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.

Language of instruction

Czech

Number of ECTS credits

4

Entry knowledge

Fundamental level of programming in course VP0 (Python programming).

Rules for evaluation and completion of the course

The active participation and mastering the assigned task.
Education runs according to week schedules. Attendance at the seminars is required. The form of compensation of missed seminars is fully in the competence of a tutor.

Aims

Understand the use of Python and its libraries (pandas, numpy, matplotlib, etc.) for Data Science. Advanced Python programming.
Upon successful completion of this course, students will be able to use knowledge in practical areas of Data Science. The main goal of data specialists is to clean and analyze large data.

Study aids

https://www.kaggle.com/

VANDERPLAS, J., Python Data Science Handbook: Essential Tools for Working with Data, 978-1098121228, 2023

https://jupyter.org/

GÉRON, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2022, 978-1098125974

The study programmes with the given course

Programme N-MAI-P: Mathematical Engineering, Master's, elective

Programme N-AIŘ-P: Applied Computer Science and Control, Master's, compulsory

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.