|
Lecturer(s)
|
-
Čengery Miloslav, Ing. Ph.D.
-
Šorejs Pavel, Ing. Ph.D.
|
|
Course content
|
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
|
|
Learning activities and teaching methods
|
- Undergraduate study programme term essay (20-40)
- 40 hours per semester
- Preparation for an examination (30-60)
- 40 hours per semester
- Contact hours
- 52 hours per semester
|
| prerequisite |
|---|
| Knowledge |
|---|
| 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 |
|---|
| advanced pc operation |
| is able to work with MS Excel spreadsheet on advanced level |
| Competences |
|---|
| N/A |
| N/A |
| learning outcomes |
|---|
| Knowledge |
|---|
| When passing the course student will be able to prepare, analyze and process different kinds of data. |
| Basic knowledge of programming in Python. |
| Skills |
|---|
| 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 |
|---|
| N/A |
| teaching methods |
|---|
| Knowledge |
|---|
| Lecture |
| Lecture with visual aids |
| Interactive lecture |
| Students will gain professional knowledge especially from lectures with demonstration, discussion and activation of students. |
| Skills |
|---|
| Lecture with visual aids |
| Practicum |
| Skills demonstration |
| Competences |
|---|
| Lecture |
| Lecture with visual aids |
| Practicum |
| assessment methods |
|---|
| Knowledge |
|---|
| Combined exam |
| Seminar work |
| Individual presentation at a seminar |
| Skills |
|---|
| Combined exam |
| Seminar work |
| Individual presentation at a seminar |
| Competences |
|---|
| Combined exam |
| Seminar work |
|
Recommended literature
|
-
Brian Kokensparger. Guide to Programming for the Digital Humanities: Lessons for Introductory Python. 2018.
-
Hans Petter Langtangen. Python Scripting for Computational Science. 2009.
|