Data Analysis with Python - Presentation and Content for Each Unit

In two units that build upon each other, we offer you a chance to develop your own data analysis and machine learning skills in data analysis or sometimes data analytics (https://en.wikipedia.org/wiki/Analytics).

Analytics has a strong tradition, very established methods, and a broad range of applications in culture, economy, and society. The two social media traditions and cultural analytics/analysis are particularly relevant to us. Social media analytics (https://en.wikipedia.org/wiki/Social_media_analytics) extends general analytics toward analysing social (media) relationships. Cultural analytics (https://en.wikipedia.org/wiki/Cultural_analytics) builds on successful analytical methods and applies its techniques and methods to studying cultural objects.

In the course, you will learn about the theoretical and practical foundations of an analysis of socio-cultural objects. While experimenting, you will also learn about the limits of the current capacities and the exciting opportunities.

Before you reach these opportunities, we will take you through the relevant foundations of Python in the first unit. This might seem abstract sometimes, but we promise to take you to the interesting parts as quickly as possible. The second unit presents digital methods from association mining to network analysis.

Unit I: Basics

The course's first module provides an introduction to interactive Python and a first real-life case study at the end. We focus on using Python for data exploration. You will master the basics of data analysis in Python, including NumPy, SciKit-learn and Matplotlib. Once you finish this short course, you can move on to unit two, where we will start with machine learning. 

Unit II: Machine Learning for Society and Culture

The second unit introduces interactive data analysis with Python. Students will work through several research use cases using basic machine learning. We will use association mining to analyse communities of practice. One of the most intuitive techniques of data analysis is community detection. In the second lesson, we will explore political opinions in the US Congress and how users of a social network share interests. We also employ network analysis to split a small community network into groups and clusters before finally learning more about visualisation and image analysis in the third session.

Course developers: 

Tobias Blanke,
University Professor of Humanities and AI at the University of Amsterdam. Tobias holds a PhD in Political Philosophy and Informatics. His research focuses on big data and its implications for culture and society.

Giovanni Colavizza,
Assistant Professor of Digital Humanities at the University of Amsterdam. Previously, Giovanni did his PhD at the Digital Humanities Laboratory of the EPFL in Lausanne and has been a senior data scientist at the Alan Turing Institute. His research focuses on using data to study cultural and social phenomena, including the human past, the arts market, open science, and Wikipedia. Giovanni also works on developing responsible AI workflows in the cultural and creative sectors.

Zarah van Hout, 
Research Assistant in Digital Humanities at the University of Amsterdam. Zarah has a background in Artificial intelligence and is currently finishing her Master's in Logic at the University of Amsterdam.

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