Foundations of Quantitative Political Analysis (II)

Continuation of Foundations of Quantitative Political Analysis (I) Development of students' basic analytical skills in quantitative methods. Development of their skills related to the use of computer software for this purpose. Getting them acquainted with the fundamental challenges of using quantitative methods in the social sciences.

Workshop | Seminar

1. Databases | Simple Linear Regression -- Repetition

2. Ideas for PROJECT-1 | Multiple Linear Regression -- Applications

3. Working on PROJECT-1 | Non-linear regressions -- Logistic regressions

4. Working on PROJECT-1 | Casual analysis

5. PROJECT-1 presentations | Big Data

6. NLP-AI (I) | NLP

7. NLP-AI (II) | AI

8. Ideas for PROJECT-2 | Test

9. Working on PROJECT-2

10. Working on PROJECT-2

11. PROJECT-2 presentations

12. PROJECT-2 presentations

Workshop

Project-1

McKinney W. (2022). Python for data analysis : data wrangling with pandas numpy and jupyter (Third). O'Reilly Media. and additional online materials.

Project-2

Sowmya V. B., Majumder, B., Gupta, A., & Surana, H. (2020). Practical natural language processing : a comprehensive guide to building real-world NLP systems (First edition). O’Reilly Media.

PLUS online resources.

Seminar:

Simple Linear Regression

Martin P. (2022). Linear regression : an introduction to statistical models (1st ed.). SAGE Publications. Chapters: 1, 2, and 3.

Mutliple Linear Regression

Martin P. (2022). Linear regression : an introduction to statistical models (1st ed.). SAGE Publications. Chapters: 4 and 3.

Logistic Regression

Martin P. & Martin P. (2022). Regression models for categorical and count data ed. 1. SAGE Publications. Chapters: 1 and 2.

Casual Analysis

McBee M. (2022). Statistical approaches to causal analysis. SAGE Publications. Chapters: 1, 2, and 3.

Big Data

Castellani B. C. & Rajaram R. (2022). Big data mining and complexity. SAGE Publications. Chapters: 2, 3, 4, and 5.

NLP and AI

Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data : a new framework for machine learning and the social sciences. Princeton University Press. Chapters: 2 and 17.

AI usage -- Level 5: Full AI

AI should be used as a ‘co-pilot’ in order to meet the requirements of the assessment, allowing for a collaborative approach with AI and enhancing creativity.

You may use Al throughout your assessment to support your own work and do not have to specify which content is Al generated.

See above.

Students can perform simple quantitative analysis, starting from obtaining a dataset through its cleaning and ending with simple statistical tests and the use of simple regression models. (K_U05-K_U08, K_K01-K_K03)

Students are familiar with the basic dilemmas related to the use of quantitative methods in political science research. (K_W01, K_K01-K_K03)

The course concludes with a written exam, in which students must perform various analytical tasks using the relevant software. In order to take the exam, students must pass the tests at the end of the seminar and complete all the tasks, including the project, carried out during the workshop.

Students may have one unexcused absence from the seminar and two from the workshop. The teacher must account for all additional absences. The maximum number of absences permitted for the entire course is four.