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Advanced Programming, 2020

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Autor: Ricardo Aler Mur
In this course three advanced topics are taught in order to complement the knowlege acquired in the master, so that data science and data analysis skills of students are improved: combining C++ and R in order to improve R efficiency; basic knowledge about Python and its data, machine learning, and visualization libraries; and basic knowledge about the statistical Stan language.
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RICARDO ALER MUR

Departamento de Informática
Universidad Carlos III de Madrid

Area:
Computer Science

Degree:
Master in Statistics for Data Science

November, 2020

Image courtesy of Pixabay via [ Pixabay ]

Theorethical and Lab hours: 21+21
Total hours: 42

 

PRERREQUISITES AND RECOMMENDED PREVIOUS KNOWLEDGE

Programming in R; Basic knowledge about machine learning.

 

GENERAL DESCRIPTION OF THE SUBJECT

In this course three advanced topics are taught in order to complement the knowlege acquired in the master, so that data science and data analysis skills of students are improved: combining C++ and R in order to improve R efficiency; basic knowledge about Python and its data, machine learning, and visualization libraries; and basic knowledge about the statistical Stan language.

OBJETIVES: KNOWLEDGE AND SKILLS

  • Knowledge:
    • Concepts about C++ necessary for integrating this language with R
    • Basic concepts of base (core) Python
    • Main concepts about machine learning stages and its use with R
    • Basic concepts about data visualization
    • Basic concepts about probabilistic programming
  • Skills:
    • Use of RCpp for integrating C++ and R
    • Advanced use of Python, its main data libraries (numpy and pandas), its main machine learning library (scikit-learn), and its main visualization libraries (matplotlib and seaborn).
    • Basic use of RStan

 

TEACHING MATERIAL

  • Slides for the lectures, that include some tutorials and exercises

ASSESSMENT ACTIVITIES OR PRACTICAL ASSIGNMENTS

  • Lab materials for practicing R and C++ integration, Python, Machine Learning, and data visualization.
  • Exams (with answers) for self-evaluation.

 

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