Course detail
Python Programming – Data Science
FSI-VPD Acad. year: 2026/2027 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
Supervisor
Department
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
VANDERPLAS, J., Python Data Science Handbook: Essential Tools for Working with Data, 978-1098121228, 2023
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
1. Introduction to the subject and the Python ecosystem
2. Principles of programming in Python – review and systematization
3. Data structures I – theory and application
4. Data structures II – functions, modules, OOP basics
5. Working with data – formats and principles
6. Python for data analytics – libraries and ecosystem
7. Data sources I – structured and open data
8. Data sources II – unstructured and streamed data
9. Data streams and real-time processing
10. Python and AI I – machine learning basics
11. Python and AI II – more advanced approaches
12. Python solution integration I – applications and services
13. Python solution integration II – automation and DevOps
Computer-assisted exercise
26 hours, compulsory
Syllabus
1. Introduction to the environment.
2. Python basics – review.
3. – 4. Data structures in Python, functions, etc.
5. Working with CSV, JSON, and other file types.
6. Pandas, NumPy, Seaborn, Plotly, Matplotlib
7. and 8. Working with data sources
9. Data processing in the field of data streams
10. and 11. Python and AI/ML
12. and 13. Integration of Python solutions in real applications – project