IDEM 187

Topics in Digital and Computational Demography

Hybrid: in-person for students in the PHDS network and/or already in Rostock; Online (via Zoom) for everyone else, November 02–06, 2026

Course Coordinator:  Carolina Coimbra Vieira

Instructors:
Carolina Coimbra Vieira | Ebru Sanlitürk | Emilio Zagheni | Tom Theile | Benjamin-Samuel Schlüter | Irena Chen | Liliana P. Calderón-Bernal | Mallika Snyder | Boris Barron | Jordan Klein

Start date: 2 November 2026
End date: 6 November 2026
Location: Hybrid: in-person for students in the PHDS network and/or already in Rostock; Online (via Zoom) for everyone else.

Course Description

Rapid advances in computational power, artificial intelligence, and the widespread use of the internet, social media, smartphones, and digital platforms have fundamentally transformed our lives, the way we interact, and our behavior, including our demographic choices and constraints. The digitalization of everyday life has generated an unprecedented volume of digital trace data, fueling a new "data revolution" that is reshaping the population sciences and the broader social sciences.

These developments enable researchers to address longstanding demographic questions in new ways while also raising important methodological and ethical challenges. Modern data science and computational tools provide unprecedented opportunities to study population processes at greater temporal and spatial scales. At the same time, demographic theory and social science methods remain essential for interpreting complex digital trace data, assessing their quality and representativeness, and accounting for potential biases.

This course provides an introduction to the emerging field of Digital and Computational Demography, integrating demographic theory with modern data science and computational approaches. Participants will learn how to leverage diverse data sources, including digital trace data, to investigate substantive questions about population processes while critically evaluating issues of data quality, representativeness, ethics, and methodological limitations. The course covers key methodological foundations, including digital trace data collection and analysis, approaches for integrating representative and non-probabilistic data sources, Bayesian methods for data-deficient settings, demographic simulation and microsimulation, and applications to kinship demography. Hands-on practical sessions will provide experience with modern computational tools, AI coding agents, web APIs, and specialized R packages and software.

The main goals of this course are to:

1. Introduce students to digital trace data for demographic research, including their collection, analysis, ethical considerations, strengths, and limitations, as well as the core demographic and social science methods that are essential for interpreting digital trace data.
2. Introduce students to core data science methods that are key to advance our understanding of population processes in the context of the increasing heterogeneity of data sources useful for demographic research.
3. Introduce students to statistical methods and demonstrate how they can be applied to demographic problems involving missing data, data scarcity, small-area estimation, and the integration of multiple data sources.
4. Introduce students to computational approaches in population research, including demographic simulation, microsimulation, and kinship modeling, highlighting when and how simulation complements empirical research.
5. Familiarize students with contemporary applications in Digital and Computational Demography, fostering critical thinking about research design, methodological choices, modern demographic analysis and data-driven discovery.
6. Support students identify research questions in their own area of substantive interest that could be addressed with innovative data sources, and support them in the process of devising an appropriate research plan.
7. Provide students with practical skills for acquiring and processing digital data, including the use of AI coding agents, web APIs, and specialized software packages in R to collect, manage, and analyze digital trace data.

Organization

The course will be offered in a hybrid format: in-person for students in the PHDS network who are already in Rostock; online (via Zoom) for everyone else. Each day, there will be one lecture and one discussion session. The lecture will be pre-recorded and made available ahead of time.

Students are expected to watch the lecture carefully at their own pace, complete the assigned readings, and, where applicable, complete any assignments before the live discussion session. The live discussion session will be held every day from 14:00–17:00 CET (Central European Time). During the discussion session, the assigned readings will be discussed, assignments and/or hands-on computing exercises will be reviewed, and questions about the lecture will be addressed. Active student participation is expected.

Each day, the lecture and discussion session will be presented by an experienced scholar in the field who will focus on a relevant research topic in which s/he is an expert.

Students should generally expect to spend about 8 hours per day on the course (lectures, discussion sessions, readings, assignments).

Schedule

Day 1 (Nov. 2nd): Digital trace data for migration research

Instructors: Carolina Coimbra Vieira & Ebru Sanlitürk
Topics: Introduction to migration theories and ethics of digital data use; Fundamentals of data collection and analysis of digital trace data; Advantages and critical challenges of using different types of digital trace data, such as Facebook, Instagram, Twitter, LinkedIn, Google Trends, Wikipedia, and Bibliometric data.

