A place where we can

• share tips and codes

• explore new areas of the health data science landscape

• take challenges on online competitions such as Kaggle and Dream challenges

What to expect from the meetup?

1. Time: 9/26   9:00-11:00 AM; Topic: Data Science Pipeline, Python crash course, common packages
    
2. Time: 10/10 9:00-11:00 AM; Topic: Shrinkage and Regularization: Lasso, Ridge, Elastic Net
    
3. Time: 10/24 9:00-11:00 AM: Topic: Tree-based Methods: CART, Random Forests, GBDT & Ensembles: Voting, Bagging, Boosting
    
4. Time: 11/7   9:00-11:00 AM; Topic: Support Vector Machines
    
5. Time: 11/21 9:00-11:00 AM; Topic: Neural Network: MLP, CNN
    
6. Time: 12/5   9:00-11:00 AM: Topic: Neural Network: RNN

Raw Data

Data Wrangling

Explore

Model Selection

Feature Engineering

Train Model

Evaluate Performance

Data Product

Data Science Pipeline

Inferential Model

vs

Predictive Model

Decomposition of Error

Bias-Variance Trade-off

Cross-Validation

• The test error can be calculated if a test dataset is available.
• Unfortunately, this is usually not the case.
  We don’t have a very large designated test dataset that can be used to directly estimate the test error rate in most time.
   
• Cross-validation:
  A method that can estimate the test error rate by holding out a subset of the training observations from the fitting process, and then applying the trained method to those held out observations

Python

Python vs R

Python 2 vs 3

• Install Python 2
• Install numpy
  pip install numpy
• Install pandas
  pip install pandas
• Install matplotlib
  pip install matplotlib
• Install scikit-learn
  pip install scikit-learn
• Install jupyter notebook
  pip install jupyter