Course detail
Python Programming – Data Science
FSI-VPD-K 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
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-AIŘ-K: Applied Computer Science and Control, Master's, compulsory
Type of course unit
Guided consultation in combined form of studies
17 hours, compulsory
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.
Guided consultation
35 hours, optionally
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.