Programming for Data Science in Python · Programming for AI and Data Science¶
Two courses of the Department of Information and Knowledge Engineering (KIZI), Faculty of Informatics and Statistics, Prague University of Economics and Business (VŠE). Each course is taught twice a year under two codes — in Czech in the winter semester and in English in the summer semester — and the two winter courses share one seminar series, as do the two summer courses. All seminar material is in English.
| Code | Course | Language | Semester 2026/27 | Lectures / seminars per week | Syllabus |
|---|---|---|---|---|---|
| 4IZ566 | Programování pro data science v jazyce Python | Czech | Winter (Sept–Dec 2026) | 2 / 2 | INSIS |
| 4IZ582 | Programování pro AI a data science | Czech | Winter (Sept–Dec 2026) | 1 / 2 | INSIS |
| 4IZ565 | Programming for data science in Python (in English) | English | Summer (Feb–May 2027) | 2 / 2 | INSIS |
| 4IZ583 | Programming for AI and data science (in English) | English | Summer (Feb–May 2027) | 1 / 2 | INSIS |
Now running: winter semester 2026/27
4IZ566 and 4IZ582 — Wednesdays in SB M15, first seminar 23 September 2026. Timetable, deadlines and the guest blocks are on the winter page. Seminars: Ing. Lukáš Sýkora, Ph.D. · lectures: prof. Ing. Tomáš Kliegr, Ph.D. — see Teachers.
What the two courses have in common¶
- One shared seminar series and the same nbgrader assignments: Python for data analysis — NumPy, pandas, matplotlib, scikit-learn — with the emphasis on doing it correctly (leakage-free pipelines, honest model selection, cost-aware evaluation); language models from Python: structured output, embeddings, retrieval-augmented generation.
- The four-week deep-learning block taught by ÚTIA (Institute of Information Theory and Automation, Czech Academy of Sciences): PyTorch, image classification, detection and segmentation, transfer learning.
- A term project in teams of two on a classification dataset of your choice, with an opponent review and a mandatory presentation, where each student answers a verification question on the project — see the term project.
How they differ¶
The only difference in the teaching is the lecture: 4IZ566 / 4IZ565 have a lecture every week, 4IZ582 / 4IZ583 every other week (2 versus 1 lecture hours a week in the syllabi). In the term project, 4IZ582 / 4IZ583 (Programming for AI and data science) must include an AI component — deep learning on images, LLM-generated features, embeddings, or a RAG component; for 4IZ566 / 4IZ565 the same routes are an optional extension.
Assessment in one line¶
Term project 25 % (20 project + 5 opponent review) · presentation and defence 5 % · mid-term test 15 % · final test 30 % · home assignments 15 % (10 DataCamp + 5 nbgrader) · activity 10 %. Grade boundaries 90 / 75 / 60 %. Details on the assessment page.