Data Science for Marketing

Objectives

At the end of the course students should be able to:
1. Apply preprocessing techniques to raw data
2. Understand the concepts of cross-validation, train/test split, leave-one-out
3. Understand model evaluation metrics
4. Apply regression and classification techniques to build predictive models
5. Understand the parameters tuning process
6. Apply the Machine Learning process to analyze marketing-related data

General characterization

Code

200201

Credits

7.5

Responsible teacher

Docente a designar

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

Bibliography

- Kelleher, J. D., Mac Namee, B., & D'Arcy, A. (2015 ). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press.
- 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.".
- Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. " O'Reilly Media, Inc.".
- Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.

Teaching method

Evaluation method

Subject matter

1. Machine Learning: definitions and introductory concepts
2. The machine learning task: preprocessing, model construction and validation
3. Evaluation metrics
4. Cross validation an train/test validation. Model complexity and overfitting
5. Classification task: logistic regression
6. KNN algorithm and K-means algorithm
7. Linear Regression
8. Support Vector Machines
9. Random Forests, Decision Trees and Ensemble techniques
10. The choice of the hyper parameters: model tuning.
11. Applications of the techniques in the field of Marketing

Programs

Programs where the course is taught: