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---
title: "Courses"
subtitle: "Structured learning pathways for computational and quantitative methods in language research"
toc: true
toc-depth: 2
---
```{=html}
<style>
/* ── Course catalogue cards ──────────────────────────────────────── */
.course-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
gap: 16px;
margin: 1.5rem 0;
}
.course-card {
background: #fff;
border: 1px solid #e8e4f0;
border-radius: 8px;
overflow: hidden;
display: flex;
flex-direction: column;
transition: box-shadow 0.2s;
}
.course-card:hover { box-shadow: 0 4px 16px rgba(81,36,122,0.12); }
.course-card-header {
padding: 16px 18px 12px;
border-bottom: 1px solid #f0eaf7;
}
.course-format-tag {
display: inline-flex;
align-items: center;
gap: 5px;
font-size: 0.7rem;
font-weight: 700;
padding: 2px 9px;
border-radius: 20px;
margin-bottom: 8px;
text-transform: uppercase;
letter-spacing: 0.04em;
}
.tag-short { background: #e6f0fb; color: #2a5ea8; }
.tag-long { background: #f0e6fb; color: #6b1e9c; }
.course-card-header h4 {
margin: 0 0 4px 0;
font-size: 0.9rem;
font-weight: 700;
color: #51247A;
line-height: 1.3;
}
.course-card-header h4 a {
color: inherit;
text-decoration: none;
}
.course-card-header h4 a:hover { color: #00A2C7; }
.course-card-body {
padding: 12px 18px 14px;
flex: 1;
display: flex;
flex-direction: column;
gap: 8px;
}
.course-meta-row {
display: flex;
align-items: center;
gap: 6px;
font-size: 0.78rem;
color: #444;
}
.course-meta-row .meta-icon { flex-shrink: 0; font-size: 0.85rem; }
.course-card p.cc-desc {
margin: 0;
font-size: 0.82rem;
color: #222;
line-height: 1.5;
}
.difficulty-bar {
display: flex;
align-items: center;
gap: 8px;
margin-top: 4px;
}
.difficulty-bar .db-label { font-size: 0.72rem; color: #888; width: 70px; flex-shrink: 0; }
.difficulty-dots { display: flex; gap: 3px; }
.difficulty-dots span {
width: 8px; height: 8px;
border-radius: 50%;
background: #e0d8eb;
}
.difficulty-dots span.active { background: #51247A; }
.course-card-footer {
padding: 10px 18px;
border-top: 1px solid #f0eaf7;
}
.cc-link {
font-size: 0.78rem;
font-weight: 600;
color: #51247A;
text-decoration: none;
}
.cc-link:hover { color: #00A2C7; }
/* ── Section header strips ───────────────────────────────────────── */
.section-banner {
background: linear-gradient(135deg, #51247A 0%, #3d1a5e 100%);
color: white;
border-radius: 8px;
padding: 22px 28px;
margin: 2rem 0 1.5rem 0;
display: flex;
align-items: center;
gap: 18px;
}
.section-banner .sb-icon { font-size: 2rem; flex-shrink: 0; }
.section-banner h2 { margin: 0 0 4px 0; color: white; font-size: 1.1rem; }
.section-banner p { margin: 0; color: #e8d8f8; font-size: 0.875rem; line-height: 1.5; }
/* ── Individual course blocks ────────────────────────────────────── */
.course-block {
border: 1px solid #e8e4f0;
border-radius: 8px;
overflow: hidden;
margin-bottom: 2rem;
}
.course-block-header {
background: #51247A;
padding: 20px 28px;
display: flex;
align-items: flex-start;
justify-content: space-between;
gap: 16px;
flex-wrap: wrap;
}
.course-block-header.aqua { background: linear-gradient(135deg, #007a9a 0%, #005c75 100%); }
.course-block-header.magenta { background: linear-gradient(135deg, #7a2272 0%, #5c1856 100%); }
.course-block-header.blue { background: linear-gradient(135deg, #2a5ea8 0%, #1d4580 100%); }
.course-block-header.green { background: linear-gradient(135deg, #2a7a3a 0%, #1e5c2c 100%); }
.course-block-header.dark { background: linear-gradient(135deg, #2d2540 0%, #1a1428 100%); }
.cbh-info h3 { margin: 0 0 6px 0; color: white; font-size: 1.05rem; }
.cbh-info p { margin: 0; color: #e8d8f8; font-size: 0.82rem; line-height: 1.5; }
.cbh-badges { display: flex; flex-wrap: wrap; gap: 6px; flex-shrink: 0; }
.cbh-badge {
background: rgba(255,255,255,0.18);
color: white;
font-size: 0.7rem;
padding: 3px 10px;
border-radius: 20px;
font-weight: 600;
white-space: nowrap;
}
.cbh-badge.free-badge { background: rgba(46,168,54,0.5); }
.course-block-meta {
background: #faf8fd;
padding: 14px 28px;
display: flex;
flex-wrap: wrap;
gap: 20px;
border-bottom: 1px solid #e8e4f0;
}
.cbm-item { font-size: 0.82rem; color: #333; display: flex; align-items: center; gap: 6px; }
.cbm-item strong { color: #111; }
.course-block-body { padding: 20px 28px; }
.course-block-body > p { font-size: 0.875rem; color: #444; line-height: 1.65; margin: 0 0 16px 0; }
/* ── Outcomes list ───────────────────────────────────────────────── */
.outcomes-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
gap: 8px;
margin: 0 0 20px 0;
}
.outcome-item {
background: #f7f5fb;
border-radius: 4px;
padding: 9px 12px;
font-size: 0.82rem;
color: #444;
line-height: 1.5;
display: flex;
gap: 8px;
align-items: flex-start;
}
.outcome-item::before {
content: "✓";
color: #51247A;
font-weight: 700;
flex-shrink: 0;
margin-top: 0px;
}
/* ── Tutorial sequence ───────────────────────────────────────────── */
.tutorial-sequence {
display: flex;
flex-direction: column;
gap: 0;
