Programming with R

The main course objectives are: 1. Integrate C++ with R using Rcpp: Learn to combine the power of C++ programming with the R programming language, enabling efficient and high-performance computation. 2. Master Python, numpy, and pandas: Gain proficiency in Python programming language along with essential data manipulation and analysis libraries like numpy and pandas. 3. Develop Data Visualization Skills: Acquire the ability to create visual representations of data using Python libraries such as matplotlib and seaborn. 4. Explore Machine Learning with scikit-learn: Understand the fundamentals of machine learning and apply it practically using the scikit-learn package in Python.

1) Combination of C ++ with R through Rcpp. 2) Python Language, numpy and pandas libraries. Graphics in Python (matplotlib and seaborn). 3) Machine learning packages (scikit-learn). 4) Introduction to Stan

Theory: Lectures will be focused on teaching concepts and language elements. Practical computer Sessions (sessions with student's own laptops): The practical classes will be developed so that, in a supervised way, students learn to solve practical cases. The practices will be carried out in groups of 2 students. There are several assignments related to topics in the course. There will be tutorials to help the understanding both of theory and practice. === TRAINING ACTIVITIES OF THE STUDY PLAN REFERRED TO MATTERS AF1 Theoretical class AF2 Practical classes AF4 Laboratory practices AF5 Tutorials AF6 Group work AF7 Individual student work AF8 Face-to-face evaluation tests TEACHING TRAINING METHODOLOGIES OF THE PLAN REFERRED TO MATTERS MD1 Lectures with material and bibliography provided. MD3 Resolution of practical cases, problems, etc. MD5 Preparation reports individually or in groups