Day 2 (Nov. 3rd): Addressing bias in digital trace data

Instructors: Emilio Zagheni & Tom Theile 
Topics: Approaches for combining representative data and non-probabilistic samples; Identifying sources of bias in digital trace data and adjusting for them. In the practice session, we will learn how to collect digital trace data using AI coding agents to scrape websites and then access web APIs with R.

Day 3 (Nov. 4th): Bayesian approaches with applications for data deficient contexts

Instructors: Benjamin-Samuel Schlüter & Irena Chen
Topics: Introduction to Bayes (comparison to frequentist statistics), including Bayes rule; MCMC algorithms and posterior samples; interpretation of results and model diagnostics; Gompertz mortality model in a Bayesian framework; Practice session will apply Bayesian methods to solve estimation issues in demography (data scarcity, missing data, small area estimation, multiple data sources).

Day 4 (Nov. 5th): Demographic microsimulation and kinship demography

Instructors: Liliana P. Calderón-Bernal & Mallika Snyder
Topics: Overview of key data sources and methods in kinship demography; a brief history of demographic microsimulation; introduction to the SOCSIM platform using the rsocsim package; discussion of applications to research questions in kinship demography; introduction to the Kinship Database project.

Day 5 (Nov. 6th): Simulation in the social and population sciences

Instructors: Boris Barron & Jordan Klein
Topics: A broad overview of simulation as a philosophy and methodology in the social and population sciences (expanding on SOCSIM application introduced in the previous module); when and why simulation is useful as a complement to empirical approaches; simulation paradigms including agent-based models, compartmental models, and demographic microsimulation; hands-on implementation of simulation models in R; applications to segregation, infectious disease epidemiology, and population dynamics.

Diversity of Student Backgrounds

Students in this course have different backgrounds. Some students may have strong computational and statistical skills, others may not. Some students may be very familiar with demographic methods, some others may only have basic knowledge of population processes. The instructors will emphasize substance and key statistical, mathematical, computational, and demographic concepts to accommodate the range of backgrounds. There will also be different types of homework assignments. Some of them will involve computing and coding. Others may involve critical reflections on the readings. In short, we will facilitate the participation of students who do not have an extensive background in statistics or computational methods, but are eager to learn.

Course Prerequisites

Students should be familiar with programming with R/RStudio, Python (Anaconda), or an equivalent programming environment. Homework assignments that require programming can be completed using the programming environment of your choice. Solutions to the assignments will be discussed using R/RStudio or Python (Anaconda).   

Instructions on how to download and install R can be found in “A (very) short introduction to R” by Torfs and Brauer (2014):

https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf

A concise free Python starter course is available on Kaggle: https://www.kaggle.com/learn/python. For installation at Max Planck, use Miniforge from: https://conda-forge.org/download/ or PyCharm: https://www.jetbrains.com/pycharm/. Anaconda: https://www.anaconda.com/download was previously a common choice to download it, but after Anaconda’s licensing changes for scientific institutions, direct use of Anaconda is not supported at Max Planck Institutes and access may be blocked on MPG networks. This restriction applies to Max Planck; colleagues at other educational institutions may still be able to use Anaconda if their institution qualifies under Anaconda’s current academic terms or holds an appropriate license. Please check with your local IT/licensing office.

Examination

Students will receive a pass/fail grade based on a multiple-choice final quiz and active participation in class. Students who pass will receive a certificate of completion.

Tuition

There is no tuition fee for this course.

Recruitment of students external to the IMPRS-PHDS network

Applicants should either be enrolled in a PhD program or have received their PhD. Applications from advanced master’s students will also be considered.

How to apply

• Applications have to be submitted online via https://survey.demogr.mpg.de/index.php/153345?lang=en.
• You will need to attach the following items integrated into a single pdf file:
  • (1) Curriculum vitae, including a list of your scholarly publications.
  • (2) A one-page statement of your research and how it relates to the course. Please include a short description of your knowledge of the programming language R, Python and/or similar.
• The application deadline is 25 September 2026.
• Applicants will be informed of their acceptance by 12 October 2026.
• Applications submitted after the deadline will be considered only if logistically feasible.