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Maths for Social Scientists

Course Dates and Times

Monday 29 July – Friday 2 August 08:00 – 08:45

This is a free supplementary course. You must register and pay for a one-week or two-week course to qualify for attendance.  To book, please check the box when registering.

Julia Koltai

Eötvös Loránd University

This course is designed to refresh critical mathematical background in quantitative social science research. It provides an overview of the essential concepts and language required for competent analysis using quantitative approaches in social science.

Instructor Bio

Julia Koltai is an assistant professor at the Faculty of Social Sciences, Eötvös Loránd University. She is also a research fellow at the Centre for Social Sciences, Hungarian Academy of Sciences. She gained her PhD in sociology in 2013.

Julia has led several domestic research programs and has taken part in international research projects and groups, including EU FP6-funded programs.

Her main scientific focus is on statistics and social research methodology, so her research has ranged widely, from minority research through political participation to social justice and integration.

In recent years, Julia's interest has turned to computational social science, especially network analysis and big data processing.


The topics focus on dimensions of calculus, linear algebra and probability theory most commonly applied in social science research. Rather than taking a comprehensive mathematical approach, the course will focus on the most critical concepts and approaches behind widely used tools like multivariate regression, logistic regression, principle component analysis, structural equation modelling, and other statistical methods.

This is a refresher course. We won't have time for deeper elaboration of the topics or detailed computation of examples. We will focus on the concept, context and basic rules of the mathematical topics. For each topic, I will talk briefly about the application of the given mathematical concept, and suggest literature for deeper understanding.

Basic algebra (secondary school level)

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.

Day Topic Details
1 Basics

Relations and functions


Limits and continuity

2 Basic concept of calculus: differential and integral

Differential calculus

Partial derivatives

The theory of integration

3 Matrices

Matrix rules

Representing a system of equations in matrix form


Inverses of matrices

4 Probability theory 1

Permutations, combinations

Conditional probability and Bayes' Law


5 Probability theory2

Probability distributions: normal, binomial, poisson

Random variables

Day Readings

Timothy M. Hagle (1995)
Basic Math for Social Scientists
Concepts. Thousand Oaks, Calif: SAGE Publications. Chapters 1–2


Hagle. Chapters 3–5


Hagle. Chapter 6


Hagle. Chapter 1

Tamás Rudas (2004)
Probability Theory: A Primer
Thousand Oaks, Calif: SAGE Publications. Chapter 3


Rudas. Chapter 4

Software Requirements


Hardware Requirements