Acquiring basic knowledge about algorithms and data structures. Understanding complexities of algorithms and learning numerous algorithms for common problems in software development.

Knowledge of common algorithms and data structures. These algorithms will be implemented and their complexities will be understand in real examples.

Basic of algorithms (definitions, features, algorithm analysis, algorithm description, pseudo-code, basic algorithmic problems, algorithm complexity, asymptotic notations, etc.). Search problem (linear searching, binary search). Sorting problem and sorting algorithms (selection sort, insertion sort, recursion and divide-and-conquer algorithm, merge sort, quicksort, heap structure and heapsort, priority queue, etc.). Linear-time sorting algorithms (counting sort, radix sort, bucket sort). Order statistics (problem description, minimum and maximum, medians, and algorithm). Elementary data structures (stacks and queues, linked lists, operations, implementations, trees, binary trees, BST, AVL, etc.). Hash tables (dictionaries, operations, hashing functions, collisions and linked lists, open addressing, asymptotic algorithm complexity, etc.). Graphs (definition, examples and graph types, directed acyclic graphs, representations of graphs, etc.). Graph algorithms (topological sort, graph search, breadth-first search, depth-first search, etc.). Shortest paths graphs (shortest paths in DAG, Dijkstra’s algorithm, Bellman-Ford algorithm, etc.). Complex problems (P and NP problems, NP-complete problems, NP-hard problems, exponential problems, examples). Dynamic programming (elements, examples, etc.). Parallel algorithms (sequential and parallel execution, Amdahl's law, implementation complexities, examples). Examples of algorithms implemented in cryptography, data compression, string operations, etc.)

Lectures; Auditory and computer practice; Consultations.