Checking date: 02/10/2023

Course: 2023/2024

Advanced Programming
Master in Statistics for Data Science (Plan: 386 - Estudio: 345)

Coordinating teacher: ALER MUR, RICARDO

Department assigned to the subject: Computer Science and Engineering Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
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.
Skills and learning outcomes
Description of contents: programme
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
Learning activities and methodology
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
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70

Calendar of Continuous assessment

Basic Bibliography
  • Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edición. O'Reilly Media. 2019
  • Dirk Eddelbuettel. Seamless R and C++ Integration with Rcpp (Use R!) . Springer. 2013
  • Eric Matthes. Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming. No Starch Press. 2019
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Julian Avila. scikit-learn Cookbook (2nd edition). Packt. 2017
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.