TeogopK/Data_Structures_and_Algorithms_FMI
Nov 2024 – Feb 2026
8 Months active
Languages Used
C++MarkdownPythonJupyter NotebookC#gitignore
Technical Skills
Algorithm DesignAlgorithmsBinary TreesC++Data StructuresDepth First Search
Teodor Kostadinov
PROFILE
Teodor Kostadinov
Teodor Kostadinov developed and maintained the TeogopK/Data_Structures_and_Algorithms_FMI repository, delivering over 50 features and numerous bug fixes across eight months. He focused on building a robust educational platform for data structures and algorithms, implementing solutions and documentation in both Python and C++. His work included designing and optimizing algorithms such as graph traversals, minimum spanning trees, and priority queues, while also refactoring code for clarity and maintainability. Teodor emphasized hands-on learning by providing executable examples, test-driven resources, and detailed READMEs, resulting in a scalable, well-organized codebase that supports both student onboarding and advanced problem-solving.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
134Total
Bugs
8
Commits
134
Features
53
Lines of code
43,722
Activity Months8
Your Network
14 peopleWork History
February 2026
2 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) - Summary of contributions for TeogopK/Data_Structures_and_Algorithms_FMI. Delivered two feature-focused initiatives, documented heuristics and ethics considerations, and maintained code quality. No major bug fixes recorded; stability improved through targeted enhancements in algorithmic tooling.
2 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) - Summary of contributions for TeogopK/Data_Structures_and_Algorithms_FMI. Delivered two feature-focused initiatives, documented heuristics and ethics considerations, and maintained code quality. No major bug fixes recorded; stability improved through targeted enhancements in algorithmic tooling.
February 2026
January 2026
18 Commits • 5 Features
Jan 1, 2026January 2026: Delivered core DS&A FMI repository enhancements, expanded hands-on learning resources, and improved documentation for course materials. Key features include: (a) Course Materials Documentation and Readme Formatting with robust link/navigation updates; (b) DAG Topological Sorting and Shortest Paths with new Python snippets; (c) Dijkstra and Bellman-Ford Educational Playground Update; (d) Minimum Spanning Tree Visualization Enhancements; (e) Exam Solutions in Python for Historical Finals. These changes improve accessibility, instructional clarity, and reusability of resources, enabling students to learn and evaluate algorithms effectively. Tech stack and skills demonstrated include Python (data structures/algorithms), refactoring, documentation best practices, and version-controlled collaboration.
January 2026
18 Commits • 5 Features
Jan 1, 2026January 2026: Delivered core DS&A FMI repository enhancements, expanded hands-on learning resources, and improved documentation for course materials. Key features include: (a) Course Materials Documentation and Readme Formatting with robust link/navigation updates; (b) DAG Topological Sorting and Shortest Paths with new Python snippets; (c) Dijkstra and Bellman-Ford Educational Playground Update; (d) Minimum Spanning Tree Visualization Enhancements; (e) Exam Solutions in Python for Historical Finals. These changes improve accessibility, instructional clarity, and reusability of resources, enabling students to learn and evaluate algorithms effectively. Tech stack and skills demonstrated include Python (data structures/algorithms), refactoring, documentation best practices, and version-controlled collaboration.
December 2025
9 Commits • 4 Features
Dec 1, 2025December 2025 monthly summary for TeogopK/Data_Structures_and_Algorithms_FMI: Delivered comprehensive documentation, solutions, and educational resources to strengthen learning and code reliability. Key features delivered include Exam 04 Documentation and Solutions, Exam 05 Documentation and Solutions, Graph and Algorithm Educational Content and Notebooks, and README Enhancements with Algorithm Examples. These efforts produced executable tests and Python/C++ examples that support hands-on practice and faster onboarding. No explicit major bug fixes were reported this month; however, the new tests and examples help reduce regressions and improve maintainability. Technologies and skills demonstrated include Python and C++ solutions, test-driven examples, notebook-based teaching aids, graph and heap algorithms, and data-structure problem solving. Overall impact: improved developer onboarding, richer learning resources, and higher quality problem solutions for algorithm education, which translates to faster skill development and better repository quality.
9 Commits • 4 Features
Dec 1, 2025December 2025 monthly summary for TeogopK/Data_Structures_and_Algorithms_FMI: Delivered comprehensive documentation, solutions, and educational resources to strengthen learning and code reliability. Key features delivered include Exam 04 Documentation and Solutions, Exam 05 Documentation and Solutions, Graph and Algorithm Educational Content and Notebooks, and README Enhancements with Algorithm Examples. These efforts produced executable tests and Python/C++ examples that support hands-on practice and faster onboarding. No explicit major bug fixes were reported this month; however, the new tests and examples help reduce regressions and improve maintainability. Technologies and skills demonstrated include Python and C++ solutions, test-driven examples, notebook-based teaching aids, graph and heap algorithms, and data-structure problem solving. Overall impact: improved developer onboarding, richer learning resources, and higher quality problem solutions for algorithm education, which translates to faster skill development and better repository quality.
December 2025
November 2025
30 Commits • 12 Features
Nov 1, 2025November 2025 highlights the delivery of structured, learner-ready materials and repository improvements that directly support student success and course scalability. Key features delivered include comprehensive Homework and Exam Materials (HW2, HW3; exam02 tasks and solutions; task explanations), Week04 Python codeblocks, Weeks 05–06 materials with solutions and readme updates, and a general readme link. Major bug fixes improved accessibility and correctness: Fix exam links to point to solutions, Cyrillic name metadata corrected, and migration/moving files to the proper folder structure enhanced consistency. The overall impact includes faster onboarding for students, reliable exam preparation resources, and a cleaner, more navigable repository, enabling the team to scale updates and maintain higher-quality materials. Technologies/skills demonstrated include Python, Markdown/docs work, repository organization, link validation, and documentation-driven development, with a strong emphasis on business value and learner outcomes.