margin: 16px 0;
border: 1px solid #e8e4f0;
border-radius: 6px;
overflow: hidden;
}
.tseq-item {
display: flex;
align-items: flex-start;
gap: 14px;
padding: 13px 16px;
border-bottom: 1px solid #f0eaf7;
background: #fff;
transition: background 0.15s;
}
.tseq-item:last-child { border-bottom: none; }
.tseq-item:hover { background: #faf8fd; }
.tseq-num {
background: #51247A;
color: white;
font-size: 0.72rem;
font-weight: 700;
width: 22px; height: 22px;
border-radius: 50%;
display: flex; align-items: center; justify-content: center;
flex-shrink: 0;
margin-top: 1px;
}
.tseq-info h5 {
margin: 0 0 2px 0;
font-size: 0.875rem;
font-weight: 700;
}
.tseq-info h5 a { color: #51247A; text-decoration: none; }
.tseq-info h5 a:hover { color: #00A2C7; }
.tseq-info p { margin: 0; font-size: 0.8rem; color: #666; line-height: 1.5; }
/* ── Week accordion ──────────────────────────────────────────────── */
.week-accordion { margin: 16px 0; }
.week-item {
border: 1px solid #e8e4f0;
border-radius: 6px;
overflow: hidden;
margin-bottom: 6px;
}
.week-item summary {
list-style: none;
padding: 12px 18px;
cursor: pointer;
background: #fff;
display: flex;
align-items: center;
gap: 12px;
user-select: none;
}
.week-item summary::-webkit-details-marker { display: none; }
.week-item[open] summary { background: #f7f5fb; border-bottom: 1px solid #e8e4f0; }
.week-item summary:hover { background: #f7f5fb; }
.week-num-badge {
background: #51247A;
color: white;
font-size: 0.7rem;
font-weight: 700;
padding: 2px 8px;
border-radius: 3px;
flex-shrink: 0;
white-space: nowrap;
}
.week-title {
font-size: 0.875rem;
font-weight: 600;
color: #333;
flex: 1;
}
.week-chevron {
font-size: 0.7rem;
color: #999;
transition: transform 0.2s;
flex-shrink: 0;
}
.week-item[open] .week-chevron { transform: rotate(180deg); }
.week-body {
padding: 14px 18px;
background: #faf8fd;
font-size: 0.85rem;
color: #444;
line-height: 1.6;
}
.week-body p { margin: 0 0 8px 0; }
.week-body ul { margin: 4px 0 10px 16px; padding: 0; }
.week-body ul li { margin-bottom: 3px; }
.week-body .week-section-label {
font-size: 0.7rem;
font-weight: 700;
text-transform: uppercase;
letter-spacing: 0.06em;
color: #51247A;
margin: 10px 0 4px 0;
}
.week-body .week-section-label:first-child { margin-top: 0; }
.week-tutorial-links { display: flex; flex-wrap: wrap; gap: 6px; margin: 4px 0 10px 0; }
.wtl-link {
background: #f0eaf7;
color: #51247A !important;
font-size: 0.75rem;
padding: 3px 10px;
border-radius: 20px;
text-decoration: none !important;
font-weight: 500;
border: 1px solid #d8cce8;
}
.wtl-link:hover { background: #51247A; color: white !important; }
/* ── Reading list ────────────────────────────────────────────────── */
.reading-list {
background: #fff;
border: 1px solid #e8e4f0;
border-radius: 6px;
padding: 16px 20px;
margin: 16px 0;
}
.reading-list h5 { margin: 0 0 10px 0; font-size: 0.85rem; color: #51247A; font-weight: 700; }
.reading-list ul { margin: 0; padding-left: 18px; }
.reading-list ul li { font-size: 0.82rem; color: #444; line-height: 1.6; margin-bottom: 5px; }
.reading-list ul li a { color: #51247A; }
/* ── CTA banner ──────────────────────────────────────────────────── */
.cta-banner {
background: linear-gradient(135deg, #51247A 0%, #3d1a5e 100%);
color: white; border-radius: 8px;
padding: 32px 40px;
display: flex; align-items: center;
justify-content: space-between; flex-wrap: wrap; gap: 20px;
margin: 2rem 0;
}
.cta-banner h3 { margin: 0 0 6px 0; color: white; font-size: 1.2rem; }
.cta-banner p { margin: 0; color: #e8d8f8; font-size: 0.875rem; }
.cta-actions { display: flex; gap: 10px; flex-wrap: wrap; }
.btn-aqua {
background: #00A2C7; color: white !important;
padding: 10px 20px; border-radius: 4px; font-weight: 600;
font-size: 0.875rem; text-decoration: none !important; white-space: nowrap;
}
.btn-aqua:hover { background: #008faf; }
.btn-ghost-white {
background: transparent; color: white !important;
padding: 10px 20px; border-radius: 4px; font-weight: 600;
font-size: 0.875rem; text-decoration: none !important;
border: 2px solid rgba(255,255,255,0.5); white-space: nowrap;
}
.btn-ghost-white:hover { border-color: white; background: rgba(255,255,255,0.1); }
@media (max-width: 640px) {
.cta-banner { flex-direction: column; }
.course-block-header { flex-direction: column; }
.cbh-badges { align-self: flex-start; }
}
</style>
```
{width="100%" height="200px" loading="lazy" fetchpriority="high"}
LADAL Courses are curated sequences of tutorials, readings, and practical exercises for learners progressing from foundational knowledge to advanced skills. All courses are free, open, and built around reproducible R workflows. Whether you are a complete beginner or an experienced analyst, there is a pathway here for you.
By following a LADAL course, you will develop practical skills in R — data management, visualisation, statistics, and text analytics — and learn to apply them to real research questions in linguistics, the humanities, and the social sciences.
