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‎README.md‎

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Website: <https://processingcomplexdata.github.io/>
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The course site is built with Jekyll and hosted on GitHub Pages. The landing page is [`index.md`](index.md). The course manual is [`course_manual.md`](course_manual.md), project guidelines are in [`project_guidelines.md`](project_guidelines.md), and the six group projects live in [`projects/`](projects/).
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Lecturer: [Javier Garcia-Bernardo](https://javier.science/), Assistant Professor of Social Data Science, Department of Methodology & Statistics, Utrecht University.
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The course site is built with Jekyll and hosted on GitHub Pages. The landing page is [index.md](index.md). The course manual is [course_manual.md](course_manual.md), project guidelines are in [project_guidelines.md](project_guidelines.md), and the six group projects live in [projects/](projects/).
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All materials are licensed [CC-BY-4.0](LICENSE).

‎_config.yml‎

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title: PCD
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description: Materials for the Methodology and Statistics master course <i>Processing Complex Data</i>.
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description: Materials for the Methodology and Statistics master course Processing Complex Data.
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sidebar: >-
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<span class="sidebar-summary">This webpage contains all materials for the Methodology and Statistics master course <i>Processing Complex Data</i> (PCD). The materials on this website are <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0 licensed</a>.</span>
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<span class="sidebar-block"><strong>Lecturer</strong><br><a href="https://javier.science/">Javier Garcia-Bernardo</a><br>Assistant Professor of Social Data Science<br>Department of Methodology &amp; Statistics<br>Utrecht University</span>
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<span class="sidebar-block sidebar-links"><a href="/course_manual.html">Course manual</a><a href="/project_guidelines.html">Project guidelines</a><a href="https://processingcomplexdata.github.io/">Course website</a></span>
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theme: jekyll-theme-minimal
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logo: /assets/img/logo.svg
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github:

‎_layouts/default.html‎

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<!DOCTYPE html>
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<html lang="{{ site.lang | default: "en-US" }}">
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<head>
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<meta charset="UTF-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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{% seo %}
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<link rel="stylesheet" href="{{ "/assets/css/style.css?v=" | append: site.github.build_revision | relative_url }}">
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<!--[if lt IE 9]>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.3/html5shiv.min.js"></script>
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<![endif]-->
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{% include head-custom.html %}
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</head>
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<body>
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<div class="wrapper">
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<header>
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<h1><a href="{{ "/" | absolute_url }}">{{ site.title | default: site.github.repository_name }}</a></h1>
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{% if site.logo %}
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<img src="{{ site.logo | relative_url }}" alt="Logo" />
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{% endif %}
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<p>{{ site.sidebar | default: site.description | default: site.github.project_tagline }}</p>
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{% if site.github.is_project_page %}
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<p class="view"><a href="{{ site.github.repository_url }}">View the Project on GitHub <small>{{ site.github.repository_nwo }}</small></a></p>
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{% endif %}
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{% if site.github.is_user_page %}
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<p class="view"><a href="{{ site.github.owner_url }}">View My GitHub Profile</a></p>
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{% endif %}
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{% if site.show_downloads %}
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<ul class="downloads">
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<li><a href="{{ site.github.zip_url }}">Download <strong>ZIP File</strong></a></li>
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<li><a href="{{ site.github.tar_url }}">Download <strong>TAR Ball</strong></a></li>
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<li><a href="{{ site.github.repository_url }}">View On <strong>GitHub</strong></a></li>
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</ul>
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{% endif %}
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</header>
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<section>
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{{ content }}
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</section>
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<footer>
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{% if site.github.is_project_page %}
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<p>This project is maintained by <a href="{{ site.github.owner_url }}">{{ site.github.owner_name }}</a></p>
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{% endif %}
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<p><small>Hosted on GitHub Pages &mdash; Theme by <a href="https://github.com/orderedlist">orderedlist</a></small></p>
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</footer>
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</div>
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<script src="{{ "/assets/js/scale.fix.js" | relative_url }}"></script>
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</body>
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</html>

‎assets/css/style.scss‎

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---
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---
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@import "jekyll-theme-minimal";
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body {
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padding: 42px;
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}
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.wrapper {
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width: min(1120px, calc(100vw - 84px));
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}
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header,
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footer {
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width: 310px;
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}
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header p {
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font-size: 12.5px;
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line-height: 1.45;
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}
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header img {
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max-width: 250px;
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margin: 8px 0 22px;
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}
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.sidebar-block {
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display: block;
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margin-top: 14px;
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}
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.sidebar-links a {
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display: block;
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margin: 3px 0;
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}
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section {
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width: 740px;
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}
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@media print, screen and (max-width: 960px) {
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body {
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padding: 28px;
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}
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.wrapper {
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width: auto;
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}
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header,
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footer,
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section {
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width: auto;
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}
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header img {
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max-width: 190px;
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}
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}
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@media print, screen and (max-width: 480px) {
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body {
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padding: 15px;
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}
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}

