The Graduate Summer School in Data Science is an educational experience designed to explore the main challenges and opportunities in data analysis. Students will gain a deep understanding of the fundamental principles of data science and the skills necessary to analyse and interpret data.

The aim of the Summer School ‘Data Science and AI in Health’ is to introduce young, enthusiastic researchers to the principles of data science and AI in healthcare and clinical research. Participants will be introduced to various aspects of data science, such as setting up and implementing a project. This includes both the collection and cleaning of data and the practical conduct of an initial (machine learning) analysis of the collected data.

A hands-on introduction to the methods of principles of data science. Strategies for analyzing and visualizing tabular data, including common patterns and pitfalls. Data acquisition through web scraping and REST APIs. Core principles of machine learning: supervised vs. unsupervised learning, training vs. test error, hyperparameter tuning, and ensemble methods. Introduction to data of different shapes and sizes, including text, image, and geospatial data.

The IEEE-EMBS International Summer School on Advanced Data Analytics in Physiology is organized by the IEEE EMBS Technical Community on Biomedical Signal Processing and the IEEE EMBS Technical Community on Cardiopulmonary Systems and Physiology-based Engineering.

In the Summer Course in Data Analysis in R, participants will explore the omnipresence of data and the necessity for essential analytical skills to extract valuable insights. The course leverages the extensive array of R packages, created by a myriad of active programmers, making R a versatile tool for a spectrum of data analysis techniques. From fundamental hypothesis testing to advanced methods like generalized linear regression and multivariate analysis, including principal component analysis, factor analysis, and clustering, participants will immediately apply their acquired knowledge through short exercises using Rmarkdown.

Storytelling skills are among the most important skills today – the skills we all should practice. When combined with data, stories can become powerful tools in the digital industry. The module aims to teach you how to think out of the box and create interesting and relevant stories using data.

Introduction to key concepts from calculus (differentiation and integration), probability (discrete and continuous random variables), and linear algebra (vector spaces, matrices, and linear systems).

Discrete and continuous time Markov chains, Poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics master's students may want to consider taking STATS 215 instead.

The 6th International Summer School on Resource-Aware Machine Learning (REAML 2022) will be held this year from Sept. 12-16, 2022. The school provides lectures on the latest research in machine learning, typically with a twist on resource consumption and how these can be reduced.

Unravel complex systems and visualize their interactions. Experiment and reimagine them through interventions, algorithms, models, and simulations. Communicate your understanding through powerful system visualizations.