1. Learning outcomes
   On a general level the student shall acquire practical knowledge of the important components of formal demography. Specifically, students will be able to:
   • use infinitesimal, differential, integral, and matrix calculus in their future practice;
   • acquire an overview of usual formalization in demography.
2. Course content
   The course is divided into sever modules:

• • Causation
    The course aims to introduce students to principles of counterfactual causal inference, providing them with an overview of some methods in this field. Focus is placed on methods for conditioning on observables (i.e. regression adjustment, g-computation, propensity score methods, imputation methods for missing data), with a possibility, if there is interest, to also delve into methods for conditioning unobservables (i.e. individual intercepts, regression discontinuity, difference-in-differences, and instrumental variables).
  • Event History Analysis
    During these courses, students shall acquire practical knowledge of the important components of formal demography. Specifically, students will be able to use infinitesimal, differential, integral, and matrix calculus in their future practice and acquire an overview of usual formalization in demography. The module will deal primarily with so-called event history models, statistical techniques used to analyze the occurrence of events in time, such as death, marriage, childbirth, entry into retirement, etc. The following topics will be covered:
    • Characterizing duration distributions and common parametric families
    • Observation schemes: censoring and truncation
    • Nonparametric approaches
    • Basic hazard regression (proportional hazards)
    • The Cox PH model, model diagnostics
    • Discrete-time hazard regression
    • The piece-wise constant hazard model; aggregate event-data
    • Non-proportional hazards models
    • Unobserved heterogeneity, repeated events, competing risks, multistate models

• • Sequence Analysis
    The increasing availability of (longitudinal) data and the explosion of big data in demography and other social sciences present methodological challenges in relation to the significant reduction of information that allows the isolation and identification of crucial properties and relationships. At the same time, there is growing interest in understanding what are the relevant patterns in time-related processes that have recently gained complexity such as professional careers, family trajectories or migration pathways. The course aims to provide a concise introduction to sequence analysis, its origins and its applications, which will be demonstrated with hands-on practice using the software R (package TraMineR). The course covers basics of data management, major techniques of algorithmic sequence comparison, sequence visualization, grouping of sequences in typologies, as well as recent developments and applications in the social sciences.

• • Qualitative Research in Demography
    The rise in qualitative contributions to research endeavours has been mirrored in Demography, traditionally a quantitative discipline. Since 1980s, populations studies scientists have increasingly used qualitative methods. The methods used have themselves shifted over tome, from ethnographic approaches, towards increasing reliance on focus groups and in-depths interviews. Qualitative approaches are used in demography at multiple points in the production of knowledge, including: design and testing of quantitative questionnaires; to understand unexpected survey results; and, to grasp sensitive issues, perceptions, “cultural contexts”, and other elements of the social world which are difficult to measure quantitatively.
  • Multilevel Data Analysis
    This course will teach you a basic conceptual understanding of the multilevel (also known as mixed or hierarchical) analysis. The seminars will cover theoretical fundamentals of the multilevel approach to the data and show potential applications, paying particular attention to the solid understanding of key concepts (such as levels, fixed and random coefficients, variability or shrinkage), reasonable approach to model building, and interpretation of the results. Issues related to contextual effects, within-between models, and cross-level interactions will also be discussed. Further, the course will devote time to specific applications, such as repeated measures (panel data analysis) or non-linear models, and the Bayesian approach to multilevel analysis.
  • Agent-based Modelling and Simulation
    The aim of the course is to connect microsimulation, agent-based modeling (ABM), and probability theory. Microsimulation models and ABM rely on random variables and their probability distributions. One particular function, the quantile function or inverse distribution function, is the workhorse of both microsimulation and ABM. Different quantile functions are covered and their use in stochastic agent-based modeling discussed. Illustrations are in R.

