- Primary instructor: John Stachurski
- Assistant instructor: Natasha Watkins
- Dates: 4th--7th June
- Class times: 9:30 am--12 noon
This mini course will provide a fast paced introduction to Python for computational economic modeling, from basic scripting to high performance computing. The course is aimed at graduate students with proficiency in at least one scientific computing platform (e.g, MATLAB, Fortran, STATA, R, C or Julia).
No Python knowledge is assumed.
- Python vs MATLAB vs Julia vs Fortran vs others
- The Python language: syntax and semantics
- Jupyter notebooks
- Object oriented vs procedural programming
- The major scientific libraries ( SciPy / NumPy / Matplotlib / etc.)
- Numba and other JIT compilers
- Parallelization
- Distributed and cloud computing
- Application I: Inventory dynamics
- Application II: Inequality and distributions
- Application III: Job search
- Application IV: Optimal savings
- The pandas data analysis library
- Working with data
- Some empirical applications
- Login to Amazon AWS Console
- Navigate to EC2 Service
- Choose your region for setting up an instance
- Create security key-pair for the region if you don't have one
- Launch & Configure an instance and choose Ubuntu 64-bit
- enable access through Port 8000 (in addition to Port 22 for ssh)
- Choose security key you've set up
Use ssh -i /path/to/pem-key ubuntu@hostname
Here hostname is your Public DNS, as shown in the instance information from AWS console
Now run sudo apt-get update so you can install things you might need using apt-get
- ssh into the running instance using IP from AWS Console
- Install Anaconda using wget and the latest download link for python36
- Run: jupyter notebook --generate-config
- For Automatic Password Setup run: jupyter notebook password
- Edit .jupyter/jupyter_notebook_config.py and set the following
# Set ip to '*' to bind on all interfaces (ips) for the public server
c.NotebookApp.ip = '*'
c.NotebookApp.open_browser = False
c.NotebookApp.port = 8000