---
## All Courses at a Glance {#overview}
```{=html}
<div style="display:flex;gap:14px;flex-wrap:wrap;margin:0 0 1.5rem 0;font-size:0.82rem;align-items:center;">
<span><span class="course-format-tag tag-short">Short Course</span> — 6–10 tutorials, self-paced, one focused topic</span>
<span><span class="course-format-tag tag-long">Long Course</span> — 12-week semester programme with weekly lectures, tutorials, and readings</span>
</div>
<div class="course-grid">
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-short">Short Course</span>
<h4><a href="#langtech">Introduction to Language Technology</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Linguists and humanities students</div>
<div class="course-meta-row"><span class="meta-icon">📚</span> 6 tutorials</div>
<p class="cc-desc">A conceptual and practical first introduction to language technology — from text processing and regex to OCR and NLP overview.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Beginner</span>
</div>
</div>
<div class="course-card-footer"><a href="#langtech" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-short">Short Course</span>
<h4><a href="#corpusling-short">Introduction to Corpus Linguistics</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Linguistics students and language teachers</div>
<div class="course-meta-row"><span class="meta-icon">📚</span> 7 tutorials</div>
<p class="cc-desc">Core corpus methods — concordancing, collocations, keyness, and frequency analysis — using R and reproducible workflows.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Beginner</span>
</div>
</div>
<div class="course-card-footer"><a href="#corpusling-short" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-short">Short Course</span>
<h4><a href="#textanalysis-short">Introduction to Text Analysis</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Humanities and social science students</div>
<div class="course-meta-row"><span class="meta-icon">📚</span> 7 tutorials</div>
<p class="cc-desc">From text processing basics to topic modelling, sentiment analysis, and network analysis of text collections.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Beginner</span>
</div>
</div>
<div class="course-card-footer"><a href="#textanalysis-short" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-short">Short Course</span>
<h4><a href="#dataviz-short">Data Visualisation for Linguists</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Linguists and language researchers</div>
<div class="course-meta-row"><span class="meta-icon">📚</span> 6 tutorials</div>
<p class="cc-desc">Publication-quality visualisation with ggplot2 — histograms, scatter plots, maps, Likert charts, and more.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span class="active"></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Introductory</span>
</div>
</div>
<div class="course-card-footer"><a href="#dataviz-short" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-short">Short Course</span>
<h4><a href="#stats-short">Introduction to Statistics</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Humanities and social science researchers</div>
<div class="course-meta-row"><span class="meta-icon">📚</span> 7 tutorials</div>
<p class="cc-desc">Statistical literacy from the ground up — descriptive statistics, hypothesis testing, t-tests, chi-square, and simple regression.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Beginner</span>
</div>
</div>
<div class="course-card-footer"><a href="#stats-short" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-short">Short Course</span>
<h4><a href="#lcr-short">Introduction to Learner Corpus Research</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Applied linguists and SLA researchers</div>
<div class="course-meta-row"><span class="meta-icon">📚</span> 7 tutorials</div>
<p class="cc-desc">Learner corpus methods — frequency comparison, collocations, lexical diversity, readability, and error analysis with ICLE and LOCNESS.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span class="active"></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Introductory</span>
</div>
</div>
<div class="course-card-footer"><a href="#lcr-short" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-short">Short Course</span>
<h4><a href="#nlp-short">Natural Language Processing with R</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Computational linguists and data scientists</div>
<div class="course-meta-row"><span class="meta-icon">📚</span> 7 tutorials</div>
<p class="cc-desc">NLP pipeline in R — preprocessing, TF-IDF, classification, NER, dependency parsing, and introduction to word embeddings.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span class="active"></span><span class="active"></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Intermediate</span>
</div>
</div>
<div class="course-card-footer"><a href="#nlp-short" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-long">Long Course</span>
<h4><a href="#dh-long">Introduction to Digital Humanities with R</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Humanities researchers and students</div>
<div class="course-meta-row"><span class="meta-icon">📅</span> 12 weeks · No background required</div>
<p class="cc-desc">Full semester course: DH methods from data literacy and text processing through corpus analysis, topic modelling, networks, and mapping.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span class="active"></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Foundational</span>
</div>
</div>
<div class="course-card-footer"><a href="#dh-long" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-long">Long Course</span>
<h4><a href="#corpusling-long">Corpus Linguistics and Text Analysis with R</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Linguistics and applied linguistics students</div>
<div class="course-meta-row"><span class="meta-icon">📅</span> 12 weeks · No background required</div>
<p class="cc-desc">Corpus construction through concordancing, collocations, keywords, topic modelling, sentiment analysis, and network analysis.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span class="active"></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Foundational</span>
</div>
</div>
<div class="course-card-footer"><a href="#corpusling-long" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-long">Long Course</span>
