Python for Arts, Humanities and Social Sciences
- Room:
- Liffey A
- Start (Dublin time):
- Start (your time):
- Duration:
- 30 minutes
Abstract
Computational methods particularly those involving data analytics are now taking root in various humanities disciplines. However, students and researchers working in these disciplines lack the necessary programming proficiency and coding experience . The need then arises to make Python-based computational methods accessible – we present case-studies of how to do this via various Python modules being taught at College of Business in Technological University Dublin and by means of walkthrough of an interdisciplinary social good project called InEire. It comes down to complementing existing quantitative and qualitative methods with methods based on analysis of various types of data specific to the social science problem being solved. We essentially go through the process of building curiosity-driven exploration in social science students via a theoretically driven research question rather than the Python technique itself, and then focusing on the various steps involved in solving that question; and finally boiling it down to a concrete Python-based data analytics methodology. This project-based teaching methodology helped us develop Python skills in newbies eventually leading to a Python-based data analytic skills in students of disciplines other than Computer Science.
TalkEducation, Teaching & Further Training
Description
The various areas within humanities and social sciences such as political science, sociology, psychology, economics etc. have evolved to a point where they have been complementing existing qualitative and quantitative methods with methods rooted in data science. This shift in paradigm is primarily driven by real-world, publicly available data sets that cover a variety of scholarly domains and have the potential to solve fundamental research questions in these intriguing fields. There is however a huge bottleneck to be overcome before realizing the full potential of data science in arts, humanities and social sciences; and that bottleneck relates to a fear of programming in students/researchers within these disciplines. Our talk presents some tips and tricks from course modules being taught in Technological University Dublin; the fundamental idea is to present an overview of data science toolkit and how it relates to problem solving in the real world.
First we will present ways to make the data science lifecycle being made easy via tools such as Google colab and Jupyter notebooks followed by explaining how showing students the big picture and the workflow lifecycle of a data science technique helps grasps concepts in a very effective manner. We will present examples from exploratory data analysis and classification using data-driven research questions; and look into elegant solutions that can easily be plugged into a social scientist’s skillset. Examples of continuous assessment projects based on different Python libraries are also presented with a view to further establish use of Python as a valuable tool for arts, humanities, and social sciences.
Finally, we will give an overview of an Irish research council funded project emanating from a combination of STEM and HUMANIITIES disciplines that aims to perform an economic assessment of anti-immigrant sentiment in Ireland. Various phases of the project will be explained emphasizing particularly those where Python tools play a major role in interpretion of research outcomes. These research outcomes obtained through smart use of Python data analytic tools play a key role in building relationships between data scientists and policy makers in both government and not-for-profit sector.