Updating the experts and expertise tab

[rickecon]

@MattHJensen, @jdebacker, and @rickecon had an OpenRG board meeting last week and discussed adding areas of expertise to each expert's information. We currently have an "Experts" page option across the top menu of the website

1. I played around with adding areas of expertise to the short bio summary cards on the openrg.com/experts.html page, but that did not look very good. I think we should add areas of expertise to the top of each expert's individual bio page (e.g., Richard Evans).
2. I would like to create a new top menu option called "Services", which I would place between the "About Us" drop down and the "Experts" page. I would make the "Services" option a dropdown with the following options. We should have a page for each of those five services.
   • Modeling
   • Data
   • Data Analytics
   • Dynamic visualization
   • Training

I would recommend that the information on each of the pages be the following.

Modeling

• Household microsimulation
• Business microsimulation
• Overlapping generations (OG) macroeconomic modeling
• Computable general equilibrium (CGE) modeling
• Dynamic stochastic general equilibrium (DSGE) modeling
• Population dynamics and demographics
• Firm dynamics
• Asset pricing

Data

• Versioning
• Validation
• Data wharehousing
• Firewall access
• Data synthesis
• Matching and imputation

Data Analytics

• Data description
• ???

Dynamic visualization

• Bokeh
• Plotly
• D3 JavaScript

Training
OpenRG's training goals are to:

• Increase our clients’ capacity to model and simulate policy
• Improve the collaborative workflow efficiency of the model maintainers
• Make the models as transparent, replicable, verifiable, and accessible as possible

OpenRG can provide substantive, hands-on, instructor-delivered, classroom-tested, open-access training in all the following areas. We have developed all of these training modules through university courses to undergraduate and graduate students, as well as faculty, at the University of Chicago, New York University, University of South Carolina, Brigham Young University, University of Lausanne, and University of Zurich. We have also provided these courses to organizations like the World Bank, European Commission, and International Monetary Fund.

Let us increase the capacity of your modeling team by selecting a combination of modules that is tailored to the needs of your organization. Trainings can range from one day to two months.

• Modeling
  • Household microsimulation models
  • Business microsimulation models
  • Overlapping generations (OG) macroeconomic models
  • Computable general equilibrium (CGE) models
  • Dynamic stochastic general equilibrium (DSGE) models
  • Population dynamics and demographics
  • Firm dynamics
  • Asset pricing
• Programming
  • Introduction to Python
  • Object oriented programming
  • Advanced Python
  • Data with Pandas
  • Optimization and root finding with Scipy.Optimize
  • Web scraping with Beautiful Soup and Scrapy
  • Parallel programming and high-performance computing (HPC) with Dask
• Math, statistics, and optimization
  • Numerical derivatives
  • Numerical integration
  • Newton's method
  • Measure theory
  • Inner product spaces
  • Spectral Theory
  • QR and singular value decomposition (SVD)
  • Dynamic programming
  • Linear optimization
  • Unconstrained optimization
  • Constrained optimization
  • Maximum likelihood estimation (MLE)
  • Generalized method of moments (GMM) estimation
  • Simulated method of moments (SMM) estimation
• Machine learning
  • Classification and logistic regression
  • Resampling methods, cross validation, and bootstrapping
  • Decision trees and random forests
  • Support vector machines
  • Hyperparameter tuning and prediction optimization
  • Neural networks
• Open-source workflow
  • Git and GitHub workflow
  • Continuous integration testing
  • Environments and package management
  • Jupyter Book documentation

[jdebacker]

@rickecon I like this idea. The Data Analytics sections is looking sparse, so I'd add a few items:

• Visualization
• Causal inference
• Forecasting
• Machine learning
• Structural estimation

Also, for Dynamic viz, I'd substitute "Plotly/Dash" for "Plotly"