Data Science International Summer School

Goal:

The goal of the summer school is to familiarize students with relevant state of the art topics in data science. The program will cover fundamentals of data science and focus on the following key data science topics:

• Data analytics and statistics
• Machine learning
• Time series and graph data
• Conversational AI
• Data pipelines (data enrichment pipelines, machine learning pipelines)
• Data sharing

The program will consist of a combination of lecture-style talks introducing various data science paradigms and methods, and hands-on sessions.

The summer school aims to have a practical orientation, with Python and Jupyter Notebooks being used to exemplify many of the topics covered at the summer school.

At the end of the summer school, the students are expected to have an understanding of key paradigms used in data science and be able to practically apply them in data science projects.

Prerequisites:

Familiarity with computer programming and basic knowledge about Python, interest in working with data, enthusiasm, and willingness to learn new things!

Basic knowledge of linear algebra, probability theory, and knowledge representation would be useful, though not strictly necessary.