Python Programming – Data Science (FSI-VPD-K)

Academic year 2021/2022
Supervisor: prof. Ing. Radomil Matoušek, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
Understand the use of Python and its libraries (pandas, numpy, matplotlib, etc.) for Data Science. Advanced Python programming.
Learning outcomes and competences:
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).
Course contents:
Students will use the Python programming language and its libraries to solve problems in the field of Data Science.
Teaching methods and criteria:
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.
Controlled participation in lessons:
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.
Type of course unit:
    Guided consultation in combined form of studies  1 × 13 hrs. compulsory                  
    Guided consultation  1 × 26 hrs. optionally                  
Course curriculum:
    Guided consultation in combined form of studies 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 The project form reflects the content of the lectures (4 projects with defense, checkpoints).
Literature - fundamental:
1. VANDERPLAS, Jacob T., [2017]. Python data science handbook: essential tools for working with data. Beijing: O'Reilly. ISBN 978-1-4919-1205-8.
2. BERKA, Petr, 2003. Dobývání znalostí z databází. Praha: Academia. ISBN 80-200-1062-9.
3. VANDERPLAS, Jacob T., [2017]. Python data science handbook: essential tools for working with data. Beijing: O'Reilly. ISBN 978-1-4919-1205-8.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
N-AIŘ-K combined study --- no specialisation -- GCr 4 Compulsory-optional 2 1 S