10 top lecturers will try 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, Causal inference, Time series, graphs, text processing, Data visualization, Data pipelines (data enrichment pipelines, machine learning pipelines) The program will consist of a combination of lecture-style talks introducing various data science paradigms and methods, hands-on sessions, and student projects. The participation is open to students and professionals trying to boost their perspectives and embark on the data wave.
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
- Causal inference
- Time series, graphs, text processing
- Data visualization
- Data pipelines (data enrichment pipelines, machine learning pipelines)
The program will consist of a combination of lecture-style talks introducing various data science paradigms and methods, hands-on sessions, and student projects.
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.
More details and registration forms available here: https://datascience.ase.ro.