1. Início
  2. NOVA Information Management School
  3. Pós-Graduação em Business Intelligence Analytics for Hospitality and Tourism
  4. Ciência de Dados Para a Hotelaria e Turismo II

Ciência de Dados Para a Hotelaria e Turismo II

Objetivos

Predictive analytics takes advantage of statistical, Data Mining, and Machine Learning algorithms and techniques to estimate future outcomes based on historical data. This curricular unit will familiarize students with the most common predictive analytics algorithms and techniques and their applications in hospitality and tourism.

Caracterização geral

Código

400115

Créditos

7.5

Professor responsável

Nuno Miguel da Conceição António

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

Completion of the course Data Science for Hospitality and Tourism I.

Bibliografia

Método de ensino

The curricular unit is based on theoretical-practical classes. The sessions include the presentation of concepts and methodologies and the practical application of different concepts using different languages and computer applications. Several teaching strategies are applied, including slide presentation and step-by-step instructions on approaching practical examples, questions, and answers. The practical component is oriented towards exploring tools introduced to students, including the discussion of the best approach in different scenarios.

Applications used: Python, Jupyter notebook, Microsoft visual code

Método de avaliação

Due to the application-based design of the course, evaluation is continuous and applies to both the theory and practical components. There is no "one only exam" with a single weight of 100%.

All evaluation grades are on a scale of 0-20. The final course grade is calculated based on the following weights:

  • Two group projects:
    • Members: 2 to 3
    • Delivery for each project:
      • Python notebook (commented)
      • Presentation
    • Weight on final grade: 35% per project
  • Three quizzes:
    • Individual
    • With consultation of materials
    • Weight on final grade: 10% per quiz

All submissions should be made via Moodle. Submissions after the deadline will be rejected.

Conteúdo

LU1. Introduction to Machine Learning, Data Mining, and Predictive Analytics.
LU2. Machine Learning applications in hospitality and tourism.
LU3. Machine Learning libraries for Python.
LU4. CRoss-Industry Standard Process for Data Mining (CRISP-DM) Methodology.
LU5. Data understanding.
LU6. Data preparation.
LU7. Modeling: Model validation, generalization, and overfitting.
LU8. Modeling: Supervised learning - regression: performance measures.
LU9. Modeling: Main families of algorithms: linear regression, decision trees, neural networks, Support Vector Machines (SVM), K-Nearest Neighbors (KNN).
LU10. Modeling: Supervised learning - classification: performance measures.
LU11. Modeling: Main families of algorithms: logistic regression, decision trees, neural networks, Naive Bayes, SVM, and KNN.
LU12. Modeling: Ensembles of methods.
LU13. Modeling: Models' interpretability.

Cursos

Cursos onde a unidade curricular é leccionada: