Free online course from the Hasso Plattner Institute (HPI) shows how to analyze large data sets and visualize the results.
The “Data Science Bootcamp“ starts on June 7th on the learning platform openHPI. Participants work with real data and programming tasks that require basic knowledge of the Python programming language.
Two HPI doctoral students lead the English-language boot camp: Mohamed Elhayany and Hendrik Steinbeck. They introduce participants to Jupyter Notebooks, an interactive web platform that enables scientific calculations and visualizations via a web browser.
“This means that no one in our course has to install a special programming environment on their local computer,” emphasizes Elhayany. This is intended to make it easier to get started with programming, visualizing, analyzing and extracting data from various sources.
The two experts want to take those interested on a journey of discovery into the world of scientific data analysis. “In order to be able to fulfill the requirements well, basic knowledge of Python is important. An interest in mathematical approaches is certain
helpful, but the course is designed to understand and apply existing frameworks rather than creating your own models,” says Steinbeck.
Using commonly used libraries, the two HPI experts focus on practical problems and typical models such as linear regression and multivariate analysis. “In this way, every learner can integrate the approaches into the context of their everyday work and industry
contribute,” emphasizes Steinbeck. The leaders of the openHPI boot camp are certain that anyone who acquires knowledge in the areas of data science and machine learning today will be more successful in their field of work in the future.
The target group of the free course is professionals who want to integrate data science methods into their everyday work based on their industry-specific knowledge, but also school and college students. Depending on previous knowledge, five to seven hours per session should be given
Week should be planned for working through the teaching videos, self-tests, programming tasks as well as participating in live streams and discussions in the course forum.