Course detail

Python Programming – Data Science

FSI-VPD Acad. year: 2025/2026 Summer semester

Students will use the Python programming language and its libraries to solve problems in Data Science.
Students will be introduced to the ecosystem of applications and development tools in Python for various Data Science tasks.

Supervisor

Ing. Jiří Kovář, Ph.D.

Department

Institute of Automation and Computer Science

Learning outcomes of the course unit

Prerequisites

Planned learning activities and teaching methods

Assesment methods and criteria linked to learning outcomes

Language of instruction

Czech

Aims

Specification of controlled education, way of implementation and compensation for absences

The study programmes with the given course

Programme N-AIŘ-P: Applied Computer Science and Control, Master's
branch ---: no specialisation, 4 credits, compulsory

Programme N-MAI-P: Mathematical Engineering, Master's
branch ---: no specialisation, 4 credits, elective

Type of course unit

 

Lecture

26 hours, optionally

Syllabus

P1: Overview of basic machine learning methods and applied statistics.
P2: Advanced machine learning methods. Combination of learning algorithms. Learning in multirelational data. Mining in graphs and sequences.
P3: Big data analytics. Machine learning theory Bias-variation tradeoff. Learning models. Data visualization.
P4: Search for frequent patterns and association rules: Apriori algorithm; alternatives; common patterns in multirelational data. Detection of remote points.
P5: Knowledge mining from selected data types: text mining, mining in temporal and spatio-temporal data, web mining, biological sciences and bioinformatics.

Computer-assisted exercise

26 hours, compulsory

Syllabus

1. Environment definition.
2.-12. The project form reflects the content of the lectures (4 projects with defence, checkpoints).
13. Presentation of projects, repetition, consultation.