The workshop will take place on August 18, 2023, on Zoom and Youtube Livestreaming
About The Data Science Summer School
The Data Science Summer School is a series of theoretical and practical workshops on the exciting methods and technologies currently employed by industry, government, and civil society to address the world's most complex problems today. It is organized by the Hertie School Data Science Lab with funding and support from the Hertie School and the Dieter Schwarz Foundation
An absolute must-have knowledge for data scientists is statistics and probability. They gives us the language and tools to quantify the uncertainty of events and reason in a principled manner. Machine learning is about developing predictive models from uncertain data, with imperfect or incomplete information. We can manage this uncertainty using the theories and tools of these two disciplines.
To be updated
All workshop materials and recording are under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. However, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- Session slide deck
- Law of large numbers colab notebook
- Analysing the tail colab notebook
- First experiments colab notebook
- Independence and dependence colab notebook
Nikolas Nüsken obtained his PhD from Imperial College London in 2018. After a short stay at the Alan Turing Institute, he worked as a postdoctoral researcher at the University of Potsdam within the Collaborative Research Centre "Scaling Cascades in Complex Systems". He joined King’s College London in 2022 as a Lecturer in Mathematical Data Science. His research interests lie at the interface of computational statistics, machine learning, and the mathematics of stochastic dynamics and interacting particle systems.
Schedule (Central European Summer Time - CEST)
Statistics and Probability for Data Science (Part I)
Statistics and Probability for Data Science (Part II)