artificial boundaries and how the resulting artefacts can be interpreted. This session then investigates how artificial and abstract spaces can be constructed through different “projection” techniques, and how these spaces help us navigate data that are high-dimensional in nature and apply analytic frameworks to them.
Multi-model Thinking and Rigour in Data Science (Chapter 19)
This week we focus on multi-model approaches as a way of thinking and how critical, pluralistic thinking can improve our understanding of the underlying phenomena implicit in data. We also discuss how to adopt a comprehensive approach to the data science process, and investigate indicators of rigour in data science.
Recognising and Avoiding Traps (Chapter 25)
Data analysis and statistical routines and procedures are ingrained with several pitfalls and limitations – these range from methodological pitfalls in the processes and data that once can use, to cognitive and behavioural pitfalls that one can come across in making inferences from data and data artefacts. This week we discuss such theoretical and practical traps and pitfalls, how we can be aware of them and what approaches we have to avoid them.
Data Science and Society (Chapter 31)
We will engage with academic and practices discourse on the social, cultural and ethical aspects of data science, and discuss around how one can responsibly carry out data science research on social phenomena, whether data science can be a transformative power in society, and what ethical and social frameworks can help us to critically approach data science practices and its effects on society, and what are ethical practices for data scientists.
Data Science Workshop 1: Design Thinking in Data Science (Chapter 35)
This week explores the question “Can we approach data science as a design problem?” and discusses how one can embrace a user-centred approach to design appropriate data science processes. We will do this through hands-on practical where we go through the data science process over an applied case.
Data Science Workshop 2: Bring Your Own Data (Chapter 37)
This workshop week will involve you working hands-on towards your final assessments individually but in small coding groups. In the workshop session, we would like to hear from you on your ideas for your second coursework. You will also be able to use this session to start working on the dataset(s) that you have decided (or still considering) to analyse as part of your second assessment and discuss with your peers and with the staff members.
Acknowledgements
This handbook has been created by Carlos Cámara-Menoyo and Cagatay Turkay, based on the materials from previous years created by Cagatay Turkay, James Tripp and Zofia Bednarowska-Michaiel.