Anyone interested in machine learning and the basics of statistics can now learn how to analyze large data sets and visualize the results in a free online course at the Hasso Plattner Institute (HPI). The "Data Science Bootcamp", as it is called, starts on 7 June on the openHPI learning platform. The participants will work with real data and programming tasks that require basic knowledge of the Python programming language.
Two HPI doctoral students are leading the English-language bootcamp: Mohamed Elhayany and Hendrik Steinbeck. They introduce the learners to Jupyter Notebooks, an interactive web platform that enables scientific computations and visualizations through a web browser. "This means that no one in our course has to install a special programming environment on their local computer," Elhayany emphasizes. That should make it easier to get started in programming, visualize, analyze, and extract data from various sources.
The two experts want to take interested people on a voyage of discovery into the world of scientific data analysis. "In order to meet the requirements well, a fundamental knowledge of Python is important. An interest in mathematical approaches certainly helps, but the course is designed to understand and apply existing frameworks, rather than creating one’s own models”, Steinbeck says. Through often-used libraries, the two HPI experts focus on hands-on problems and typical models like linear regression and multivariate analysis. “Therefore, each learner can contextualize the approaches to their daily work and industry," Steinbeck points out. Those who acquire knowledge in the areas of data science and machine learning today will be more successful in their field of work in the future, the leaders of the openHPI bootcamp are certain.
The target group for the free course is professionals who aim to integrate data science methods with their industry-specific knowledge in their day-to-day work, but also pupils and students. Depending on previous knowledge, five to seven hours per week should be planned for working through the instructional videos, self-tests, programming tasks, and participation in live streams and discussions in the course forum.
