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Lecturer(s)
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Symonová Radka, doc. Mgr. Ph.D.
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Course content
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1. Linux - basic platform for data analysis and bioinformatics 2. Jupyter Notebook and Jupyterhub - both documentation and programming in one 3. Itertools - library for combinatorics 4. Numpy - tool for numeric mathematics and multidimensional sets 5. Scipy - algorithms for interpolations and integrations 6. Pandas - tool for data analysis and data visualization 7. Matplotlib and Seaborn - tools for graph creation 8. Parallel Python in a nutshell - optimizing of calculations 9. Basics of Python object programming Work with data gained from real environment of the University or firms. Applying of the taught libraries to data processing.
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Learning activities and teaching methods
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- Preparation for classes
- 50 hours per semester
- Preparation for exam
- 25 hours per semester
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Learning outcomes
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The goal of this course is to learn using large possibilities of Python libraries, mostly focused on numeric and scientific calculations, big data, basic statistical analysis of biological data, data display in form of various graphs. The libraries which will be taught - Itertools - Numpy - Scipy - Matplotlib - Seaborn - Pandas Work with libraries of Applied Python will be focused on gaining of practical experience, thus the processed data will be taken mostly from practice, based on requirements of particular teams of the University. The Seminar Project of student teams will be always the inevitable part of the course. To gain credits, particular student teams will have to advocate their Seminar Projects as the part of the exam.
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Prerequisites
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UAI 735I/UAI 673 Python Basics/Python základy
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Assessment methods and criteria
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unspecified
Seminar work comprising own analysis and detailed documentation.
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Recommended literature
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Hands-On Data Analysis with Pandas - Stefanie Molin (2021).
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Matplotlib 3.0 Cookbook - Benjamin Walter Keller (2018).
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Python Data Analysis - Third Edition - Ivan Idris | Armando Fandango (2017).
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