‎assets/img/logo.svg‎

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‎index.md‎

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# Processing Complex Data
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This webpage contains all materials for the Methodology and Statistics master course **Processing Complex Data (PCD)**. The materials on this website are [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) licensed.
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![cc](https://mirrors.creativecommons.org/presskit/icons/cc.svg) ![by](https://mirrors.creativecommons.org/presskit/icons/by.svg)
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**Lecturer:** [Javier Garcia-Bernardo](https://javier.science/), Assistant Professor of Social Data Science, Department of Methodology & Statistics, Utrecht University.
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## About the course
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Contrary to what most introductory data science and statistics courses teach, real-world scientific data come in an enormous variety of formats, sizes, structures, and procedures — from simple tables to spatiotemporal arrays, normalized relational schemas, nested API responses, raw scraped web pages, networks, and domain-specific scientific standards. This course gives students hands-on experience with handling, processing, and modelling six families of complex data, in a hackathon-style format where each group goes deep on one data type and teaches the rest of the class.
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The narrative spine of the course is *from raw traces to defensible claims*. Each group works through a single pipeline: raw source → operationalized clean object → baseline model with one sensitivity check → presentation.
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## Course materials
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- [Course manual](course_manual.md) — official course description, learning goals, assessment, and materials.
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- [Project guidelines](project_guidelines.md) — workflow files, raw-data policy, decision logs, quality checks, and contribution tracking.
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## Lectures
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| Variant | Data family | Example research question |
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| :------------------- | :------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------- |
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| Geospatial | [`projects/geospatial.md`](projects/geospatial.md) | What is the relation between municipal land use and population composition? |
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| Networks | [`projects/networks.md`](projects/networks.md) | What is the relationship between gender and cross-program relations in high school? |
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| Messy web text | [`projects/messy_web_text.md`](projects/messy_web_text.md) | Do company sustainability pages differ linguistically from public-interest climate information pages? |
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| Relational database | [`projects/relational_database.md`](projects/relational_database.md) | Which driver, constructor, grid, circuit, and season characteristics are associated with F1 finishing points? |
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| Time series | [`projects/time_series.md`](projects/time_series.md) | How does an fMRI signal change across NSD scan sessions? |
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| API data | [`projects/api_data.md`](projects/api_data.md) | Which study attributes are associated with completed versus ongoing clinical trials? |
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| Geospatial | [projects/geospatial.md](projects/geospatial.md) | What is the relation between municipal land use and population composition? |
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| Networks | [projects/networks.md](projects/networks.md) | What is the relationship between gender and cross-program relations in high school? |
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| Messy web text | [projects/messy_web_text.md](projects/messy_web_text.md) | Do company sustainability pages differ linguistically from public-interest climate information pages? |
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| Relational database | [projects/relational_database.md](projects/relational_database.md) | Which driver, constructor, grid, circuit, and season characteristics are associated with F1 finishing points? |
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| Time series | [projects/time_series.md](projects/time_series.md) | How does an fMRI signal change across NSD scan sessions? |
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| API data | [projects/api_data.md](projects/api_data.md) | Which study attributes are associated with completed versus ongoing clinical trials? |

‎projects/api_data.md‎

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- Programming language: `R` (suggested) or `python` (allowed)
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- Expert contact: `Javier Garcia-Bernardo`
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> **Canonical course conventions live in [`project_guidelines.md`](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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> **Canonical course conventions live in [project_guidelines.md](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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‎projects/geospatial.md‎

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- Programming language: `R` (suggested) or `python` (allowed)
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- Expert contact: TBD, Marco Helvich
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> **Canonical course conventions live in [`project_guidelines.md`](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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> **Canonical course conventions live in [project_guidelines.md](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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‎projects/messy_web_text.md‎

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- Programming language: `python` (suggested) or `R` (allowed)
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- Expert contact: TBD, Anastasia?
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> **Canonical course conventions live in [`project_guidelines.md`](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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> **Canonical course conventions live in [project_guidelines.md](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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‎projects/networks.md‎

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- Programming language: `python` (suggested) or `R` (allowed)
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> **Canonical course conventions live in [`project_guidelines.md`](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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> **Canonical course conventions live in [project_guidelines.md](../project_guidelines.md).** That file is the source of truth for the four required workflow files (`week1_explore.qmd`, `week2_operationalize_clean.qmd`, `week3_model.qmd`, `week4_storytelling.qmd`), the `data/model_data.rds` -> `data/model_results.rds` pipeline, the raw-data policy, quality-check requirements, decision logs, and contribution tracking. Read it before starting and treat anything below as project-specific guidance on top of those conventions.
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