Palacký University Olomouc
Study programmes & Course catalogue
for academic year 2026/2027
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Course: AI for Data Science

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Course title AI for Data Science
Course code KMA/AIDV
Organizational form of instruction Seminar
Level of course unspecified
Year of study not specified
Semester Winter
Number of ECTS credits 2
Language of instruction Czech
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Trnečková Markéta, Mgr. Ph.D.
Course content
1. AI Tools for Data Science Current AI tools for data analysis, research, coding, and agentic workflows. Effective task formulation, iterative use, and result verification. 2. AI for Data Analysis Data exploration, cleaning, visualization, and exploratory analysis. AI-assisted workflow design and detection of errors and inconsistencies. 3. AI in Statistical Analysis Selection and evaluation of statistical methods, assumption checking, and interpretation of results. Risks of bias, data leakage, multiple testing, and incorrect inference. 4. AI-Assisted Programming Code generation, explanation, debugging, refactoring, documentation, and testing. Verification of AI-generated code and implementation of data and mathematical algorithms. 5. Coding Agents and Software Projects Use of coding agents for codebase navigation, change planning, version control, testing, and review of agent-generated modifications. 6. AI for Scientific Literature Literature search, analysis of scientific publications, and comparison of methods and results. Verification of citations and AI-generated claims. 7. AI and Reproducible Research Reproduction of published results using available data and code. Identification of missing information, methodological issues, and sources of discrepancies. 8. AI for Mathematical Problem Solving AI-assisted proof strategies, auxiliary results, and counterexamples. Verification and critical assessment of mathematical arguments. 9. AI and Experimental Mathematics Generation of mathematical data, pattern detection, conjecture formulation, and computational testing. From experiment to proof. 10. AI for Algorithmic and Mathematical Discovery Generation, evaluation, and iterative improvement of candidate algorithms and mathematical constructions. Selected case studies. 11. AI in Scientific Research AI-assisted hypothesis formulation, experimental design, data analysis, and workflow automation. Human-AI collaboration in scientific discovery. 12. Responsible Use of AI Reproducibility, academic integrity, data protection, copyright, and responsibility for AI-assisted outputs in education and research.

Learning activities and teaching methods
Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration, Group work
Learning outcomes
The aim of the course is to teach students how to use contemporary AI tools effectively and critically when solving tasks typical of data science, mathematics, and scientific research.

Prerequisites
unspecified

Assessment methods and criteria
Student performance

Recommended literature
  • Další odborná literatura, aktuální vědecké články a metodické materiály budou průběžně doplňovány v průběhu semestru s ohledem na rychlý vývoj v oblasti umělé inteligence.
  • Bishop, Ch., Bishop, H. (2024). Deep Learning: Foundations and Concepts.
  • Raschka, S. (2024). Build a Large Language Model (From Scratch).


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Palacký University Olomouc, date of update: 06.10.2026 23:53. Data created for academic year 2026/2027