This course will teach you how to do data science with R. In recent years, data analysis skills have become essential for those pursuing careers in policy advocacy and evaluation, business consulting and management, or academic research in the fields of education, health, medicine, and social science. This course provides students with advanced data science skills using the powerful R programming language.
Python is a versatile and expressive programming language that is becoming more and more important in data science and analysis. Python has become essential in modern day applications of Machine Learning and NLP. This course is an introduction to the Python programming language for students without prior programming experience. We cover data structures, control flow, object-oriented programming and algorithm analysis. Upon its completion, students will master foundational concepts of programming and be able to write professional-grade Python code.
Public policy and data-driven organisations are complex arenas, and the ability to uncover causal connections rather than simple correlations is vital in evidence-based decision making. This course teaches the analytical framework of contemporary causal inference, which isconnected to modern statistical and machine learning methods.The course covers a comprehensive array of topics and guidelines on designing and implementing causal evaluation research based on the latest methodologies. Special emphasis will be given to the application of causal analysis for policymakers and development practitioners.
Machine learning is a core technology of artificial intelligence and data science that enables computers to operate without being explicitly programmed. Recent advances in machine learning have given us innovations behinds self-driving cars, AlphaGo, Amazon, and Netflix. This technology has also allowed us to predict armed conflict and post-electoral violence, detect fake news, develop targeted provision of care and public services, and implement early policy interventions. This course provides a hands-on introduction to machine learning. The course covers topics in supervised and unsupervised learning, including the most common learning algorithms such as regression, classification, random forests, clustering, and dimensionality reduction. Students will learn the fundamental concepts underlying machine learning algorithms, but will equally focus on the practical use of machine learning algorithms using open-source frameworks.
Natural Language Processing (NLP) is a key technology of the information age. Automatically processing natural language outputs is a key component of artificial intelligence. Applications of NLP are everywhere as people and institutions largely communicate in language. Recently statistical techniques based on neural networks have achieved a number of remarkable successes in natural language processing leading to a great deal of commercial and academic interest in the field. This course provides an overview of modern data-driven models to richer structural representations of how words interact to create meaning. We will discuss salient linguistic phenomena and successful computational models. We will also cover machine learning techniques relevant to natural language processing.
In the study of politics, text of one kind or another is often essential to measuring important underlying concepts, e.g. policy sentiment, issue frames, or ideological positions. It also poses special challenges, notably to machines but also to researchers who simply cannot themselves read everything their research designs require and must therefore look to operationalise and extend their own understanding using quantitative tools. This course is about the kinds of statistical and computational tools that political scientists and policy analysts have found useful for treating text 'as data'.
If programming is an art, then data structures and algorithms are the colours and strokes with which programmers display their expertise. Data structures are the manner in which data is stored so that algorithms can operate on them. Algorithms, in turn, are instructions on how to handle data efficiently to achieve a desired result. By the end of the course, students will understand these elementary building blocks of programming, solve simple coding problems, evaluate the complexity and efficiency of algorithms, and know the best use cases for each type of data structure.
Mathematics is foundational to data science. This course aims to deliver compact and tailored introduction to the core mathematical concepts of data science. Upon completing the course, students should have a broad understanding of linear algebra, probability and statistics, and optimisation necessary for data science.
Innovations in Artificial intelligence (AI) are transforming economies and societies globally, and with them politics. This course explores these transformations and corresponding policy challenges. As a governance school Hertie has a special responsibility to address these critical topics. Integrating perspectives from both natural and social sciences, this course will provide learning experiences that examine the impact of AI on humans and societies. We will explore the proliferation of algorithmic decision-making and autonomous systems; the issues of ethics, fairness, transparency and accountability raised by AI techniques such as machine learning; balances and interactions between regulation and innovation; the effects of AI on human rights and economic wellbeing; the global AI arms race; and increasing oppressive capabilities of state- and non-state actors. We consider both public and private strategies of regulation, and local, national, and transnational aspects of governance.
This course offers students the opportunity to reflect on the extensive methods and techniques they have acquired and learn how to make optimal decisions based on the evidence at hand. It also provides relevant insights into the day-to-day operations of data and policy practitioners in the field and how their choices affect the work of their organisations at large. By the end of the course, students should have understood the connection between data science theory and practice and how public policy can employ them for better decision-making, have networked and engaged with professionals from different sectors and obtained a better vantage point on their career paths. The course begins with a formal introduction to decision theory and how to reason under a multitude of choices, information and data sources, especially when the decision taken will have real-world consequences. Following this, the second part of the course will feature practitioners from government, industry and non-profit organizations to explain in detail how these principles and theories are applied in reality. Depending on availability, the course seeks to include a range of speakers to generate a lively discussion on how data-driven decision-making is implemented in different scenarios and professional settings.
Artificial Intelligence and Machine Learning have been dominating the headlines in the last few years coming with a lot of promises also for transforming government work. Whether it is to gain efficiency in current processes, improve serviced delivery or transform decision making and service delivery, there are many ways to utilise artificial intelligence technologies in a government context. What do these new technologies mean? What are these technologies and where can they be applied in a government context? What benefits can public sector organisations derive from deploying such technologies and how can they go about and embed them to deliver tangible benefits? What are the key management challenges in implementing such technologies and how can they be addressed? This course aims to demystify these concepts by looking at government implementation experiences. We look beyond the hype and focus on the real challenges and opportunities of practical applications of AI for government organisations. We also consider challenges and opportunities arising from ethical, fair, transparent, and accountable deployment of artificial intelligence and will look at key factors for successful implementation such as data management and data sharing, public sector innovation, project management, change management, cross-sectoral collaboration and safe implementation.
Artificial Intelligence, Machine Learning, and Data Science have been dominating the headlines in the last few years. But what does it all mean? What are these technologies and how are they linked? What benefits can organisations and businesses derive from deploying such technologies and how can they go about and embed them to deliver tangible benefits? What are the governance implications of artificial intelligence deployment? This course aims to demystify these concepts and highlight direct business and societal benefits. Navigating through the complex maze of these rapidly evolving technologies can be non-trivial for organisations of different size and market maturity. We look beyond the hype and focus on the real challenges and opportunities of practical applications of such technologies for organisations. Whether it is to gain efficiency in current business model or transform decision making and product or service delivery, there are many ways to utilise artificial intelligence technologies. We also consider challenges and opportunities arising from ethical, fair, transparent, and accountable deployment of artificial intelligence.