Algorithms and Data Structures

Bachelor 2023/2024

Type: Compulsory course

Area of studies: Business Informatics

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 1 year, 2 module

Mode of studies: offline

Open to: students of one campus

Instructors: Maria Gordenko

Language: English

ECTS credits: 4

Contact hours: 48

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Abstract

The course is dedicated to the basics of design and analysis of algorithms. It also involves learning advanced algorithms, data structures and basics of the automata theory. The lectures and practical classes are closely inter-related. The lectures are primarily intended to introduce new topics, whereas the practical classes are intended for solving specific problems by coding programs in C++ or Python. Successful completion of “Introduction to Programming” course is the sole prerequisite for being enrolled in this course.

Learning Objectives

Изучить базовые возможности языка программирования Python и разобраться с основными понятиями алгоритмической сложности для оценки эффективности алгоритмов.
Освоить принципы выбора подходящих структур данных для реализации различных алгоритмов, обеспечивая оптимальную производительность и эффективность
Изучить алгоритмы бинарного поиска и сортировки, а также научиться применять их на практике для решения задач различных уровней сложности
Развить навыки оптимального использования системных и программных ресурсов при разработке на Python, включая управление памятью и обработку данных.
Понять и реализовать основные алгоритмы обработки массивов данных, включая вычисление префиксных сумм для повышения эффективности решения комплексных задач.
Ознакомиться с методом двух указателей и жадными алгоритмами, научиться применять их для оптимального решения задач оптимизации и поиска.
Изучить основные методики и техники оптимизации алгоритмов для повышения их производительности и снижения временных и пространственных затрат.
Понять основные структуры данных деревьев и графов, освоить методы динамического программирования для решения сложных задач, связанных с этими структурами.

Expected Learning Outcomes

Be able to set up python environment under any operating system;
Understand key concepts of programming (procedural, functional paradigms, OOP, data types, algorithm complexity, TDD, etc.) and apply them;
Be able to write scripts in python for the variety of different tasks;
Be able to cover and support code with unit tests, linters, and formatters;
Be able to find and read documentation on python libraries not covered by the course.
Be able to set up python environment
Understand key concepts of programming and apply them
Be able to write scripts in python
Develop and Debug Python Programs
Evaluate and Compare Algorithmic Complexity
Identify and Select Appropriate Data Structures
Implement and Optimize Algorithms Using Selected Data Structures
Implement Search and Sort Algorithms
Analyze and Compare Algorithmic Efficiency
Optimize Memory and Resource Usage
Identify and Resolve Performance Bottlenecks
Implement Array-Based Algorithms and Prefix Sum Techniques
Analyze and Optimize Algorithm Performance
Apply the Two Pointers Technique to Solve Complex Problems
Design and Implement Greedy Algorithms for Optimization Tasks
Implement Advanced Optimization Techniques
Evaluate and Select Appropriate Optimization Strategies
Design and Implement Tree and Graph Algorithms
Apply Dynamic Programming Techniques to Optimize Solutions

Course Contents

1. Introduction to programming and the Python language
2. Python script mode. Conditional execution
3. Loops
4. Bitwise operations. Error handling techniques
5. Functions and modules. Name scopes. Recursive algorithms
6. Python data model. Lists, sets, tuples.
7. Introduction to algorithmic complexity. Sorting algorithms.
8. Strings. Advances techniques of text processing, regular expressions
9. File input-output

Assessment Elements

Final exam
The set of small programming auto-tested tasks.
Homework assignments
Home assignments for the relevant topics discussed in the class in a form of small problems in the web IDE.

Interim Assessment

2023/2024 2nd module
0.3 * Final exam + 0.7 * Homework assignments

Bibliography

Recommended Core Bibliography

Downey, A. (2015). Think Python : How to Think Like a Computer Scientist (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1105725
Eric Matthes. (2019). Python Crash Course, 2nd Edition : A Hands-On, Project-Based Introduction to Programming: Vol. 2nd edition. No Starch Press.
Learning Python : [covers Python 2.5], Lutz, M., 2008
Lutz, M. (2011). Programming Python : Powerful Object-Oriented Programming (Vol. 4th ed). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415412
Sweigart, Al. Automate the boring stuff with Python: practical programming for total beginners. – No Starch Press, 2015. – 505 pp.
Программируем на Python, Доусон, М., 2015

Recommended Additional Bibliography

Baka, B. (2017). Python Data Structures and Algorithms. Birmingham, U.K.: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1528144

Authors

GLADKOVA MARGARITA ANATOLEVNA
YAKOVLEVA NATALIYA VADIMOVNA
GORDENKO MARIIA KONSTANTINOVNA

Course Syllabus