Standard track
Use Orange Data Mining for demonstrations and assessed workflows. Complete the short R foundation activities for exposure.
Expectations and policies
Course guide
The essential rules for participation, submission, assessment, and choosing the correct learning track.
What this course is for
This six-class strand introduces computational text analysis through a small set of methods and an in-class hackathon. By the end, you should be able to organize a small corpus and choose an appropriate method. You should also be able to inspect the source text and explain the limits of your result.
Course schedule
Sessions 1 and 2 are available now. Each later page opens before its class so that you can review the preparation and assignment requirements.
Regular classes meet Fridays from 09:15 to 11:00 in the Digital Humanities Lab, Johan Huizinga 0.09. The final hackathon meets Nov. 13 from 09:15 to 13:15 in the same room.
| Class | Topic | Main activity | Assignment |
|---|---|---|---|
| Session 1Sep. 18 · 09:15–11:00 | Introduction to Text as Data | Questions, corpora, and reproducible setup | Demo 1 due Sep. 24, 17:00 |
| Session 2Sep. 25 · 09:15–11:00 | Text Preprocessing | Tokens, normalization, and corpus choices | Demo 2 due Oct. 1, 17:00 |
| Session 3Oct. 2 · 09:15–11:00 | Descriptive PatternsPage opens Sep. 25 | Bag of words, document-term matrices, and TF-IDF | Demo 3 due Oct. 8, 17:00 |
| Session 4Oct. 9 · 09:15–11:00 | Clustering and SimilarityPage opens Oct. 2 | Cosine similarity and hierarchical clustering | Demo 4 due Oct. 15, 17:00 |
| Session 5Oct. 16 · 09:15–11:00 | Classification and Sentiment AnalysisPage opens Oct. 9 | Dictionary-based sentiment and manual checking | Demo 5 due Oct. 22, 17:00 |
| Session 6Oct. 30 · 09:15–11:00 | Topic ModelingPage opens Oct. 16 | LDA, document inspection, and project preparation | Demo 6 due Nov. 5, 17:00 |
| FinalNov. 13 · 09:15–13:15 | In-class text-as-data hackathonPage opens Oct. 30 | Group analysis and report | End of the class |
Learning tracks
BA2 review track
Complete the weekly assignments and hackathon in R. Complete the assigned R foundations work. Orange is not permitted for assessed work.
BA2 review policy
Students who took BA2 Digital Korea may join this class to review the content. All assignments that the standard track completes in Orange Data Mining must instead be completed in R. Orange is not permitted for assessed work. Starter scripts and equivalent requirements will be provided.
The R foundations assignments are also required. They include the selected Swirl lessons and both DataCamp courses. You may repeat this sequence. With advance approval, equivalent Swirl or DataCamp blocks may be assigned for additional learning.
BA2 review students form R-only hackathon groups. Mixed Orange and R groups are not used.
Assessment
The digital-humanities strand is worth 25% of the full course grade.
30%
Weekly deliverables & attendance
6 demos · 6 R blocks · 6 attendance marks
70%
In-class hackathon
One four-hour group analysis and report
Completion rubric for weekly demos
- 2 / Complete means every requested task and usable file is present by the deadline.
- 1 / Partial means one or more checklist items are missing or unusable, or the submission arrives within seven days after the deadline.
- 0 / Missing means no required files are submitted, or the submission arrives more than seven days late.
These demos are not evaluated for analytical sophistication or interpretive quality. The checklist determines the score. Feedback may still identify technical mistakes so that they do not carry into the hackathon.
R foundation marks
The six R blocks are assessed separately from the weekly demos. Each block is due at 09:15 when its associated class begins. Each completed block is worth one mark. Partial completion earns 0.5 and missing work earns 0. DataCamp completion is checked through the classroom dashboard. Swirl completion or fallback work is checked in the student repository. The course has 24 available marks across the demos, R blocks, and attendance. These marks are scaled to the 30% weekly deliverables and attendance component.
Attendance points
Attendance is recorded during the first eight minutes of Sessions 1–6. Each verified attendance record is worth one point. The final hackathon readiness check is part of the assessment and does not add a seventh attendance mark. Documented absences are handled according to programme policy. Completing a demo does not create an in-room attendance mark.
Attendance opener and learning questions
The opener asks for your full registered name, an in-room codeword, and a small number of closed questions about concepts from previous weeks. The concept questions are ungraded. Their purpose is to reactivate prior knowledge and help the instructor decide what needs clarification.
- The codeword is given only in the room.
- A paper/manual route is available.
- Named answers are never displayed.
- Only grouped learning patterns are discussed.
- Linked Qualtrics responses are deleted after attendance is reconciled under the course retention workflow.
The survey data are not used for research or machine-learning model training.
Electronics during class
The opening survey and second-half demonstration require devices. The lecture does not.
- Devices may be used for the opening survey.
- At the screens away cue, close laptops and put phones away.
- Keep devices away throughout the lecture block.
- At the laptops open cue, use your laptop for the demonstration and guided work.
Approved accessibility accommodations always take precedence. Speak with the instructor privately if another arrangement is needed.
Submission rules
- Weekly demos are individual work. Demos 1–4 and Demo 6 are due at 17:00 on the Thursday before the next class. Demo 5 is due on Thursday, Oct. 22, as though a regular class followed the next day.
- Submit by committing and pushing the required files to your private GitHub repository.
- Verify the files on github.com. A file left only on your computer is not submitted.
- Do not upload the same assignment separately to Brightspace unless specifically instructed.
- Discussion and troubleshooting are encouraged, but each student maintains their own demo record.
Hackathon groups
Groups contain two or three students. The submission receives a group mark unless documented non-participation requires individual adjustment. The contribution statement and commit history record who did the work. Solo R work is permitted only if the cohort does not allow an appropriate R-only group or an accommodation requires it.
Accessibility
Course pages use semantic headings, keyboard-accessible navigation, high-contrast colors, descriptive links, and responsive layouts. Slides and figures should include readable labels and alternative text where appropriate. Contact the instructor early if a format, classroom practice, or timed activity creates an access barrier.
Academic integrity and data care
Follow Leiden University rules for academic integrity and the parent course’s stated AI policy. Cite outside sources, describe borrowed code, and distinguish assistance from your own analysis. Never place personal, confidential, copyrighted, or access-restricted data in a public repository.