Algorithms and Data Structures 390-ERS-3ASD
Study profile: general academic
Study form: stationary
Subject type: obligatory
Discipline and discipline of science: computer science
Study year, semester: year 3, semester 5
Module: utility computing
Punkty ECTS: 5
student workload:
- participation in lectures (15 hours),
- participation in laboratory (45 hours),
- participation in consultations (15 hours),
- own work (solving algorithmic problems) at home (20 hours),
- preparation for the exam written or project implementation (30 hours).
Quantitative indicators:
student workload associated with activities requiring direct teacher participation - 3.6 ECTS;
student workload related to practical activities - 1.8 ECTS.
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Term 2024:
Study profile: general academic Punkty ECTS: 5 Quantitative indicators: |
Term 2025:
Study profile: general academic Punkty ECTS: 5 Quantitative indicators: |
Prerequisites (description)
Course coordinators
Type of course
Mode
Learning outcomes
1. Student know how to note algorithms in form of: list of steps, block scheme, Nassi-Shneiderman scheme.
2. Can determinate computational complexity of simple algorithms.
3. Can use simple tools (like JavaBlock) supporting algorithms designing.
4. Can apply recursion, understanding its strong and weak sides.
5. Can programing and take use classes (in C++) implementing interfaces of popular data structures like: arrays, stacks, queues, lists, trees and graphs.
6. Can designing and applying different kinds of sorting algorithms.
K_W24 (has basic knowledge of algoithmics and data structures);
K_U29 (can use English sources of knowledge);
k_K05 (can independent find information in literature and the intemet resources, also in foreign languages).
Assessment criteria
Credit for the grade.
Final grade will include results of laboratory and the result of the written examination.
Final grade from laboratory.
The final laboratory grade is based on a computer-based exam. The exam covers selected topics from the laboratory materials and requires students to solve problems by writing or modifying algorithms using C++.
The following grading scale is used for the verification of learning outcomes:
very good: 5 (100%–91%),
good plus: 4.5 (90%–81%),
good: 4 (80%–71%),
satisfactory plus: 3.5 (70%–61%),
satisfactory: 3 (60%–51%),
fail: 2 (50%–0%).
The use of artificial intelligence (AI) tools during classes, lectures, and examinations is not permitted.
Bibliography
1. Rober Lafore – „Data Structures and Algorithms”, (ISBN: 0-672-31633-1).
Supplementary:
1. Adam Drozdek – Data Structures and Algorithms in C++”.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: