Python Programming – Data Science (FSI-VPD)

Academic year 2025/2026
Supervisor: Ing. Jiří Kovář, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech
Course type: departmental course
Aims of the course unit:
 
Learning outcomes and competences:
 
Prerequisites:
 
Course contents:

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.
Teaching methods and criteria:
 
Assesment methods and criteria linked to learning outcomes:
 
Controlled participation in lessons:
 
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture 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

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.
Literature - fundamental:
1. VANDERPLAS, Jacob T., [2017]. Python data science handbook: essential tools for working with data. Beijing: O'Reilly. ISBN 978-1-4919-1205-8.
2. BERKA, Petr, 2003. Dobývání znalostí z databází. Praha: Academia. ISBN 80-200-1062-9.
3. VANDERPLAS, Jacob T., [2017]. Python data science handbook: essential tools for working with data. Beijing: O'Reilly. ISBN 978-1-4919-1205-8.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
C-AKR-P full-time study CLS -- GCr 4 Elective 1 1 S
N-MAI-P full-time study --- no specialisation -- GCr 4 Elective 2 2 S
N-AIŘ-P full-time study --- no specialisation -- GCr 4 Compulsory 2 1 S