<h4><a href="#stats-long">Introduction to Statistics in the Humanities</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Students and researchers, all disciplines</div>
<div class="course-meta-row"><span class="meta-icon">📅</span> 12 weeks · No background required</div>
<p class="cc-desc">From probability and descriptive statistics through regression and mixed-effects modelling, using R throughout.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span class="active"></span><span></span><span></span>
</div>
<span style="font-size:0.72rem;color:#888;">Foundational</span>
</div>
</div>
<div class="course-card-footer"><a href="#stats-long" class="cc-link">View course →</a></div>
</div>
<div class="course-card">
<div class="course-card-header">
<span class="course-format-tag tag-long">Long Course</span>
<h4><a href="#advstats-long">Advanced Statistics in the Humanities</a></h4>
</div>
<div class="course-card-body">
<div class="course-meta-row"><span class="meta-icon">👥</span> Researchers with prior statistics knowledge</div>
<div class="course-meta-row"><span class="meta-icon">📅</span> 12 weeks · Basic stats + R required</div>
<p class="cc-desc">Multivariate modelling, classification trees, random forests, clustering, correspondence analysis, and survey data analysis.</p>
<div class="difficulty-bar">
<span class="db-label">Level</span>
<div class="difficulty-dots">
<span class="active"></span><span class="active"></span><span class="active"></span><span class="active"></span>
</div>
<span style="font-size:0.72rem;color:#888;">Advanced</span>
</div>
</div>
<div class="course-card-footer"><a href="#advstats-long" class="cc-link">View course →</a></div>
</div>
</div>
```
---
## Short Courses {#short-courses}
```{=html}
<div class="section-banner">
<div class="sb-icon">⚡</div>
<div>
<h2>Self-Paced Short Courses</h2>
<p>6–10 tutorials per course. Work through them in order at your own pace — no enrolment needed. Ideal for researchers building a specific skill quickly, or instructors embedding a focused module in a larger course.</p>
</div>
</div>
```
### Introduction to Language Technology {#langtech}
```{=html}
<div class="course-block">
<div class="course-block-header">
<div class="cbh-info">
<h3>Introduction to Language Technology</h3>
<p>A first introduction to language technology — what it is, what it can do, and how to get started</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">6 tutorials</span>
<span class="cbh-badge">No background required</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Anyone curious about how computers process and analyse language</span></div>
<div class="cbm-item">🎯 <span><strong>Aim:</strong> Conceptual and practical first introduction — from text processing and regex to OCR and NLP overview</span></div>
</div>
<div class="course-block-body">
<p>Language technology encompasses the computational tools and methods used to analyse, generate, and interact with human language. This short course introduces learners to the landscape of language technology with hands-on practice in R. By the end, learners will understand the key methods and be equipped to explore more specialised pathways.</p>
<div class="outcomes-grid">
<div class="outcome-item">A conceptual map of language technology and its applications in linguistics and the humanities</div>
<div class="outcome-item">Practical experience loading, cleaning, and exploring text data in R</div>
<div class="outcome-item">Familiarity with regular expressions as a foundation for all text-analytic work</div>
<div class="outcome-item">Hands-on experience with OCR for converting PDFs and scanned documents to text</div>
<div class="outcome-item">An understanding of how corpus tools and NLP pipelines are constructed</div>
</div>
<div class="tutorial-sequence">
<div class="tseq-item">
<div class="tseq-num">1</div>
<div class="tseq-info">
<h5><a href="/tutorials/text_analysis_intro/text_analysis_intro.html">Introduction to Text Analysis</a></h5>
<p>What text analysis is, how it relates to corpus linguistics and NLP, and key concepts: corpus, token, type, frequency, and concordance.</p>
</div>
</div>
<div class="tseq-item">
<div class="tseq-num">2</div>
<div class="tseq-info">
<h5><a href="/tutorials/r_intro/r_intro.html">Getting Started with R</a></h5>
<p>Installing packages, loading data, working with vectors and data frames, and writing simple functions in R and RStudio.</p>
</div>
</div>
<div class="tseq-item">
<div class="tseq-num">3</div>
<div class="tseq-info">
<h5><a href="/tutorials/data_loading/data_loading.html">Loading and Saving Data</a></h5>
<p>Importing text from plain text files, CSV, Excel, and web URLs — and saving results for later use.</p>
</div>
</div>
<div class="tseq-item">
<div class="tseq-num">4</div>
<div class="tseq-info">
<h5><a href="/tutorials/string/string.html">String Processing</a></h5>
<p>Pattern matching, substitution, splitting, and the core string operations (using <code>stringr</code>) that underpin all text analysis.</p>
</div>
</div>
<div class="tseq-item">
<div class="tseq-num">5</div>
<div class="tseq-info">
<h5><a href="/tutorials/regular_expressions/regular_expressions.html">Regular Expressions</a></h5>
<p>Character classes, quantifiers, anchors, and look-arounds with worked linguistic examples — the pattern language for searching and transforming text.</p>
</div>
</div>
<div class="tseq-item">
<div class="tseq-num">6</div>
<div class="tseq-info">
<h5><a href="/tutorials/pdf_to_text/pdf_to_text.html">Converting PDFs to Text</a></h5>
<p>Extracting machine-readable text with <code>pdftools</code> (digital PDFs) and <code>tesseract</code> (scanned documents), including post-OCR spell-checking.</p>
</div>
</div>
</div>
</div>
</div>
```
### Introduction to Corpus Linguistics {#corpusling-short}
```{=html}
<div class="course-block">
<div class="course-block-header aqua">
<div class="cbh-info">
<h3>Introduction to Corpus Linguistics</h3>
<p>Core corpus methods — concordancing, collocations, keyness — using R and reproducible workflows</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">7 tutorials</span>
<span class="cbh-badge">No background required</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Linguistics students; language teachers; researchers new to corpus methods</span></div>
<div class="cbm-item">🎯 <span><strong>Aim:</strong> Introduce concordancing, collocations, and keyness with hands-on R practice</span></div>
</div>
<div class="course-block-body">
