The world has become data-driven. Domain scientists and industry increasingly rely on data analysis to drive innovation and
discovery; this reliance on data is not only restricted to science or business, but also is crucial to those in government, public policy, and those wanting to be informed citizens. As the size of data continues to grow, everyone will need to use powerful tools to work with that data.
This course teaches intermediate data programming. It is a follow on to CSE142 (Computer programming I) or CSE160 (Data Programming).
The course complements CSE143, which focuses more deeply on fundamental programming concepts and the internals of data structures. In contrast, CSE163 emphasizes the efficient
use of those concepts for data programming.
In this course, students will learn:
- More advanced programming concepts than in CSE142 or CSE160 including how to write bigger programs with multiple classes and modules.
- How to work with different types of data: tabular, text, images, geo-spatial.
- Ecosystem of data science tools including Jupyter Notebook and various data science libraries including scikit image, scikit learn, and Pandas data frames.
- Basic concepts related to code complexity, efficiency of different types of data structures, and memory management.