1. Início
  2. NOVA Information Management School
  3. Mestrado em Data-Driven Marketing
  4. Especialização em Digital Marketing and Analytics
  5. Big Data para Marketing

Big Data para Marketing

Objetivos

This curricular unit builds on marketing concepts and advanced analytical techniques to take full advantage of the vast amount of data available these days to marketing professionals.

Caracterização geral

Código

200202

Créditos

7.5

Professor responsável

Flávio Luís Portas Pinheiro

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

It is recommended that students have basic knowledge of statistics and Python.

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 slides 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, HDInsight Azure, BigQuery, Databricks.
 

Método de avaliação

Due to the application-based design of the course, evaluation is continuous.

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

  • Completion of self-assessment survey: 2.5%
  • Group membership submission (in the due deadline): 2.5%
  • Group project (minimum grade is 8.0):
    • Materials (datasets, code, etc.) and report: 50%
    • Due date: to be confirmed (around 10 days before 2nd season exam date
  • Exam (individual - with materials consultation - minimum grade is 8.0): 45%

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

Conteúdo

  • LU1. Introduction to Big Data
  • LU2. Data sources
  • LU3. Databases and SQL
  • LU4. Hadoop
  • LU5. BigQuery
  • LU6. Spark
  • LU7. Spark: Introduction to text mining
  • LU8. Spark: Frequent pattern mining
  • LU9. Spark: Machine learning
  • LU10. Big data marketing project