This is an open access title available under the terms of a CC BY-NC-ND 4.0 International license. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations.The Egyptian al-Jama'a al-Islamiyya and Islamic Jihad have shaped the trajectory of jihadi salafism since its inception and defined a key strategic divide between mass-movement mobilization and elitist avant-gardism. Despite their shared histories, however, al-Jama'a al-Islamiyya rejected al-Qaeda's transnational violence and became a political party after 2011, whereas Islamic Jihad has formed the backbone of Osama bin Laden's organization. These strategic divergences are puzzling since these groups emerged in the same country around congruent ideologies. Institutionalizing Violience develops an institutional approach to radicalization to compare the two groups' comparative trajectories. It is based on extensive field research conducted with their leaders and members in Egypt. The interviews provide a unique perspective on how jihadi groups make and implement new strategic decisions in changing environments, as well asthe evolution of their approaches to violence and non-violence.Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitivegroups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data.