Data Science for Economics and Business

Recommended Prerequisites

Not applicable.

Teaching Methods

Theoretical-practical lessons with computers. The main concepts, instructions and routines will be presented to the students using practical examples. The students will then have an opportunity to apply these to several practical examples in business and economics

Learning Outcomes

The student is expected to be able to:
1) Suggest and apply prior treatments appropriate to the available data;
2) Apply the appropriate procedures for statistical analysis of the data;
3) Apply machine learning procedures suited to the data and to the goals of the analysis;
4) Use software to communicate the results.
Competencies:
1) Specific:
- Collect, analyze and properly process the data.
- Use computer programs to analyze and model the data.
- Use computer programs to communicate the results.
2) Generic:
- Analyze data.
- Solve problems.
- Use computer programs.
- Communicate.
- Plan work activities.
- Make decisions.
- Work in a team.

Work Placement(s)

No

Syllabus

1. Essential procedures
2. Statistical tools
3. Machine learning techniques
4. Communicating the results

Head Lecturer(s)

Pedro Miguel Avelino Bação

Assessment Methods

Assessment
Project: 40.0%
Mini Tests: 60.0%

Bibliography

Baumer, B.,Kaplan, D., Horton, N. (2017). Modern Data Science with R. Chapman &; Hall/CRC Press.
Bruce, P., Bruce, A., Gedeck, P. (2020). Practical Statistics for Data Scientists. O'Reilly.
Grus, J. (2019), Data Science from Scratch: First Principles with Python. O'Reilly.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning. Springer.
Wickham, H., Grolemund, G. (2017). R for Data Science. O'Reilly.
Zumel, N., Mount, J. (2020). Practical Data Science with R. Manning.