Deep knowledge in data science:
1. Machine learning: logistic regression, random forest, support vector machine (SVM), ensemble learning;
2. Statistical learning: Bayesian Hierarchical modeling, multilevel generalized linear mixture model (GLMM), spatial and patio-temporal modeling;
3. Probabilistic graphic models and generative models: Bayesian network and Markov fields, restricted Boltzmann machine (RBM) and autoencoder.
4. Deep learning practioner.
Summary applications in healthcare and transportation domains:
1. Social and behavior risk factors analysis for population health;
2. Spatial analysis for demand/supply model for healthcare providers;
3. Individual-level patient health ris...