<p>Corpus linguistics uses large, principled collections of authentic text to investigate patterns of language use. This short course takes learners from a conceptual introduction through hands-on practice with the most widely used corpus methods, culminating in a case-study showcase integrating all techniques into a full corpus-based analysis.</p>
<div class="outcomes-grid">
<div class="outcome-item">What a corpus is and how corpus-based research differs from introspective approaches</div>
<div class="outcome-item">Practical skills in frequency analysis, concordancing, collocation, and keyword extraction using R</div>
<div class="outcome-item">Ability to design, conduct, and report a reproducible corpus-based study</div>
<div class="outcome-item">Familiarity with key R packages: <code>quanteda</code>, <code>tidytext</code>, and related tools</div>
</div>
<div class="tutorial-sequence">
<div class="tseq-item"><div class="tseq-num">1</div><div class="tseq-info"><h5><a href="/tutorials/text_analysis_intro/text_analysis_intro.html">Introduction to Text Analysis</a></h5><p>Key concepts — corpus, concordance, collocation, keyword, frequency — used throughout the course.</p></div></div>
<div class="tseq-item"><div class="tseq-num">2</div><div class="tseq-info"><h5><a href="/tutorials/r_intro/r_intro.html">Getting Started with R</a></h5><p>First introduction to R and RStudio. Focus on the first four sections (up to Working with Tables).</p></div></div>
<div class="tseq-item"><div class="tseq-num">3</div><div class="tseq-info"><h5><a href="/tutorials/string/string.html">String Processing</a></h5><p>Essential string manipulation: pattern matching, substitution, tokenisation preparation, and whitespace management.</p></div></div>
<div class="tseq-item"><div class="tseq-num">4</div><div class="tseq-info"><h5><a href="/tutorials/concordancing/concordancing.html">Concordancing (Keywords-in-Context)</a></h5><p>KWIC concordance search and display in R — sorting, filtering, and interpreting concordance output.</p></div></div>
<div class="tseq-item"><div class="tseq-num">5</div><div class="tseq-info"><h5><a href="/tutorials/coll/coll.html">Collocation and N-gram Analysis</a></h5><p>Statistically significant collocations and n-gram sequences — PMI, log-likelihood, t-score, and visualisation.</p></div></div>
<div class="tseq-item"><div class="tseq-num">6</div><div class="tseq-info"><h5><a href="/tutorials/keywords/keywords.html">Keyness and Keyword Analysis</a></h5><p>Comparing two corpora to identify words that are statistically more or less frequent — the foundation of contrastive corpus analysis.</p></div></div>
<div class="tseq-item"><div class="tseq-num">7</div><div class="tseq-info"><h5><a href="/tutorials/corplingr/corplingr.html">Corpus Linguistics with R</a></h5><p>Capstone showcase: complete case studies integrating concordancing, frequency analysis, collocations, and keyness.</p></div></div>
</div>
</div>
</div>
```
### Introduction to Text Analysis {#textanalysis-short}
```{=html}
<div class="course-block">
<div class="course-block-header magenta">
<div class="cbh-info">
<h3>Introduction to Text Analysis</h3>
<p>From text processing basics to topic modelling, sentiment analysis, and network analysis</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">7 tutorials</span>
<span class="cbh-badge">No background required</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Humanities and social science students; researchers wanting computational approaches to text</span></div>
<div class="cbm-item">🎯 <span><strong>Aim:</strong> Build practical R text analysis skills from cleaning and processing through to advanced methods</span></div>
</div>
<div class="course-block-body">
<p>Text analysis uses computational methods to extract patterns, topics, sentiment, and relational structure from large collections of text. This course builds from foundational R skills through to topic modelling, sentiment analysis, and network analysis. By the end, learners will be able to apply a range of text-analytic methods to their own research texts.</p>
<div class="outcomes-grid">
<div class="outcome-item">An understanding of the major families of computational text analysis and their research applications</div>
<div class="outcome-item">Practical R skills for cleaning, processing, and analysing text data</div>
<div class="outcome-item">Hands-on experience with topic modelling, sentiment analysis, and network analysis</div>
<div class="outcome-item">Ability to select the most appropriate method for a given research question</div>
</div>
<div class="tutorial-sequence">
<div class="tseq-item"><div class="tseq-num">1</div><div class="tseq-info"><h5><a href="/tutorials/text_analysis_intro/text_analysis_intro.html">Introduction to Text Analysis</a></h5><p>Overview of the field, key concepts, and situating text analysis within computational humanities research.</p></div></div>
<div class="tseq-item"><div class="tseq-num">2</div><div class="tseq-info"><h5><a href="/tutorials/r_intro/r_intro.html">Getting Started with R</a></h5><p>First introduction to R and RStudio. Focus on the first four sections.</p></div></div>
<div class="tseq-item"><div class="tseq-num">3</div><div class="tseq-info"><h5><a href="/tutorials/string/string.html">String Processing</a></h5><p>Core string manipulation skills for preparing raw text for analysis.</p></div></div>
<div class="tseq-item"><div class="tseq-num">4</div><div class="tseq-info"><h5><a href="/tutorials/textanalysis/textanalysis.html">Practical Overview of Text Analytics Methods</a></h5><p>Frequency analysis, TF-IDF, and basic classification workflows using R.</p></div></div>
<div class="tseq-item"><div class="tseq-num">5</div><div class="tseq-info"><h5><a href="/tutorials/topic/topic.html">Topic Modelling</a></h5><p>Latent Dirichlet Allocation (LDA) for discovering thematic structure in document collections — theory and R implementation.</p></div></div>
<div class="tseq-item"><div class="tseq-num">6</div><div class="tseq-info"><h5><a href="/tutorials/sentiment/sentiment.html">Sentiment Analysis</a></h5><p>Lexicon-based and machine-learning approaches to opinion and emotion extraction, including dictionary methods and valence shifting.</p></div></div>
<div class="tseq-item"><div class="tseq-num">7</div><div class="tseq-info"><h5><a href="/tutorials/network_analysis/network_analysis.html">Network Analysis</a></h5><p>Representing relational structure in textual and social data — node and edge construction, centrality measures, and visualisation.</p></div></div>
