Data Structures and Algorithms WM-FI-S1-E4-ALSD

The subject covers basic issues related to the design, analysis and implementation of algorithms as well as the selection of data structures appropriate to the problem being solved. Methods for assessing the correctness and complexity of algorithms, asymptotic analysis of computational costs and the relationship between the method of data representation and the efficiency of their processing are discussed.

The course introduces abstract data types and basic data structures, including lists, stacks, queues, trees, and graphs, as well as structures for efficiently storing, retrieving, and organizing data. Classical search, sorting, tree and graph processing algorithms are discussed, as well as selected algorithm design techniques, in particular recursion, divide and conquer, greedy algorithms and dynamic programming.

Particular emphasis is placed on the analysis of the relationship between the algorithm, the data structure used and the cost of calculations. Students learn to design and implement solutions to algorithmic problems, analyze their correctness and complexity, and compare alternative ways of solving the same problem. The methods and data structures discussed are also considered in the context of data processing and analysis and the implementation of calculations occurring in scientific applications.

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

physical sciences

(in Polish) E-Learning

(in Polish) E-Learning (pełny kurs)

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

Course coordinators

William Steingartner

(in Polish) Opis nakładu pracy studenta w ECTS

LECTURE class attendance: 30 hours exams outside of class: 3 hours consultations: 4 hours exam preparation: 13 hours Total: 50 hours 1.5 ECTS – direct contact with the instructor 0.5 ECTS – independent work EXERCISES class attendance: 30 hours consultations: 4 hours class preparation and assignment completion: 30 hours preparation for course credit: 11 hours Total: 75 hours 1.4 ECTS – direct contact with the instructor 1.6 ECTS – independent work

Subject level

intermediate

Learning outcome code/codes

LECTURE: FIZ1_W15 EXERCISES: FIZ1_U02, FIZ1_U09, FIZ1_K02 SYMBOL DESCRIPTIONS – Graduate: lecture FIZ1_W15 - knows and understands the fundamentals of how computer programs operate, particularly those used for the simulation and visualization of physical phenomena. exercises FIZ1_U02 - is able to gather, process, and communicate information, FIZ1_U09 - is able to use information technologies to process, analyze, and present data from experiments, simulations, and theoretical models, FIZ1_K02 - is prepared to formulate questions aimed at deepening their understanding of a given topic or identifying missing elements in a line of reasoning.

Type of subject

obligatory

Preliminary Requirements

Ability to program in a chosen programming language. Basic knowledge of discrete mathematics.

Learning outcomes

LECTURE

The student:

W1 – characterizes basic algorithm design techniques, including recursion, the divide-and-conquer method, greedy algorithms, and dynamic programming, and explains fundamental methods for analyzing algorithm correctness and complexity (FIZ1_W15),

W2 – describes basic data structures and the operations performed on them, and explains the criteria for selecting data structures appropriate for a given problem (FIZ1_W15),

W3 – characterizes classic algorithms for searching, sorting, and data processing, and explains the relationship between the chosen algorithm, the data representation method, and computational cost (FIZ1_W15).

EXERCISES

The student:

U1 – selects data representation methods and data structures appropriate for the problem at hand and uses them to process and organize data (FIZ1_U02),

U2 – designs and implements algorithms using learned techniques and data structures, and employs IT tools to analyze their operation and efficiency (FIZ1_U09),

U3 – applies learned algorithms and data structures to solve problems related to data processing, data analysis, and scientific computing (FIZ1_U02, FIZ1_U09),

K1 – formulates questions and identifies gaps in reasoning when analyzing the correctness, complexity, and operation of algorithms (FIZ1_K02).

Assessment criteria

For all learning outcomes, the following assessment criteria are adopted for all forms of verification:
grade 5: fully achieved (no obvious shortcomings),
grade 4.5: achieved almost fully and criteria for awarding a higher grade are not met,
grade 4: largely achieved and the criteria for a higher grade are not met,
grade 3.5: largely achieved with a clear majority of positives and the criteria for granting a higher grade are not met,
grade 3: achieved for most of the cases covered by the verification and criteria for a higher grade are not met,
grade 2: not achieved for most of the cases covered by the verification.

Additional information

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

Description of WM-FI-S1-E4-ALSD in USOSweb