3. Assessment
The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation or group activities.

1. Learning outcomes
   On a general level the student shall acquire a thorough knowledge of the theories and trends behind the causes of various demographic outcomes. Specifically, students will be able to:
   • make use of theories to analyse changes in fertility, nuptiality, mortality and migration
   • make use of theories to analyse changes in long-term relationships between population development and living conditions
   • understand the precise mechanisms by which personal attributes, including the stage in the life course, and contextual factors, such as economic conditions and socio-cultural system, affect fertility, nuptiality, mortality and migration
   • understand the mechanisms by which events and conditions during one stage of life affect demographic events and behaviour later in life
   • prove a disprove a theory using falsifiable hypotheses
   • present a theoretically based analysis of the complex interplay between population change and economic and social development.
2. Course content
   The aim of the course is to introduce students to macro-level theories of population change, micro-level theories of demographic behaviour and the micro-macro interactions. At the end of the courses, students should comprehend the major theories that explain the trends and patterns of fertility, family formation and dissolution, the ageing of individuals and society, migration behaviour and migration systems. These theories are situated within the overarching framework of the human life course, embedded in institutional contexts that reflect economic, social, cultural and historical conditions. In addition, students should understand the demographic transition and the demographic response to situational changes such as technological change, economic development, food shortage and economic crisis. Therefore, theories explaining both the influence of population growth on economic, social, and environmental development and vice-versa are discussed. Students should be able to apply these theories to interpret data on levels and differentials in demographic change and the drivers: fertility, mortality and migration, to identify how long-term and short-term economic changes influence population behaviour as well as to understand the complex interrelationships between population and living standards by using information with details at individual and family, and household levels.
   The course is divided into four modules and covers the following topics:
   1. Theories of Fertility, Family and the Life Course
      • 1. Major trends in fertility and the family and cross-national differences (i.e. low fertility, LAT, divorce, etc.)
      • Natural fertility and proximate determinants of fertility
      • The impact of values, norms and economics on fertility behaviour
      • The impact of gender policies, labour market policies and institutional arrangements (e.g. child care) on fertility behaviour
      • Theories of the family and family dynamics (including the Second Demographic Transition, intergenerational transfers of values, norms and resources)
      • Causation in the field of family demography
      • Consequences of family change on subjective well-being
   2. Theories of Mortality and Morbidity
      • Major trends in life expectancy, health expectancy, diseases and causes of death
      • Facts, trends, and potential explanations of (gender, socio-economic, etc.) differences in mortality
      • Introduction to biodemography of human aging; dynamics of heterogenous populations and mortality plateau; and mortality improvements and tempo effects
      • Theories of the epidemiologic transition
      • Trends and theoretical frameworks of healthy life expectancy and other indicators of lifespan length
      • Compression and expansion of morbidity: impact of lifestyle, environmental, socio- economic and cultural factors (including institutional factors, such as characteristics of the health care system)
   3. Migration
      This module comprises several courses which cover different, but essential, aspects surrounding the field of migration studies.
      • Theories of Migration: During the lectures, the students will get to know some main theoretical approaches of migration and residential mobility, derived from various disciplines (geography, demography, economics). Students will work actively with the theories by discussing the implications of the theories for empirical research.
      • Consequences of International Migration: The goal of this course is to explore the consequences of international migration. This will be done by covering the main methodological challenges of the study of international migration, giving an overview of immigrants’ integration theories and measurements, and emphasizing the links between international migration and demography.
      • Migration, Health, and Mortality in Developing Countries: This course endeavours to explain the relationship between migration (internal or international), health, and mortality in Low and Middle Income Countries (LMIC), with occasional references to More Developed Countries (MDC). Concepts, theories and hypotheses will be presented and some case studies will be discussed and confronted to theories and related hypotheses. Students can expect to understand and use the main concepts and terminology in migration and health studies; to understand the main hypotheses regarding the impact of migration on health; to know about the main sources of data on migration and their limitations; and to understand papers on migration and health through expanding upon its methodologies, results, and discussions.
   4. Historical Demography
      This course deals with the evolution of population in the long run, particularly in relation to economic development. By relying both on standard works in the field and on the most recent studies in historical demography and economic history, the course aims to give a broad but critical perspective on the evolution of population, and demography itself, over time and also discuss its relation with the subsequent emergence of the modern economy. The seminars aim to address the larger question of the role of different actors – individuals and families but also the state, various institutions, and demographers themselves—on the way the population issues are shaped, discussed, and understood by: introducing the intellectual foundations of demography, discussing the importance of sources for demography, by presenting a (concise) history of demographic sources and their political foundations, and exploring the evolution of population itself, with an emphasis on long term changes and the mechanisms underlying them.
3. Assessment
   The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation as well as group activities.

1. Learning outcomes
   On a general level the student shall acquire practical knowledge of the use and calculation of summary measures using various data sources. Specifically, students will be able to:
   • individually discuss and calculate basic summary measures
   • link fertility and mortality laws to population dynamics
   • use multistate life tables, compare standardization methods
   • understand the methods used in working with incomplete data
   • work on building and using consistent time series
   • use heterogeneous information in a consistent way
   • understand and discuss qualitative approach in demography
2. Course content
   The course is divided into three modules:
   • Introduction to Demography. Data Quality and Types
     This module offers an overview to key concepts of data quality (e.g. accuracy, coherence, comparability, clarity) and applies them to three commonly used demographic data sources: registers, censuses and surveys. Sessions may revolve around discussions on: components of data quality and introduction to register data; population censuses and the IPUMS international project; and cross-sectional and longitudinal survey data.
   • Dealing with Data
     This module is all about data wrangling. Since students will have taken an introductory programming course as a pre-requisite, this module will allow students to extend their expertise of working with data in R. The module will not only go over necessary concepts for programming, but will mainly consist of working through examples of data wrangling in R. Tidyverse will be the main framework used to work with data.
   • Digital Demography
     The global spread of Internet, social media, and digital technologies is radically transforming the way we live and communicate, is creating new challenges and opportunities for our societies, and is enabling social scientists to address longstanding demographic research questions with new data sources—potentially requiring new conceptual and methodological approaches. With emphasis placed on population processes, discussions will be based around several substantive topics related to the emergence of (big) data-driven discovery in social sciences.
     The main goals of this module are to introduce students to: recent advancements made in the field of Digital and Computational Demography; some methods, approaches, and tools of data science in the context of population research; and to prompt critical thinking about modern demographic analysis and (online) data-driven discovery.
3. Assessment
   The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation as well as group activities.

