Advanced Programming 2401-CS-22-AP-s2

This is an advanced programming course which aim is to deepen understanding of Python programming language. Students will learn useful programming concepts and popular third-party libraries to tackle scientific problems and other tasks. Course will (1) enable students to write their own script/programs to perform various tasks from statistical analysis, data visualization, data processing, file management and web scraping and (2) increase students understanding of how Python works behind the scenes.
Gained knowledge, skills, and social competences will be demonstrated by students delivering and presenting final projects.

List of topics:
1. Data types, variables and memory
2. Flow control, basic iteration, comprehension
3. Iterators and Generators
4. First-class functions, Closures
5. Decorators
6. Modules and Packages
7. Object oriented programming – introduction
8. Object oriented programming – getters and setters
9. Object oriented programming – polymorphism and magic methods
10. Object oriented programming – single inheritance
11. Python standard library: modules sys, os, random and itertools
12. Data analysis – pandas
13. Data analysis – numpy (1)
14. Data analysis – numpy (2)
15. Data analysis – visualisation in matplotlib

Total student workload

Contact hours with teacher: - participation in lectures - 15 hrs - participation in exercises – 15 hrs - consultations - 10 hrs Self-study hours: - preparation for lectures – 20 hrs - writing projects - 40 hrs - preparation for test / examination- 10 hrs Altogether: 110 (4 ECTS)

Learning outcomes - knowledge

Student W1: has advanced and extensive knowledge of Python features – K_W03, K_W07 W2: knows how to create own Python modules and packages - K_W03 W3: is familiar with Python standard library– K_W03 W4: is well acquainted with Python third-party libraries for data analysis – K_W03, K_W07, K_W05

Learning outcomes - skills

Student U1: is able to use advanced Python features to solve real-world problems and research tasks – K_U01, K_U02, K_U05 U2: is capable of write clean Python code - K_U12 U3: has advanced skills in using and describing object oriented programming in Python – K_U04 U4: can use third-party Python libraries to analyze data - K_U05

Learning outcomes - social competencies

Student K1: understands the significance of working groups in data analysis – K_K02, K_K04 K2: understands own limitations and strong points and is able to add value to team works – K_K03, K_K04 K3: is well prepared to team works in advanced analytical projects - K_K04

Teaching methods

Expository teaching methods: - participatory lecture Exploratory teaching methods: - practical - brainstorming - classic problem-solving

Expository teaching methods

- participatory lecture
- informative (conventional) lecture
- description

Exploratory teaching methods

- practical
- brainstorming
- laboratory
- project work
- presentation of a paper

Type of course

compulsory course

Prerequisites

- basics of Python programming language

Course coordinators

Term 2022/23L:

Michał Komorowski

Term 2024/25Z:

Bogna Bylicka

Term 2023/24Z:

Michał Komorowski

Assessment criteria

Assessment methods:
- tutorial: project – U1, U2, U3
- lecture: written tests composed of theoretical and practical part - W1, W2, W3, W4
- activity – K1, K2, K3, K4

Assessment criteria:
- tutorial:
i) project not meeting agreed-upon assumptions or no project: fail
ii) project meeting agreed-upon assumptions: very good
- lecture: weighted grade: theoretical part (50%) + practical part (50%). Grading (combined both theoretical and practical part):
fail – less than 50%
satisfactory – 51%-60%
satisfactory plus- 61%-70%
good - 71%-80%
good plus- 81%-90%
very good - 91%-100%

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2401-CS-22-AP-s2 in USOSweb