Course detail

Python Programming – Data Science

FSI-VPD-KAcad. year: 2023/2024

Students will use the Python programming language and its libraries to solve problems in the field of Data Science.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

prof. Ing. Radomil Matoušek, 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

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-AIŘ-K Master's 1 year of study, summer semester, compulsory-optional

Type of course unit

 

Guided consultation in combined form of studies

13 hod., compulsory

Teacher / Lecturer

prof. Ing. Radomil Matoušek, Ph.D.

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

26 hod., optionally

Teacher / Lecturer

prof. Ing. Radomil Matoušek, Ph.D.

Syllabus

The project form reflects the content of the lectures (4 projects with defense, checkpoints).