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Data Science for Marketing

Objectives

Data science uses interdisciplinary techniques, such as statistics, data visualization, database systems, and machine learning to identify original, useful, and understandable patterns in data.
This course will familiarize students with Data science applications and analytical projects' lifecycle. Students will learn techniques for understanding and preparing data before building analytical models, such as data characterization/description, RFM or association rules (e.g., market basket analysis).

General characterization

Code

200201

Credits

7.5

Responsible teacher

Vasco Miguel Lourenço Guerreiro Jesus

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

Familiarity with the main theme of the course is not required. But it is highly recommended that the students have knowledge of Inferential Statistics as well as good skills as a computer user.

Students without previous training or experience with Python should complete the three following Datacamp online courses before the third week of this course (first practical class):  Introduction to Python, Intermediate Python, and Data manipulation with pandas. The instructor will provide information on how to have free access to the Datacamp platform.

Bibliography

Teaching method

The course is based on theoretical and practical classes. Several teaching strategies are applied, including slides presentation, step-by-step instructions on approaching practical examples, and questions and answers. The practical component is oriented towards exploring the tools introduced to students (Microsoft Excel and Python) and the development of the project.

Applications used: Microsoft Excel, Python, Jupyter notebook, Microsoft visual code.

Evaluation method

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.

  • Python Quiz:
    • Individual - with materials consultation
    • The minimum grade is 8.0
    • 10% weight
  • Group project:
    • The minimum grade is 8.0
    • 50% weight
  • Exam:
    • Individual - with materials consultation
    • The minimum grade is 8.0
    • 1st season or 2nd season: 40% weight

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

Subject matter

LU1. Introduction to Data Science
LU2. CRISP-DM process model
LU3. Common data types and introduction to SQL
LU4. Data characterization and description
LU5. Data understanding
LU6. Communication and Data visualization
LU7. Data preparation
LU8. Association rules and the Apriori algorithm
LU9. Data similarity and dissimilarity measures
LU10. RFM model
LU11. Introduction to Excel Power Pivot
LU12. Introduction to the Python programming language

Programs

Programs where the course is taught: