Checking date: 20/05/2022

Course: 2022/2023

Advanced Programming
Study: Master in Statistics for Data Science (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
COMPETENCES THAT THE STUDENT ACQUIRES WITH THIS MATTER CB9 That students know how to communicate their conclusions and the knowledge and ultimate reasons that sustain them to specialized and non-specialized audiences in a clear and unambiguous way CB10 That students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous. CG4 Ability to synthesize the conclusions obtained from these analyzes and present them clearly and convincingly in a bilingual environment (Spanish and English) in writing. CG6 Apply social skills for teamwork and to relate to others autonomously. CE2 Use free software such as Python for the implementation of statistical analysis. CE8 Apply and develop visualization techniques of collected samples with free distribution software such as Python. LEARNING RESULTS THAT THE STUDENT ACQUIRES - Integration of C ++ and R via Rcpp - Python programming language. Machine learning packages. - Brief introduction to the STAN programming language
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).
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.