</div>
</div>
</div>
```
### Data Visualisation for Linguists {#dataviz-short}
```{=html}
<div class="course-block">
<div class="course-block-header blue">
<div class="cbh-info">
<h3>Data Visualisation for Linguists</h3>
<p>Publication-quality visualisation with ggplot2 — from frequency plots to maps</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">6 tutorials</span>
<span class="cbh-badge">Basic R helpful</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Linguists and language researchers who want to communicate findings more effectively</span></div>
<div class="cbm-item">🎯 <span><strong>Aim:</strong> Principled, publication-quality data visualisation with ggplot2 and linguistic data</span></div>
</div>
<div class="course-block-body">
<p>Effective visualisation is one of the most transferable skills in quantitative research. This course builds from visualisation principles through the mechanics of ggplot2, covering the graph types most commonly needed in linguistics: frequency distributions, scatter plots, heat maps, geographic maps, and interactive visualisations. Special attention is given to colour accessibility, annotations, and formatting for publication.</p>
<div class="outcomes-grid">
<div class="outcome-item">A principled understanding of what makes a graph effective or misleading</div>
<div class="outcome-item">Practical ggplot2 skills: geoms, scales, facets, themes, and annotations</div>
<div class="outcome-item">Publication-quality static and interactive visualisations from linguistic data</div>
<div class="outcome-item">Confidence choosing the right graph type for the right data and research question</div>
</div>
<div class="tutorial-sequence">
<div class="tseq-item"><div class="tseq-num">1</div><div class="tseq-info"><h5><a href="/tutorials/r_intro/r_intro.html">Getting Started with R</a></h5><p>Introduction to R with a focus on data structures and workflow needed for visualisation.</p></div></div>
<div class="tseq-item"><div class="tseq-num">2</div><div class="tseq-info"><h5><a href="/tutorials/viz_intro/viz_intro.html">Introduction to Data Visualisation</a></h5><p>Visualisation philosophy, perceptual principles, grammar of graphics, and when to use which chart type.</p></div></div>
<div class="tseq-item"><div class="tseq-num">3</div><div class="tseq-info"><h5><a href="/tutorials/dstats/dstats.html">Descriptive Statistics</a></h5><p>Summary statistics — means, medians, distributions, variance — that underpin most visualisations of linguistic data.</p></div></div>
<div class="tseq-item"><div class="tseq-num">4</div><div class="tseq-info"><h5><a href="/tutorials/viz/viz.html">Data Visualisation with R</a></h5><p>In-depth ggplot2: histograms, density plots, box plots, bar charts, scatter plots, and line graphs with worked linguistic examples.</p></div></div>
<div class="tseq-item"><div class="tseq-num">5</div><div class="tseq-info"><h5><a href="/tutorials/surveys/surveys.html">Visualising and Analysing Survey Data</a></h5><p>Cumulative density plots, diverging stacked bar charts, and Likert scale visualisation for questionnaire data.</p></div></div>
<div class="tseq-item"><div class="tseq-num">6</div><div class="tseq-info"><h5><a href="/tutorials/maps/maps.html">Maps and Spatial Visualisation</a></h5><p>Dialect maps, distribution maps, and choropleth maps of linguistic data using ggplot2 and sf.</p></div></div>
</div>
</div>
</div>
```
### Introduction to Statistics {#stats-short}
```{=html}
<div class="course-block">
<div class="course-block-header green">
<div class="cbh-info">
<h3>Introduction to Statistics in the Humanities and Social Sciences</h3>
<p>Statistical literacy and practical quantitative skills from the ground up, using R throughout</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">7 tutorials</span>
<span class="cbh-badge">No background required</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Humanities and social science students and researchers with little or no prior statistics knowledge</span></div>
<div class="cbm-item">🎯 <span><strong>Aim:</strong> Build statistical literacy from first principles through inferential testing in R</span></div>
</div>
<div class="course-block-body">
<p>This course provides a conceptual and practical introduction to statistics for researchers whose background is in the humanities or social sciences. It begins with the philosophical foundations of quantitative reasoning and builds through descriptive statistics, visualisation, and inferential testing. By the end, learners will be able to conduct and interpret basic statistical analyses and communicate their results clearly.</p>
<div class="outcomes-grid">
<div class="outcome-item">Solid conceptual understanding of statistical thinking, probability, and hypothesis testing</div>
<div class="outcome-item">Practical R skills for summarising, tabulating, visualising, and testing data</div>
<div class="outcome-item">Ability to select, apply, and interpret t-tests, chi-square, correlation, and simple regression</div>
<div class="outcome-item">Confidence reading and critically evaluating quantitative results in published research</div>
</div>
<div class="tutorial-sequence">
<div class="tseq-item"><div class="tseq-num">1</div><div class="tseq-info"><h5><a href="/tutorials/quant_intro/quant_intro.html">Introduction to Quantitative Reasoning</a></h5><p>Scientific thinking, the logic of hypothesis testing, and the role of quantitative methods in humanities and social science research.</p></div></div>
<div class="tseq-item"><div class="tseq-num">2</div><div class="tseq-info"><h5><a href="/tutorials/quant_basics/quant_basics.html">Basic Concepts in Quantitative Research</a></h5><p>Variables, data types, sampling, populations, reliability, and validity.</p></div></div>
<div class="tseq-item"><div class="tseq-num">3</div><div class="tseq-info"><h5><a href="/tutorials/r_intro/r_intro.html">Getting Started with R</a></h5><p>Introduction to R and RStudio. Focus on the first four sections.</p></div></div>
<div class="tseq-item"><div class="tseq-num">4</div><div class="tseq-info"><h5><a href="/tutorials/table/table.html">Handling Tables in R</a></h5><p>Importing, cleaning, reshaping, and summarising data frames using dplyr and tidyr.</p></div></div>
<div class="tseq-item"><div class="tseq-num">5</div><div class="tseq-info"><h5><a href="/tutorials/dstats/dstats.html">Descriptive Statistics</a></h5><p>Means, medians, standard deviations, distributions, and frequency tables in R.</p></div></div>
