Class Data Science

This module offers an in-depth view of the field of Data Science, teaching fundamental principles and methods of analysis and learning from data. The course is aimed at data analysts without prior knowledge in statistics, and emphasizes the importance of ethics in building and using models obtained from data. Teaching methods include traditional lectures, classroom debates, and self-assessment activities, helping students develop critical and analytical skills essential for success in data science. At the end of the course, students will have a comprehensive understanding of the field, as well as the skills and knowledge necessary to become successful data scientists.

1. Introduction to data science
2. What does it mean to learn from data?
3. Is my model accurate?
4. Simple linear regression
5. Multiple linear regression
6. Working with regression models
7. Logistic classification
8. Generative classification models
9. Resampling methods
10. Tree-based methods for regression and classification
11. Ethical issues and regulations in Data Science

The teaching methods for this data science module aim to create an engaging and effective learning experience. Traditional lectures provide foundational knowledge, while in-class debates encourage active participation and critical thinking. Retrieval self-assessments improve long-term retention of information, allowing students to monitor their learning progress and identify areas for improvement. The combination of lectures, debates, and self-assessment activities creates a well-rounded learning experience that meets the needs of all students and helps them develop the skills and knowledge to succeed as data scientists.

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.