Northwestern University 1 credit Engineering Science & Applied Math
Data in digital form is now widely available. From text and images posted to social networks to multi-modal medical records, from air quality sensor data to images gathered by telescopes in space, from genome-wide sequencing experiments to protein structures, one can now download tens, thousands, and even millions of such records. In this class, students will learn about standard pipelines to analyze such data. Starting with how to download them, to how to check the data for errors and bias, how to select appropriate features for further consideration, how to generate hypothesis for testing and how to present and visualize their results. The class is organized around small individual projects and a large group project. Prerequisites: Coding in Python, understanding of probabilities.
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