leejihoindaeyo/Uwith_DataStructure
Nov 2024 – Nov 2024
1 Month active
Languages Used
CC++MarkdownPython
Technical Skills
AlgorithmAlgorithm ImplementationAlgorithmsBinary SearchBreadth First SearchC++
MinsooKang
PROFILE
Minsookang
Developed a reusable algorithmic toolkit in the leejihoindaeyo/Uwith_DataStructure repository, focusing on tree and graph problems using Python and C++. Delivered solutions for five algorithmic challenges, incorporating optimizations such as unordered_map for efficient card counting and applying code review feedback to improve correctness and performance. Implemented comprehensive tree algorithms, including leaf counting, shortest path calculations, and binary search tree construction from inorder traversals. Created Python scripts for graph traversal, utilizing both depth-first and breadth-first search to analyze connected components and node orderings. Emphasized code quality through iterative review, resulting in maintainable, interview-ready modules for data structure problem solving.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
437
Activity Months1
Your Network
1141 peopleWork History
November 2024
4 Commits • 3 Features
Nov 1, 2024Month: 2024-11 Key features delivered: - Algorithmic Challenge Solutions and Learnings: Implemented five algorithmic problems (card counting, ship loading, substring enumeration, car entry order violations, budget allocation); included optimizations (e.g., 숫자 카드 2 with unordered_map) and learnings from code reviews on erase and unique in 문자열 관련 문제. Commits: b95fff08dd7dde169764edb8fd988da2f12c033e, 42701d5c0b3d4f9710e65228c65f883b4015a8b0. - Tree-Related Algorithms in Python: Implemented leaf counting, shortest path between two nodes, generating a specific tree structure, various tree traversals (preorder, inorder, postorder), and BST construction from an inorder traversal. Commit: 73a12b2cf46faa3d58e2765f00514fdb7d12b56f. - Graph Traversal Scripts (DFS/BFS) in Python: Python scripts for graph traversal; 11724.py implements DFS to count connected components; 1260.py provides DFS and BFS to traverse and print nodes from a starting node. Commit: 2ad178a564494c5581a65fad2ed1e123c6725fd6. Major bugs fixed: - Addressed issues surfaced during code reviews (4주차) and related fixes; performance and correctness tweaks across the three feature areas, including optimizations and traversal correctness. Overall impact and accomplishments: - Built a reusable Python-centered algorithmic toolkit across trees, graphs, and general problem solving, enabling faster solution iteration and code reuse for interviews and performance-critical tasks. - Strengthened problem-solving skills with concrete improvements evidenced by targeted commits and review learnings. Technologies/skills demonstrated: - Python, DFS/BFS, tree traversals, BST construction, unordered_map optimization, code reviews, and collaboration.
4 Commits • 3 Features
Nov 1, 2024Month: 2024-11 Key features delivered: - Algorithmic Challenge Solutions and Learnings: Implemented five algorithmic problems (card counting, ship loading, substring enumeration, car entry order violations, budget allocation); included optimizations (e.g., 숫자 카드 2 with unordered_map) and learnings from code reviews on erase and unique in 문자열 관련 문제. Commits: b95fff08dd7dde169764edb8fd988da2f12c033e, 42701d5c0b3d4f9710e65228c65f883b4015a8b0. - Tree-Related Algorithms in Python: Implemented leaf counting, shortest path between two nodes, generating a specific tree structure, various tree traversals (preorder, inorder, postorder), and BST construction from an inorder traversal. Commit: 73a12b2cf46faa3d58e2765f00514fdb7d12b56f. - Graph Traversal Scripts (DFS/BFS) in Python: Python scripts for graph traversal; 11724.py implements DFS to count connected components; 1260.py provides DFS and BFS to traverse and print nodes from a starting node. Commit: 2ad178a564494c5581a65fad2ed1e123c6725fd6. Major bugs fixed: - Addressed issues surfaced during code reviews (4주차) and related fixes; performance and correctness tweaks across the three feature areas, including optimizations and traversal correctness. Overall impact and accomplishments: - Built a reusable Python-centered algorithmic toolkit across trees, graphs, and general problem solving, enabling faster solution iteration and code reuse for interviews and performance-critical tasks. - Strengthened problem-solving skills with concrete improvements evidenced by targeted commits and review learnings. Technologies/skills demonstrated: - Python, DFS/BFS, tree traversals, BST construction, unordered_map optimization, code reviews, and collaboration.
November 2024
Activity
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Quality Metrics
Correctness82.6%
Maintainability75.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++MarkdownPython
Technical Skills
AlgorithmAlgorithm ImplementationAlgorithmsBinary SearchBreadth First SearchC++Data StructuresDepth First SearchDepth-First SearchGraph AlgorithmsGraph TraversalHash MapsSortingString ManipulationTree Traversal
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