intro to python
DAY 3

KEY Take-aways from DAY 2

• NumPy:
  • more powerful arrays
  • how to manipulate them
• Matplotlib: 
  • How to plot graphs
  • You'll likely use it often to present results
• SciPy:
  • Vast scientific library 
  • Don't need it to know it all
  • Will be useful to you if you do tasks such as statistics, image or audio processing, etc.

DAY 3

• Pandas:
  • DataFrames to handle structured data
  • Cleaning data
  • Filtering, selecting, joining, grouping data
  • Computing statistics on it
  • Analysing time series
• Scikit-learn: 
  • Toolbox for:
    • Machine Learning
    • Data Mining
    • Prediction
• Apply what you've learned to a Kaggle Dataset: The Titanic!

PANDAS

time to work on a real case

What can you do to this DATA?

scikit-learn

and quick intro to Machine Learning

CLASSIFICATION STEPS

• Get your data
• (Extract features from your data)
• Choose a model type: e.g. Decision Tree
• Train your model: model.fit(data,target)
• Predict: model.predict(new_data)