quantecon_nyu_2016

Covers essential topics from programming foundations to advanced economic applications like Markov dynamics and dynamic programming, as detailed in the syllabus sections.

Requires a GitHub-hosted project with code, Jupyter notebooks, and analytical write-ups, ensuring practical skills and reproducibility, as outlined in the Assessment section.

Emphasizes open-source tools like Git and Jupyter, promoting collaborative and transparent research practices, as stated in the philosophy and course materials.

Introduces both Python and Julia ecosystems, including libraries like NumPy and Pandas, preparing learners for diverse scientific computing environments.