Python Programming 420-IS1-1PJP
Course profile General academic
Form of study Full-time
Course language English
Course type obligatory courses
Field of science: natural sciences,
Discipline of science: computer science
Year/semester of study I/2
Prerequisites (sequential system of courses and exams):
Introduction to Structural Programming,
Number of course hours with forms of teaching
Lecture: 15 Exercise classes: 30
Teaching methods: lecture, tasks implemented in the laboratory classes,
Individual consultation with the teacher
Form of assessment: credit with a grade
ECTS: 4
Class attendance:
- lecture 15h
- exercise classes 30h
Course preparation:
- lecture 5h
- exercise classes 10h
Literature study: 5h
Reports, homework 10h
Preparation for tests 10h
Preparation for the exam 10h
Exam duration 1h
Individual consultation with the teacher 5h
Student workload:
- Direct interaction with the teacher: 50h, 2ECTS
- Not direct interaction with the teacher: 50h, 2ECTS
Requirements
Course coordinators
Type of course
Mode
General: (in Polish) w sali | Term 2022: (in Polish) zdalnie Blended learning (in Polish) w sali |
Learning outcomes
1.The student knows the essential language constructions of Python and selected Python packages: KA6_WG3
2. The student knows the main programming paradigms (imperative, procedural, functional or object-oriented) in Python KA6_WG4
3. The student knows the selected numeric algorithms KA6_WG6, K_WG7
4. The student designs, and implements Python programs (intermediate level) using different programming paradigms. KA6_UW6, KA6_UW7, KA6_UW8, , KA6_UW15, KA6_UK3
5. The student is able to apply the selected numeric algorithms KA6_UW9, KA6_UW10,
6. The student is able to use the computer-science terminology in English language. KA6_UK1
7. The student see the advantages and disadvantages of Python, in particular, to solve the problems which describe the natural phenomena. KA6_UU1
8. The student is ready to carefully prioritize and sequence its activities KA6_KK1
Assessment criteria
Form of assessment: credit with a grade
Bibliography
1. Python dla każdego : podstawy programowania, Michael Dawson, Helion, 2014
2. Python for Everybody. Exploring Data in Python 3. Charles R. Severance Tłumaczenie: Andrzej Wójtowicz 2022 wyd.3 https://py4e.pl/translations/PL/py4e-pl-print-latest.pdf
3. Python data analytics : data analysis and science using Pandas, Matplotlib and the Python programming language, Fabio Nelli, Apress, 2015
4. A Beginners Guide to Python 3 Programming, John Hunt, Springer 2019
5. The Absolute Beginner's Guide to Python Programming A Step-by-Step Guide with Examples and Lab Exercises. Kevin Wilson Apress 2022
Supplementary reading:
1. Python : podstawy nauki o danych, Alberto Boschetti, Luca Massaron, Gliwice : Wydawnictwo Helion, 2017
2. Uczymy programować się w Pythonie. Otwarty podręcznik programowania
Jerzy Wawro 2017 https://elearning.otwartaedukacja.pl/pluginfile.php/218/mod_resource/content/5/pyprog.pdf
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: