Insegnamento a.a. 2025-2026

Classes: 45 (II sem.)

Instructors:
Class 45: OMIROS PAPASPILIOPOULOS

Preliminaries to the course are basic probability, at the level of Chapter 6 of https://press.princeton.edu/books/hardcover/9780691222271/quantitative-social-science and very basic calculus and linear algebra. Although this course is in R, and no previous knowledge of this is required, the course assumes that the students are familiar with the fundamentals of object-oriented programming (e.g., Python) as developed in the corresponding course in the first semester.

The course develops hands on quantitative social science skills together with the maturity to formulate interesting questions and the confidence to use data and algorithms to produce useful insights and policy recommendations. During the course the students will learn to use the R language and they will work with tidyverse in R, which is a standard framework for data analysis among computational social scientists. Note that most of the fundamental code used in the course will be provided to the students. This course is in conversation with the concurrent course in Machine Learning and each course will benefit from concepts developed earlier in the other.

The course is organized along the following themes:

1. Introduction

2. Measurement
3. Introduction to R

4. Causal inference 

5. Discovery

6. Uncertainty

7. Structural modelling

At the end of the course student will be able to...

+ interprete and answer causal inference questions

+ develop R code for data analysis

+ formulate and evaluate structural models

+ carry out and interpret unsupervised learning

At the end of the course student will be able to...

+ carry out data analysis

+ create insightful graphs and visualizations

+ pose and answer causal inference questions

+ use state of the art analysis methods for quantitative social science

• Lectures
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments

+ practical exercises are in terms of case studies of analyzing real data and social science questions

+ collaborative work is in terms of group projects that will include the collection and analysis of data for a real social science question 

	Continuous assessment	Partial exams	General exam
Written individual exam (traditional/online)			x
Individual Works/ Assignment (report, exercise, presentation, project work etc.)	x
Collaborative Works / Assignment (report, exercise, presentation, project work etc.)	x

The course does not distinguish between attending and non-attending students. 

 

The course is evaluated in three different ways that involve both exams and continuous evaluation. 

 

One is an individual final exam. It involves 31 multiple choice questions, each carries 1 point. The exam is open book, you can use your laptop and any program that is locally installed (including LLMs) but cannot use the WiFi as it is an individual exam. Details about format, content and rules will be circulated through blackboard. The exam questions are organized in levels from 1 to 3, from easiest to hardest. There are about 15 level 1 questions, 11 level 2 questions and 5 level 3. Level 1 does not involve any coding, level 2 usually does, level 3 almost always does. The levels also reflect the time that it takes to answer the question. 

The other two involve continuous evaluation and a bonus-malus system.

 

Bonus: 
After each theme a set of multiple choice questions will be posted as a worksheet. It is the type of questions (occassionally, the exact same questions) that are included in the exam. After a week that they have been posted, during the following lectures (main lectures, LI sessions or TA sessions) the professor might ask the class about the previous questions,  what is the right answer and how the student arrived to the answer. People who volunteer and answer successfully they will be awarded with 0.25 points, for the easier questions, and 0.4 for the harder ones. Each student can accumulate a maximum of 2 bonus points like that, which will be added to their exam mark.

 

Malus:
At the beginning of the semester the students will be organized in groups of 3-5 people (no group will be accepted with less than 3 or more than 5 members). Many classes (both main lectures and LI sessions) are hands-on and involve coding. The jupyter-notebooks that develop the material to be covered in the class will be distributed to the class a week in advance. The students are expected to have gone through the notebooks before the class. Each notebook will contain a small number of easy exercises. The students,working as a group, should have prepared the answer to these questions before the class. During the class, using random numbers, the professor will ask a group to share their solution. If the group has not done anything, they get -0.25 points. If they have made some progress but not arrived to the solution, they get -0.1 points. If they have a reasonable answer, they get 0. The maximum of malus points received in this way is -2, which will be subtracted from the bonus points. Any student that has not joined a group will automatically get -2 malus points.

 

Example 1: a student with exam mark 28, that received 1.5 bonus points and their group has -0.5 malus points, will get 28+1.5-0.5 = 29

Example 2: a student with exam mark 16, that received 0 bonus points and did not join any group, will get 16

The course is built around the following textbook:

https://press.princeton.edu/books/hardcover/9780691222271/quantitative-social-science

but also draws from a few other places and from the research of Professor Papaspiliopoulos.

In the later parts in the course (but also in the ML course) we will use some examples from:

https://www.statlearning.com/

which can also be used in conjunction with the lecture notes for further reading. 

Computing will be carried out using jupyter notebooks (the same framework used also in the concurrent Machine Learning book).