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1. Introduction:

LN 1.1. Introduction (PDF).


2. R and C++ integration:

LN 2.1. Brief description of contents (PDF).

Description. Integration of R and C++.

LN 2.2. RCPP (PDF).

Description. Integration of R and C++.

LN 2.3. RCPP solutions to exercises (PDF).

Description. Solutions to exercises in the slides.


3. Python for data analysis:

LN 3.1. Brief description of contents  (PDF).

LN 3.2. Python base (PDF).

Description. Python base.

LN 3.3. Python libraries: numpy and pandas (PDF).

Description. Libraries for managing numeric arrays (numpy) and dataframes (pandas).

LN 3.4. Python machine learning: scikit-learn (PDF).

Description. Scikit-learn machine learning library.

LN 3.5. Python visualization (PDF).

Description. Matplotlib and Seaborn for data visualization.


4. Stan:

LN 4.1. Brief description of contents (PDF).

LN 4.2. Stan (PDF).

Description. Brief introduction to Stan.

Última modificación: viernes, 25 de febrero de 2022, 14:06