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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 and good computer user skills.

Students without previous training or experience with Python should complete the three following Datacamp online courses before the fourth week of this course:  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

  1. VanderPlas, Jake. Python data science handbook: essential tools for working with data. "O'Reilly Media, Inc.," 2016.
  2. McKinney, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. "O'Reilly Media, Inc.," 2012.
  3. Grus, Joel. Data science from scratch: first principles with Python. "O'Reilly Media, Inc.," 2015
  4. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".
  5. Additional reading materials will be shared in Moodle with all the students, including documentation materials and book chapters; 

Teaching method

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

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.

  • Group project:
    • The minimum grade is 8.0
    • Each student in the group will assess (anonymously) the other group members' contributions by answering the question “Contribution to the group (in sharing workload and quality of work)” – from 0 to 4
    • 60% 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 hypothesis testing

LU12. Introduction to the Python programming language