Course objectives:
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The aim of the course is to introduce students to various software tools for data processing.
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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 January of current academic year at 2 P.M.
Exam - combined min. 50% points
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Content
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1. Basic concepts of computers and programming; programs and programming languages; conventions and comments; data types
2. Problem analysis, algorithmization
3. Variables, assignments, operators, mathematical calculations
4. Suggestion of problem solving, verification of program correctness
5. Control structures (conditional branching, cycle)
6. Testing and troubleshooting
7. Reuse of code - functions, procedures
8. Ways of storing information, fields, lists
9. Processing of text information
10. Work with files
11. Use of external libraries and modules
12. Possibilities of data processing and visualization
13. Overview of data processing formats - eg XML, CSV and JSON
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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%),
Ing. Michal Nykl, Ph.D. (100%),
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Tutorial lecturer:
Ing. Martin Dostal, Ph.D. (100%),
Ing. Michal Nykl, 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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Undergraduate study programme term essay (20-40)
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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 |
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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. |
Basic knowledge of programming in Python. |
Skills - skills resulting from the course: |
practical ability to analyze data and draw conclusions |
student is able to preprocess and analyze text input data |
student is able to verify the hypothesis using statistical data analysis |
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 |
Students will gain professional knowledge especially from lectures with demonstration, discussion and activation of students. |
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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