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Lecturer(s)
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Šorejs Pavel, Ing. Ph.D.
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Čengery Miloslav, Ing. Ph.D.
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Course content
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1. Development environment (IDE, text editor, plugins). 2. Numerical data processing - data structures, mathematical calculations and statistical functions. 3. Repetition and deepening of knowledge - management structures, debugging. 4. Techniques of storing information - during the run and after the end of the program. 5. Code encapsulation - functions, procedures, objects. 6.-7. Software development process, problem decomposition. Program validation, testing. 8.-9. Processing of text information and export to formats for further processing (XML, JSON, CSV). 10.-11. Ways of data visualization - online and offline techniques. Data interpretation. 12.-13. Calling external applications, web services interface (API, REST). Code execution environment.
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Learning activities and teaching methods
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- Graduate study programme term essay (40-50)
- 40 hours per semester
- Preparation for an examination (30-60)
- 40 hours per semester
- Contact hours
- 52 hours per semester
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| prerequisite |
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| Knowledge |
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| Explain the basic concepts of statistics and mathematics at the secondary school level. |
| The student has basic knowledge of computer operation. |
| The student knows the basic formats for storing textual information. |
| Skills |
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| advanced pc operation |
| is able to work with MS Excel spreadsheet on advanced level |
| Competences |
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| N/A |
| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| When passing the course student will be able to prepare, analyze and process different kinds of data. |
| Advanced knowledge of programming in Python in the area of text data processing and visualization. |
| Skills |
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| practical ability to analyze data and draw conclusions |
| the student is able to preprocess, analyze and visualize text input data |
| Competences |
|---|
| N/A |
| teaching methods |
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| Knowledge |
|---|
| Lecture |
| Lecture with visual aids |
| Interactive lecture |
| Skills |
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| Lecture with visual aids |
| Practicum |
| Skills demonstration |
| Competences |
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| Lecture |
| Lecture with visual aids |
| Practicum |
| assessment methods |
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| Knowledge |
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| Combined exam |
| Seminar work |
| Individual presentation at a seminar |
| Skills |
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| Combined exam |
| Seminar work |
| Individual presentation at a seminar |
| Competences |
|---|
| Combined exam |
| Seminar work |
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Recommended literature
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Brian Kokensparger. Guide to Programming for the Digital Humanities: Lessons for Introductory Python. 2018.
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Hans Petter Langtangen. Python Scripting for Computational Science. 2009.
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