Course detail

Python Programming – Data Science

FSI-VPDAcad. year: 2026/2027

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

Assignment to study programme types

Master's

Mode of study

Not applicable.

Guarantor

Ing. Jiří Kovář, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

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

Prerequisites and corequisites

Not applicable.

Basic literature

BERKA, Petr, 2003. Dobývání znalostí z databází. Praha: Academia. ISBN 80-200-1062-9. (CS)
VANDERPLAS, Jacob T., [2017]. Python data science handbook: essential tools for working with data. Beijing: O'Reilly. ISBN 978-1-4919-1205-8. (EN)
VANDERPLAS, Jacob T., [2017]. Python data science handbook: essential tools for working with data. Beijing: O'Reilly. ISBN 978-1-4919-1205-8. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-AIŘ-P Master's 1 year of study, summer semester, compulsory
  • Programme N-MAI-P Master's 2 year of study, summer semester, elective

  • Programme C-AKR-P Lifelong learning

    specialization CLS , 1 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Jiří Kovář, Ph.D.

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

Teacher / Lecturer

Ing. Jiří Kovář, Ph.D.

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