1. Learning outcomes
   On a general level the student shall acquire practical knowledge of the modeling, simulation and forecasting of various populations. Specifically, students will be able to:
   • analyse the dynamics of age-structured and of interacting populations
   • learn about new indicators of aging and how to evaluate them
   • learn how to prepare initial data for population projection (life table extension, smoothing age-specific fertility and mortality rates)
   • forecast population development using the cohort component approach
   • learn how to define scenarios in terms of aggregate indicators and apply demographic models in order to obtain age-specific rates
   • apply household projection methods
   • individually simulate multi-state populations
   • discuss the fundamentals of microsimulation models
2. Course content
   The course is divided into three modules:
   • Social Indicators: Theory and Applications
     This course reviews some of the most popular indicators that are used to describe the social and structural conditions under which most demographic phenomena unfold. Starting with well-known inequality and poverty indicators, the course also explores the construction of composite indices and a variegated set of measures that are used to assess the extent of heterogeneity among populations.
   • Measuring the Generational Economy
     The course explores the question: How can we use economic data to analyse the relation between demography and economics? The course is organized based on the framework of National Accounts and its applications, specifically regarding the organization of the generational economy, demography and production/growth, and generational equity.
   • Demography and Inequality
     Income and wealth inequality have received renewed interest by social scientists over the last two decades. In many countries, both have increased dramatically and have been held responsible for a wide variety of societal developments. Therefore, this module reviews and discusses trends in economic inequality in OECD countries and discusses some of the narratives that offered to explain these. Different aspects and domains contributing to the inequality of opportunity are considered—some examples include: income and occupational mobility across generations; families, schools, and neighborhoods; and changing family dynamics in people’s life courses during the post-war period.
3. Assessment
   The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation as well as group activities.

1. Learning outcomes
   On a general level the student shall acquire practical knowledge of the modeling, simulation and forecasting of various populations. Specifically, students will be able to:
   • analyse the dynamics of age-structured and of interacting populations
   • learn about new indicators of aging and how to evaluate them
   • learn how to prepare initial data for population projection (life table extension, smoothing age-specific fertility and mortality rates)
   • forecast population development using the cohort component approach
   • learn how to define scenarios in terms of aggregate indicators and apply demographic models in order to obtain age-specific rates
   • apply household projection methods
   • individually simulate multi-state populations
   • discuss the fundamentals of microsimulation models
2. Course content
   • Population Projections and Forecasts
     This course provides an introduction to population projections by discussing the constant exponential growth model, also spending time on the cohort component method and matrix projections. The lectures are interactive and R will be used to go over relevant examples and assignments for a more hands-on experience with the methods. Students also receive an introduction to dynamic visualizations (i.e. animategraphics, gganimate, and .gifs) and shiny apps. In terms of population forecasting, several topics may be explored and applied during lectures: the difference between population projections and forecasting, Lee-Carter modelling and forecasting, functional forecasting, and coherent forecasting—taking another population into account.
3. Assessment
   The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation as well as group activities.

1. Learning outcomes
   The course is designed as a series of lectures and seminars. Grading is based on individual performance, via written assignments, oral presentation as well as group activities.
   • identify a relevant research question
   • frame the research aims and goals of an independent doctoral study
   • prepare a well-structured thesis proposal
   • present a written report, in accordance with academic standards, describing their research
   • discuss, on the basis of academic standards, research reports
   • give a scientific presentation
   • prepare a scientific poster
   • write a well-structured scientific paper, chose an appropriate journal to submit the paper to, make revisions to the paper according the peer reviews
2. Course content
   The student has to define a research issue, carry out research and write the thesis independently, although with support from a supervisor. At an early stage, a supervisor will be allotted to the student on the basis of their area of interest. Well before the actual period of the thesis work, a series of preparatory seminars will be held. There, students will present their ideas and plans for their research. It is the task of the supervisor to support the development from idea to plan, and thereafter to stimulate and criticize the student’s work.
   A thesis proposal/ research paper should consist of original, independently executed work. The general structure of the proposal’s content should be: (1) Specific aims; (2) Background and significance; (3) Preliminary studies; (4) Research design and methods. The general structure of the proposal’s content should be: (1) Introduction; (2) Data and Methods; (3) Results; (4) Summary and Discussion.
3. Assessment
   Teaching takes place primarily through individual supervision and discussions in the student group at seminars, at different phases of the project, led by the examiner. Throughout the writing process, the student can consult her/his supervisor for advice, feedback and criticism.