November 2025
30 Commits • 12 Features
Nov 1, 2025November 2025 highlights the delivery of structured, learner-ready materials and repository improvements that directly support student success and course scalability. Key features delivered include comprehensive Homework and Exam Materials (HW2, HW3; exam02 tasks and solutions; task explanations), Week04 Python codeblocks, Weeks 05–06 materials with solutions and readme updates, and a general readme link. Major bug fixes improved accessibility and correctness: Fix exam links to point to solutions, Cyrillic name metadata corrected, and migration/moving files to the proper folder structure enhanced consistency. The overall impact includes faster onboarding for students, reliable exam preparation resources, and a cleaner, more navigable repository, enabling the team to scale updates and maintain higher-quality materials. Technologies/skills demonstrated include Python, Markdown/docs work, repository organization, link validation, and documentation-driven development, with a strong emphasis on business value and learner outcomes.
October 2025
28 Commits • 13 Features
Oct 1, 2025October 2025: TeogopK/Data_Structures_and_Algorithms_FMI repository refreshed and expanded to support scalable learning content and multi-language solutions. Delivered a major refactor of the Solutions structure (one folder per task with multi-language solutions), expanded Python seminar materials (Sem01-Sem02) and related HackerRank tasks, and added Week03/Week04 content with Python solutions. Strengthened documentation, fixed navigation issues, and standardized naming conventions to improve maintainability and onboarding. These efforts align with business goals of reusable teaching materials, faster contributor onboarding, and robust exam/assignment support.
28 Commits • 13 Features
Oct 1, 2025October 2025: TeogopK/Data_Structures_and_Algorithms_FMI repository refreshed and expanded to support scalable learning content and multi-language solutions. Delivered a major refactor of the Solutions structure (one folder per task with multi-language solutions), expanded Python seminar materials (Sem01-Sem02) and related HackerRank tasks, and added Week03/Week04 content with Python solutions. Strengthened documentation, fixed navigation issues, and standardized naming conventions to improve maintainability and onboarding. These efforts align with business goals of reusable teaching materials, faster contributor onboarding, and robust exam/assignment support.
October 2025
January 2025
12 Commits • 3 Features
Jan 1, 2025January 2025 (2025-01) focused on delivering key algorithmic features for the Data Structures and Algorithms learning repository, while stabilizing resources through documentation and navigation improvements. The work enhances student outcomes by providing reliable, well-structured material and concrete implementations that illustrate core graph and DSU concepts.
January 2025
12 Commits • 3 Features
Jan 1, 2025January 2025 (2025-01) focused on delivering key algorithmic features for the Data Structures and Algorithms learning repository, while stabilizing resources through documentation and navigation improvements. The work enhances student outcomes by providing reliable, well-structured material and concrete implementations that illustrate core graph and DSU concepts.
December 2024
23 Commits • 10 Features
Dec 1, 2024December 2024: Delivered a comprehensive documentation overhaul and an expanded algorithm/DS solution suite across C++ and Python. Implemented Priority Queue utilities, graph and topological task solutions, areas counting, Dijkstra task solutions, and gym/task play solutions. Fixed critical links in README and playground, and polished headings for improved navigation and onboarding. The work results in a more maintainable, learnable, and production-ready repository with ready-to-use examples.
23 Commits • 10 Features
Dec 1, 2024December 2024: Delivered a comprehensive documentation overhaul and an expanded algorithm/DS solution suite across C++ and Python. Implemented Priority Queue utilities, graph and topological task solutions, areas counting, Dijkstra task solutions, and gym/task play solutions. Fixed critical links in README and playground, and polished headings for improved navigation and onboarding. The work results in a more maintainable, learnable, and production-ready repository with ready-to-use examples.
December 2024
November 2024
12 Commits • 4 Features
Nov 1, 2024Month: 2024-11 | Repository: TeogopK/Data_Structures_and_Algorithms_FMI. Focused on delivering new problem solutions, enhancing documentation, and tightening code quality to improve learning outcomes and maintainability.Highlights include new problem implementations, updated READMEs, and cross-language solutions (C++/Python) to expand robust educational content for data structures and algorithms topics. The work strengthens the repository’s usefulness for seminars and self-paced learning, while maintaining clarity and consistency across tasks.
November 2024
12 Commits • 4 Features
Nov 1, 2024Month: 2024-11 | Repository: TeogopK/Data_Structures_and_Algorithms_FMI. Focused on delivering new problem solutions, enhancing documentation, and tightening code quality to improve learning outcomes and maintainability.Highlights include new problem implementations, updated READMEs, and cross-language solutions (C++/Python) to expand robust educational content for data structures and algorithms topics. The work strengthens the repository’s usefulness for seminars and self-paced learning, while maintaining clarity and consistency across tasks.
Activity
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Quality Metrics
Correctness96.6%
Maintainability93.0%
Architecture92.4%
Performance93.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
C#C++Jupyter NotebookMarkdownPythongitignore
Technical Skills
Algorithm AnalysisAlgorithm DesignAlgorithm ExplanationAlgorithm ImplementationAlgorithm OptimizationAlgorithmsBFSBacktrackingBellman-Ford AlgorithmBinary SearchBinary TreesBreadth-First SearchBreadth-First Search (BFS)C++C++ Programming
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