Agata Ferretti: Katalogdaten im Frühjahrssemester 2023 |
| Name | Frau Dr. Agata Ferretti |
| Adresse | Professur für Bioethik ETH Zürich, HOA H 17 Hottingerstrasse 10 8032 Zürich SWITZERLAND |
| Telefon | +41 44 632 44 81 |
| agata.ferretti@hest.ethz.ch | |
| Departement | Geistes-, Sozial- und Staatswissenschaften |
| Beziehung | Dozentin |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||
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| 376-1983-00L | Foundations of Data Science | 6 KP | 2V + 2U | C. Jutzeler, S. Brüningk, A. Ferretti, weitere Dozierende | ||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from real-world data with focus on biomedical and health data. The goals are to learn how to use acclaimed software tools (pandas, scikit-learn) for acquiring, cleaning, analyzing, exploring, and visualizing data; making data-driven inferences and decisions and communicating results. | |||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | At the end of the course, a student should be able to: 1. Construct a coherent understanding of the techniques and software tools required to perform the fundamental steps of the data science pipeline; 2. Acquire data from different sources (data formats, API, open data, big data platforms); 3. Prepare data for subsequent analysis (handling missing and incorrect data; perform data quality assessments; identify and deal with outliers); 4. Solve real-world scenarios, including tackling imbalanced data and selecting suitable models; 5. Perform data interpretation (statistics, knowledge extraction, critical thinking, ad-hoc visualizations); 6. Evaluate outcomes and make decisions based on data; 7. Effectively communicate results (reporting, visualizations, publishing reproducible results, ethical concerns). | |||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | 1. Introduction to data science 2. Data wrangling (data acquisition, cleaning, handling missing data, outlier detection) 3. Data visualization and reporting results (graphic vocabulary, graph types, methods of data visualization) 4. Statistics (repetition of basics) 5. Machine learning (definition, supervised and unsupervised ML, training vs test set, cross-validation) 6. Regression 7. Classification 8. Clustering 9. Feature selection 10. Ethics in data science 11. Data science applications | |||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | - 252-0842-00L Programmieren und Problemlösen - 401-0643-00L Statistik I - 401-0643-13L Statistik II | |||||||||||||||||||||||||||||||||||||||||||||||
| 851-0745-00L | Ethics Workshop: The Impact of Digital Life on Society  Open to all Master level / PhD students. | 2 KP | 2S | E. Vayena, A. Blasimme, A. Ferretti, C. Landers, J. Sleigh | ||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This workshop focuses on understanding and managing the ethical and social issues arising from the integration of new technologies in various aspects of daily life. | |||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Explain relevant concepts in ethics. Evaluate the ethical dimensions of new technology uses. Identify impacted stakeholders and who is ethically responsible. Engage constructively in the public discourse relating to new technology impacts. Review tools and resources currently available that facilitate resolutions and ethical practice Work in a more ethically reflective way | |||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The workshop offers students an experience that trains their ability for critical analysis and develops awareness of responsibilities as a researcher, consumer and citizen. Learning will occur in the context of three intensive workshop days, which are highly interactive and focus on the development and application of reasoning skills. The workshop will begin with some fundamentals: the nature of ethics, of consent and big data, of AI ethics, public trust and health ethics. Students will then be introduced to key ethical concepts such as fairness, autonomy, trust, accountability, justice, as well different ways of reasoning about the ethics of digital technologies. A range of practical problems and issues in the domains of education, news media, society, social media, digital health and justice will be then considered. These six domains are represented respectively by unique and interesting case studies. Each case study has been selected not only for its timely and engaging nature, but also for its relevance. Through the analysis of these case studies key ethical questions (such as fairness, accountability, explain-ability, access etc.) will be highlighted and questions of responsibility and tools for ethical practice will be explored. Throughout, the emphasis will be on learning to make sound arguments about the ethical aspects of policy, practice and research. | |||||||||||||||||||||||||||||||||||||||||||||||
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