- Why learn Python for data analysis?
- How to install Python?
- How to install libraries?
- Basic programming with Python (data structures, iterations, etc.)
- How to work with jupyter notebooks?
Exercises:
- Create a virtualenv in your machine.
- Run python code in the console.
- Create first ipython notebook.
- Introduction to Numpy & Pandas.
- Numpy arrays and operations.
- Introduction to series and dataframes. Operations.
- Reading and writing data with pandas.
- Working with incomplete data.
- Merge/Join/Concat.
- SQL dbs and pandas.
Exercises:
- Open csv file with pandas.
- Remove incomplete values.
- Save data to SQL database.
- Introduction to Visualization.
- Libraries.
- Different types of plots (line, scatter, bar, histograms).
- Interactive plots libraries.
- Save figures.
Exercises:
- Load data with pandas. Choose two values and plot them.
- Count values with different targets and visualize it.
- Save a png figure.
- Introduction to Scikit-learn.
- Estandarization and Normalization of data.
- Dimension reduction methods.
- What is a predictive model? How can we measure the performance?
- Create, train and evaluate SVM.
Exercises:
- Load dataset and Scale values between [0, 1].
- Create baseline model and a SVM for classification with dataset provided.
- Show metrics of the model.
- Save model to pickle.
- Interesting resources, projects and other libraries.
- Deep learning techniques and libraries.
- Simple pipeline overview with Fashion MNIST (from data loading to creating the model and visualize it)