Big Data for Marketing


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.

General characterization





Responsible teacher

Flávio Luís Portas Pinheiro


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


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


Teaching method

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.

Evaluation method

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.

Subject matter

  • 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