FI:PV288 Python - Course Information

PV288 Python

Extent and Intensity

2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).

Teacher(s)

RNDr. Petr Ročkai, Ph.D. (lecturer)

Guaranteed by

RNDr. Petr Ročkai, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics

Timetable

Tue 18:00–19:50 D1

Prerequisites

Basic programming knowledge (as covered in IB111 or at least IB113), not necessarily in Python. Can be combined with PV248.

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 240 student(s).
Current registration and enrolment status: enrolled: 71/240, only registered: 0/240, only registered with preference (fields directly associated with the programme): 0/240

fields of study / plans the course is directly associated with

• Image Processing and Analysis (programme FI, N-VIZ)
• Applied Informatics (programme FI, N-AP)
• Bioinformatics and systems biology (programme FI, N-UIZD)
• Bioinformatics (programme FI, N-AP)
• Computer Games Development (programme FI, N-VIZ_A)
• Computer Graphics and Visualisation (programme FI, N-VIZ_A)
• Computer Networks and Communications (programme FI, N-PSKB_A)
• Cybersecurity Management (programme FI, N-RSSS_A)
• Discrete algorithms and models (programme FI, N-TEI)
• Formal analysis of computer systems (programme FI, N-TEI)
• Graphic design (programme FI, N-VIZ)
• Graphic Design (programme FI, N-VIZ_A)
• Hardware Systems (programme FI, N-PSKB_A)
• Hardware systems (programme FI, N-PSKB)
• Image Processing and Analysis (programme FI, N-VIZ_A)
• Information security (programme FI, N-PSKB)
• Information Security (programme FI, N-PSKB_A)
• Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
• Deployment and operations of software systems (programme FI, N-SWE)
• Design and development of software systems (programme FI, N-SWE)
• Computer graphics and visualisation (programme FI, N-VIZ)
• Computer Networks and Communications (programme FI, N-PSKB)
• Principles of programming languages (programme FI, N-TEI)
• Cybersecurity management (programme FI, N-RSSS)
• Services development management (programme FI, N-RSSS)
• Software Systems Development Management (programme FI, N-RSSS)
• Services Development Management (programme FI, N-RSSS_A)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Software Systems Development Management (programme FI, N-RSSS_A)
• Software Systems (programme FI, N-PSKB_A)
• Software systems (programme FI, N-PSKB)
• Machine learning and artificial intelligence (programme FI, N-UIZD)
• Teacher of Informatics and IT administrator (programme FI, N-UCI)
• Informatics for secondary school teachers (programme FI, N-UCI) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, B-IN)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Computer Games Development (programme FI, N-VIZ)
• Processing and analysis of large-scale data (programme FI, N-UIZD)
• Natural language processing (programme FI, N-UIZD)

Course objectives

Acquaint students with the programming language Python and its internal logic, with focus on general concepts which transcend individual programming languages.

Learning outcomes

The student will gain knowledge (expression evaluation, program execution, memory management, and so on) and to a lesser degree skills (especially reading and understading existing programs) related to programming in Python.

Syllabus

• 1. expressions, variables, functions
  2. objects, classes, types
  3. scope, lexical closure
  4. iterators, generators and coroutines 1
  5. memory management, refcounting, mark & sweep
  6. objects 2: __dict__, method lookup, etc.
  7. iterators, generators and coroutines 2
  8. Python problems, testing, profiling
  9. text, data at rest, predictive parsing
  10. databases, relations vs objects
  11. asynchronous programming, http
  12. mathematics and statistics
  

Teaching methods

Lectures with practical demonstrations.

Assessment methods

A knowledge test (15 minutes, multiple choice, 60 % minimum) in the last week of semester, with a re-sit possible in the exam period. Alternatively by successfully finishing the course PV248 in the same semester.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

• Enrolment Statistics (recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2022/PV288