Course detail
Python Programming – Data Science
FSI-VPD-KAcad. year: 2024/2025
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
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.
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.
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.
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
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.
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
Syllabus
The project form reflects the content of the lectures (4 projects with defense, checkpoints).