Data science is of growing importance in every STEM field. While data science tools are more readily available now than ever before, properly using these tools requires a mathematical understanding of the algorithms within. This class develops a principled approach to using the terminology, models, and algorithms found in signal processing and machine learning, the mathematical backbone of data science.
What you'll learn
- Theoretical understanding of data models and systems for processing signals, images, and other data
- Practical implementation of signal processing and machine learning algorithms on data from the real world
- Ability to navigate the data science process as an expert instead of relying on trial and error with black box methods