Processing complex data

Course goals

At the end of the course, students will be able to:

• Identify and describe a range of complex scientific data formats (e.g., multidimensional arrays, spatiotemporal data, and metadata-rich structures) and their associated challenges.

• Apply appropriate preprocessing techniques (e.g., cleaning, transformation, normalization, filtering, feature extraction) to different types of real-world scientific datasets.

• Analyze and compare statistical modelling approaches suitable for various data modalities, evaluating their assumptions, strengths, and limitations.

• Defend and communicate statistical findings through a structured report and peer discussions, demonstrating the ability to justify methodological choices and respond to critique.

Content

Contrary to what most introductory data science courses and statistics courses teach and use, data in science has an incredible variety of formats, sizes, and procedures. From simple tables to complex multidimensional space-time arrays, including metadata and custom storage formats; the world of data for science is vast, varied, and wildly interesting. This course is designed to give students an introduction to core real-world data concepts, as well as hands-on experience with handling, processing, and modelling different types of complex data used in various fields of science and beyond. The course leans on student engagement and guided practical group work to create a dynamic learning environment.