<div class="tseq-item"><div class="tseq-num">6</div><div class="tseq-info"><h5><a href="/tutorials/viz_intro/viz_intro.html">Introduction to Data Visualisation</a></h5><p>Visualisation principles and hands-on practice creating and customising graphs in R.</p></div></div>
<div class="tseq-item"><div class="tseq-num">7</div><div class="tseq-info"><h5><a href="/tutorials/inferential_stats/inferential_stats.html">Basic Inferential Statistics</a></h5><p>Hypothesis testing, p-values, confidence intervals, t-tests, chi-square, correlation, and simple linear regression with R exercises.</p></div></div>
</div>
</div>
</div>
```
### Introduction to Learner Corpus Research {#lcr-short}
```{=html}
<div class="course-block">
<div class="course-block-header">
<div class="cbh-info">
<h3>Introduction to Learner Corpus Research</h3>
<p>Corpus methods for studying learner language — from frequency comparison to error analysis</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">7 tutorials</span>
<span class="cbh-badge">Basic corpus linguistics helpful</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Applied linguists; SLA researchers; language teachers and test developers</span></div>
<div class="cbm-item">🎯 <span><strong>Aim:</strong> Introduce LCR methods from corpus construction through to lexical diversity, readability, and error analysis</span></div>
</div>
<div class="course-block-body">
<p>Learner corpus research uses collections of authentic language produced by second-language learners to investigate the structure, development, and distinctiveness of interlanguage. This course covers the major analytical methods — concordancing, frequency comparison, collocation, POS tagging, lexical diversity, and error analysis — using the ICLE and LOCNESS corpora as running examples.</p>
<div class="outcomes-grid">
<div class="outcome-item">What learner corpora are and how they differ from native-speaker corpora</div>
<div class="outcome-item">Skills for comparing learner and native-speaker language quantitatively using R</div>
<div class="outcome-item">Experience with lexical diversity measures, readability scores, and spelling error detection</div>
<div class="outcome-item">Ability to design and interpret a basic learner corpus study in the context of SLA theory</div>
</div>
<div class="tutorial-sequence">
<div class="tseq-item"><div class="tseq-num">1</div><div class="tseq-info"><h5><a href="/tutorials/text_analysis_intro/text_analysis_intro.html">Introduction to Text Analysis</a></h5><p>Key concepts — corpus, frequency, concordance, collocation — underpinning learner corpus research.</p></div></div>
<div class="tseq-item"><div class="tseq-num">2</div><div class="tseq-info"><h5><a href="/tutorials/r_intro/r_intro.html">Getting Started with R</a></h5><p>Data structures and workflow for corpus analysis.</p></div></div>
<div class="tseq-item"><div class="tseq-num">3</div><div class="tseq-info"><h5><a href="/tutorials/string/string.html">String Processing</a></h5><p>Cleaning, normalising, splitting, and extracting character patterns from raw learner corpus texts.</p></div></div>
<div class="tseq-item"><div class="tseq-num">4</div><div class="tseq-info"><h5><a href="/tutorials/concordancing/concordancing.html">Concordancing (Keywords-in-Context)</a></h5><p>Extracting and inspecting KWIC concordances from learner texts to investigate how learners use specific words or constructions.</p></div></div>
<div class="tseq-item"><div class="tseq-num">5</div><div class="tseq-info"><h5><a href="/tutorials/coll/coll.html">Collocation and N-gram Analysis</a></h5><p>Comparing collocational patterns between learner and native-speaker corpora for studying collocational competence and L1 transfer.</p></div></div>
<div class="tseq-item"><div class="tseq-num">6</div><div class="tseq-info"><h5><a href="/tutorials/learner_language/learner_language.html">Analysing Learner Language with R</a></h5><p>Frequency comparison, POS tagging, lexical diversity, readability scores, and spelling error detection with ICLE and LOCNESS examples.</p></div></div>
<div class="tseq-item"><div class="tseq-num">7</div><div class="tseq-info"><h5><a href="/tutorials/keywords/keywords.html">Keyness and Keyword Analysis</a></h5><p>Words systematically over- or under-used by learners relative to native-speaker norms — one of the most informative methods in LCR.</p></div></div>
</div>
</div>
</div>
```
### Natural Language Processing with R {#nlp-short}
```{=html}
<div class="course-block">
<div class="course-block-header dark">
<div class="cbh-info">
<h3>Natural Language Processing with R</h3>
<p>Text preprocessing, feature extraction, classification, NER, and transformer-based representations</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">7 tutorials</span>
<span class="cbh-badge">Intermediate R required</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Computational linguists; data scientists working with language data</span></div>
<div class="cbm-item">🎯 <span><strong>Prerequisite:</strong> Intermediate R skills; basic familiarity with descriptive statistics and simple regression</span></div>
</div>
<div class="course-block-body">
<p>NLP builds on corpus and statistical methods to develop computational pipelines for understanding and generating language at scale. This course introduces the NLP workflow in R using real linguistic datasets, progressing from text preprocessing and feature engineering to supervised classification, topic models, and an introduction to working with large language model embeddings and APIs.</p>
<div class="outcomes-grid">
<div class="outcome-item">Clear understanding of the NLP pipeline from raw text to structured, analysable representations</div>
<div class="outcome-item">Practical preprocessing skills: tokenisation, stopword removal, stemming, and lemmatisation</div>
<div class="outcome-item">Experience building document-feature matrices and applying TF-IDF weighting</div>
<div class="outcome-item">Hands-on practice with text classification, NER, and dependency parsing</div>
<div class="outcome-item">Introduction to word embeddings and transformer-based representations</div>
</div>
<div class="tutorial-sequence">
<div class="tseq-item"><div class="tseq-num">1</div><div class="tseq-info"><h5><a href="/tutorials/text_analysis_intro/text_analysis_intro.html">Introduction to Text Analysis</a></h5><p>Situating NLP within corpus linguistics and computational linguistics.</p></div></div>
