Course detail
Python Programming – Data Science
FSI-VPD Acad. year: 2021/2022 Summer semester
Students will use the Python programming language and its libraries to solve problems in the field of Data Science.
Supervisor
Department
Learning outcomes of the course unit
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.
Prerequisites
Fundamental level of programming in course VP0 (Python programming).
Planned learning activities and teaching methods
Programming using examples from the field of Data Science.
Assesment methods and criteria linked to learning outcomes
The active participation and mastering the assigned task.
Language of instruction
Czech
Aims
Understand the use of Python and its libraries (pandas, numpy, matplotlib, etc.) for Data Science. Advanced Python programming.
Specification of controlled education, way of implementation and compensation for absences
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.
The study programmes with the given course
Programme N-AIŘ-P: Applied Computer Science and Control, Master's
branch ---: no specialisation, 4 credits, compulsory-optional
Type of course unit
Lecture
13 hours, optionally
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.
Computer-assisted exercise
26 hours, compulsory
Teacher / Lecturer
Syllabus
The project form reflects the content of the lectures (4 projects with defense, checkpoints).