Course objectives:
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The aim of the course is to introduce students to various tools for data processing and learn the basics of programming.
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Requirements on student
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Credit - min. 50% points from semestral work
Deadline for getting credit is 30th of June of current academic year at 2 P.M.
Exam - combined min. 50% points
Due to the continuous updating of the course, in order to obtain the credit for repeated registration of the course (see SZŘ Art. 24 par. 3), the consent of the guarantor of the course is necessary.
Notice:
The dates and form of verification of compliance with the requirements may be adjusted with regard to the measures announced in connection with the development of the epidemiological situation in the Czech Republic.
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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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Activities
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Fields of study
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Guarantors and lecturers
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Guarantors:
Ing. Martin Dostal, Ph.D. ,
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Lecturer:
Ing. Martin Dostal, Ph.D. (100%),
Doc. Ing. Dalibor Fiala, Ph.D. (100%),
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Tutorial lecturer:
Ing. Martin Dostal, Ph.D. (100%),
Doc. Ing. Dalibor Fiala, Ph.D. (100%),
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Literature
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Basic:
Hans Petter Langtangen. Python Scripting for Computational Science. 2009.
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Recommended:
Brian Kokensparger. Guide to Programming for the Digital Humanities: Lessons for Introductory Python. 2018.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Graduate study programme term essay (40-50)
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40
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Contact hours
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52
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Preparation for an examination (30-60)
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40
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Total
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132
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
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 - students are expected to possess the following skills before the course commences to finish it successfully: |
advanced pc operation |
is able to work with MS Excel spreadsheet on advanced level |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
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 - skills resulting from the course: |
practical ability to analyze data and draw conclusions |
the student is able to preprocess, analyze and visualize text input data |
Competences - competences resulting from the course: |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Individual presentation at a seminar |
Skills - skills achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Individual presentation at a seminar |
Competences - competence achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Lecture with visual aids |
Interactive lecture |
Skills - the following training methods are used to achieve the required skills: |
Lecture with visual aids |
Practicum |
Skills demonstration |
Competences - the following training methods are used to achieve the required competences: |
Lecture |
Lecture with visual aids |
Practicum |
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