<div class="tseq-item"><div class="tseq-num">2</div><div class="tseq-info"><h5><a href="/tutorials/string/string.html">String Processing</a></h5><p>Foundation string manipulation — essential for all preprocessing steps in NLP pipelines.</p></div></div>
<div class="tseq-item"><div class="tseq-num">3</div><div class="tseq-info"><h5><a href="/tutorials/regular_expressions/regular_expressions.html">Regular Expressions</a></h5><p>Regex as the primary pattern-matching tool in text preprocessing and feature extraction.</p></div></div>
<div class="tseq-item"><div class="tseq-num">4</div><div class="tseq-info"><h5><a href="/tutorials/textanalysis/textanalysis.html">Practical Overview of Text Analytics Methods</a></h5><p>Document-feature matrices, TF-IDF, and basic classification workflows in R.</p></div></div>
<div class="tseq-item"><div class="tseq-num">5</div><div class="tseq-info"><h5><a href="/tutorials/topic/topic.html">Topic Modelling</a></h5><p>Probabilistic topic models as an unsupervised NLP method for discovering thematic structure.</p></div></div>
<div class="tseq-item"><div class="tseq-num">6</div><div class="tseq-info"><h5><a href="/tutorials/learner_language/learner_language.html">Analysing Learner Language with R</a></h5><p>POS tagging with udpipe, sequence analysis, and lexical diversity measures — key NLP tasks applied to real corpus data.</p></div></div>
<div class="tseq-item"><div class="tseq-num">7</div><div class="tseq-info"><h5><a href="/tutorials/network_analysis/network_analysis.html">Network Analysis</a></h5><p>Representing relational structure in language data — semantic networks, co-occurrence graphs, and social networks of linguistic interaction.</p></div></div>
</div>
</div>
</div>
```
---
## Long Courses {#long-courses}
```{=html}
<div class="section-banner">
<div class="sb-icon">📅</div>
<div>
<h2>Semester-Length Long Courses</h2>
<p>Structured as 12-week programmes with weekly lectures, LADAL tutorials, and recommended readings. Designed to scaffold a full university course — or for motivated independent learners who want a thorough grounding in a field.</p>
</div>
</div>
```
### Introduction to Digital Humanities with R {#dh-long}
```{=html}
<div class="course-block">
<div class="course-block-header aqua">
<div class="cbh-info">
<h3>Introduction to Digital Humanities with R</h3>
<p>Computational methods for humanistic inquiry — from data literacy through corpus analysis, networks, and mapping</p>
</div>
<div class="cbh-badges">
<span class="cbh-badge">12 weeks</span>
<span class="cbh-badge">No background required</span>
<span class="cbh-badge free-badge">Free</span>
</div>
</div>
<div class="course-block-meta">
<div class="cbm-item">👥 <span><strong>Audience:</strong> Literature, history, cultural studies, linguistics, media studies students and researchers</span></div>
<div class="cbm-item">🕐 <span><strong>Structure:</strong> 1h lecture + 1.5h tutorial per week</span></div>
<div class="cbm-item">🎯 <span><strong>Aim:</strong> Design, conduct, and communicate a reproducible computational analysis of a humanities dataset</span></div>
</div>
<div class="course-block-body">
<p>Digital humanities applies computational methods to humanistic inquiry: analysing large literary corpora, mapping cultural data geographically, tracing discourse patterns across historical archives, or modelling networks of social interaction. This 12-week course introduces students to the core DH toolkit through R, with weekly tutorials grounded in real humanities datasets. No prior programming experience is assumed.</p>
<div class="week-accordion">
<details class="week-item">
<summary><span class="week-num-badge">Week 1</span><span class="week-title">What Is Digital Humanities?</span><span class="week-chevron">▾</span></summary>
<div class="week-body">
<div class="week-section-label">Lecture topics</div>
<p>Overview of digital humanities — history, debates, and current landscape; relationship to corpus linguistics, text analysis, and data science; what counts as DH research.</p>
<div class="week-section-label">LADAL tutorials</div>
<div class="week-tutorial-links"><a href="/tutorials/text_analysis_intro/text_analysis_intro.html" class="wtl-link">Introduction to Text Analysis</a></div>
<div class="week-section-label">Readings</div>
<ul><li>Burdick et al. (2012). <em>Digital humanities.</em> MIT Press, Ch. 1</li><li>Drucker (2021). <em>The digital humanities coursebook.</em> Routledge, Ch. 1</li></ul>
</div>
</details>
<details class="week-item">
<summary><span class="week-num-badge">Week 2</span><span class="week-title">Reproducible Research and Data Management</span><span class="week-chevron">▾</span></summary>
<div class="week-body">
<div class="week-section-label">Lecture topics</div>
<p>Why reproducibility matters in DH; introduction to R and RStudio; file organisation, project workflows, and version control basics.</p>
<div class="week-section-label">LADAL tutorials</div>
<div class="week-tutorial-links"><a href="/tutorials/reproducibility/reproducibility.html" class="wtl-link">Reproducible Research</a><a href="/tutorials/notebooks/notebooks.html" class="wtl-link">Creating R Notebooks</a></div>
<div class="week-section-label">Readings</div>
<ul><li>Flanagan, J. (2025). Reproducibility, replicability, robustness, and generalizability in corpus linguistics. <em>International Journal of Corpus Linguistics.</em> <a href="https://doi.org/10.1075/ijcl.24113.fla">doi:10.1075/ijcl.24113.fla</a></li></ul>
</div>
</details>
<details class="week-item">
<summary><span class="week-num-badge">Week 3</span><span class="week-title">Getting Started with R</span><span class="week-chevron">▾</span></summary>
<div class="week-body">
<div class="week-section-label">Lecture topics</div>
<p>R syntax, data types, vectors, and data frames; the tidyverse ecosystem; reading and writing data.</p>
<div class="week-section-label">LADAL tutorials</div>
<div class="week-tutorial-links"><a href="/tutorials/r_intro/r_intro.html" class="wtl-link">Getting Started with R</a><a href="/tutorials/data_loading/data_loading.html" class="wtl-link">Loading and Saving Data</a></div>
<div class="week-section-label">Readings</div>
<ul><li>Wickham & Grolemund (2016). <em>R for data science.</em> Ch. 1–3. <a href="https://r4ds.had.co.nz">r4ds.had.co.nz</a></li></ul>
</div>
</details>
<details class="week-item">