Course Syllabus 
                                                          
              Course Title                   ECTS                          Department  
              Competitive Programming        3                            Faculty of Programming and
                                                                          Information Technologies  
              Contact hours per course       Dates                         Type of Course  
              40                             January 11 - 22, 2021         Winter School  
              Lectures                       Practice                      Mode of study 
              10                             30                             Online
              Field                          Language of Instruction       Lecturer(s)  
              Computer Science               English                       Prof. Niyaz Nigmatullin
              
             Course Objectives:  
             This course is designed to improve knowledge of algorithms and programming languages, and 
             provide deep understanding of the problem solving. The course main aims are:    
                •   Introduce the competitive programming.  
                •   Cover in details the algorithms. 
                •   Cover in details the data structure.  
                •   Make students aware of approaches applied at the world competitions. 
              Course Content: 
                •    Computational complexity and linear data structures:
                    An overview of computational complexity (Big O notation) Exploring linear data structures 
                      (array, list, stack, queue): operations, complexity, implementation and examples.
                     
                •    Sorting and search algorithms:
                    An overview of sorting algorithms: insertion sort, quick sort, merge sort. Theoretical limitations 
                      and practical guidelines for sorting. Binary search. Binary heaps and priority queues.
                                              
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                     •    Graph theory:
                         Definition of graphs and examples of graph problems. Various ways of storing graphs in memory.
                         Depth first search and its applications. Dynamic programming.
                                Breadth first search. Eulerian and Hamiltonian paths and tours. Shortest paths.
 
                                                                  •    Final Assessment:
                                                                  Solving a set of problems in limited time just like in a real programming competition.
                 
                Learning Outcomes:  
                After completion of the course students are expected to be able to:   
                     1.  understand and use most common algorithms for competitive programming.
                     2.  analyze data structures for competitive upsolving.
                     3.   solve programming contest tasks presented at ACM ICPC. 
                     4.   think outside the box and meet tough deadlines.
                 Learning Activities and Teaching Methods:  
                 Lectures, practical exercises.  
                  
                 Assessment Methods:  
                      •   Final assessment: Final Examinations. 
                      •   Continuous assessment: practice reports assessments. 
                                                                                                              
                                                                                                            
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