UngSangYoon/Algorithm_Study_FISA
Jan 2025 – Mar 2025
3 Months active
Languages Used
Python
Technical Skills
AlgorithmAlgorithm ImplementationAlgorithmic Problem SolvingConditional LogicDynamic ProgrammingMathematical Algorithms
kimjeongsoo
PROFILE
Kimjeongsoo
Over a three-month period, contributed to the UngSangYoon/Algorithm_Study_FISA repository by developing eight feature-rich utilities focused on algorithmic problem solving, dynamic programming, and greedy algorithms. Delivered reusable Python modules for number theory, combinatorics, sorting, and ranking, emphasizing modular architecture and clear documentation. Implemented solutions for optimization problems such as change-making, sequence analysis, and recursive pattern generation, using techniques like memoization and the Sieve of Eratosthenes. Prioritized maintainability and onboarding readiness through disciplined commit practices and well-structured code. The work accelerated prototyping and reduced manual effort, providing a robust foundation for algorithm practice and downstream project integration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
8
Lines of code
1,440
Activity Months3
Your Network
1152 peopleShared Repositories
21
Work History
March 2025
3 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03: Delivered substantial feature work in greedy and dynamic programming problem solutions across the UngSangYoon/Algorithm_Study_FISA repo. Focused on implementing scalable algorithms, with commits tied to weekly milestones. This period prioritized delivering business-value through robust algorithm templates and reusable patterns, enabling faster problem-solving across optimization and DP problems.
3 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03: Delivered substantial feature work in greedy and dynamic programming problem solutions across the UngSangYoon/Algorithm_Study_FISA repo. Focused on implementing scalable algorithms, with commits tied to weekly milestones. This period prioritized delivering business-value through robust algorithm templates and reusable patterns, enabling faster problem-solving across optimization and DP problems.
March 2025
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025 (UngSangYoon/Algorithm_Study_FISA): Delivered two core features — Algorithmic Problem Solving Library and Sorting and Ranking Utilities — enabling reusable algorithm implementations and streamlined ranking pipelines (SportClimbingCombined and 2D coordinate sorting). The work was executed through focused commits across weeks (three commits for the library, one for sorting utilities). No major bugs fixed within the dataset; the focus was feature delivery, code organization, and documentation. Business impact: accelerates prototyping, reduces duplicated effort, and improves reliability of algorithmic components for downstream projects. Technical achievements: solid algorithm design, modular architecture, and clear, well-documented commit history.
February 2025
4 Commits • 2 Features
Feb 1, 2025February 2025 (UngSangYoon/Algorithm_Study_FISA): Delivered two core features — Algorithmic Problem Solving Library and Sorting and Ranking Utilities — enabling reusable algorithm implementations and streamlined ranking pipelines (SportClimbingCombined and 2D coordinate sorting). The work was executed through focused commits across weeks (three commits for the library, one for sorting utilities). No major bugs fixed within the dataset; the focus was feature delivery, code organization, and documentation. Business impact: accelerates prototyping, reduces duplicated effort, and improves reliability of algorithmic components for downstream projects. Technical achievements: solid algorithm design, modular architecture, and clear, well-documented commit history.
January 2025
6 Commits • 4 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for UngSangYoon/Algorithm_Study_FISA. Delivered four feature-focused utilities that enhance automation, math tooling, and algorithm practice. No major bugs reported this month. Overall impact includes reduced manual effort in prize calculation, faster access to practical number-theory utilities, and a stronger foundation for recursive/DP learning patterns. Technologies demonstrated: Python scripting, Sieve of Eratosthenes, prime counting, perfect squares, memoization, Cantor set approximation, and recursive pattern generation.
6 Commits • 4 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for UngSangYoon/Algorithm_Study_FISA. Delivered four feature-focused utilities that enhance automation, math tooling, and algorithm practice. No major bugs reported this month. Overall impact includes reduced manual effort in prize calculation, faster access to practical number-theory utilities, and a stronger foundation for recursive/DP learning patterns. Technologies demonstrated: Python scripting, Sieve of Eratosthenes, prime counting, perfect squares, memoization, Cantor set approximation, and recursive pattern generation.
January 2025
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Quality Metrics
Correctness87.0%
Maintainability80.0%
Architecture75.4%
Performance82.4%
AI Usage21.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
AlgorithmAlgorithm ImplementationAlgorithmic Problem SolvingBrute ForceCombinationsCombinatoricsConditional LogicDynamic ProgrammingGreedy AlgorithmGreedy AlgorithmsMathematical AlgorithmsNumber TheoryPermutationsProblem SolvingPython
Repositories Contributed To
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