Daniel K.
Algorithms and Data Structures Specialist
Algorithms specialist with an MSc in Computer Science and 5 years as a competitive-programming coach. Handles dynamic programming, graphs, and Big-O analysis.
Credentials and experience
- MSc in Computer Science
- 5 years coaching competitive programming
- Handles dynamic programming, graph algorithms, Big-O analysis, and LeetCode-style problems
About Daniel K.
Daniel K. is the algorithms and data structures specialist at GeeksProgramming. He coaches students through the algorithms courses that sink GPAs: dynamic programming, graph traversal, and complexity analysis. His solutions show the reasoning path a professor expects to see, and his practice questions mirror the ones TAs actually ask.
Education and background
Daniel holds an MSc in Computer Science and spent 5 years coaching competitive programming. That background shapes how he breaks down hard algorithmic problems for students, from the first idea to a clean, defensible solution.
What Daniel handles
Daniel handles algorithms and data structures coursework: dynamic programming, graph algorithms, Big-O and complexity analysis, and LeetCode-style problems. He focuses on the reasoning path behind each solution, not just a working answer, so a student can reproduce the approach under exam conditions.
Experience behind the work
Across 5 years coaching competitive programming, Daniel has guided students through the algorithms courses that most often sink a GPA. His solutions show the reasoning path a professor expects to see, and his practice questions mirror the ones TAs actually ask, so preparation matches what the course tests.
Articles by Daniel K.
- Efficient Python Algorithms Explained
· Sorting, searching, dynamic programming, greedy methods, and string algorithms in Python, with Big O analysis and working code for each.
- Binary Search Trees in Java: Complete Guide
· Learn how Binary Search Trees work in Java: insertion, deletion, traversal, balancing with AVL and Red-Black Trees, and real-world applications.
- Graph Traversal Algorithms: Java and Python
· Step-by-step DFS and BFS graph traversal in Java and Python, with code walkthroughs, diagrams, and O(N) time complexity analysis.
- Pascal's Triangle in C++ and Java
· Step-by-step implementation of Pascal's Triangle in C++ and Java, covering pattern printing and combinatorics-based value computation with full working code.
- Sorting Algorithms in Java: Step-by-Step
· Bubble Sort and Quick Sort implemented in Java with time complexity analysis, step-by-step code walkthroughs, and working examples you can run immediately.
- SOLID, DRY, KISS, YAGNI: Design Principles
· A working guide to the software design principles every developer cites: SOLID, DRY, KISS, and YAGNI, with code examples in Python, Java, and JavaScript.
- Software Design Patterns Explained
· Design patterns are reusable solutions to common coding problems. This guide covers all 23 Gang of Four patterns: creational, structural, and behavioral.
- Recursion in Java: Examples Explained
· Learn how recursion works in Java with a countdown example and factorial implementation, including base-case rules that prevent StackOverflowError.
- Inheritance vs Composition: OOP Tradeoffs
· Inheritance and composition solve code reuse in different ways. This post breaks down when each fits, why inheritance gets misused, and how interfaces restore polymorphism to composition.
- SOLID Principles of Object-Oriented Design
· The five SOLID principles structure object-oriented code to survive the maintenance phase: single responsibility, open/closed, Liskov substitution, interface segregation, and dependency inversion.
Stuck on a programming assignment?
We deliver working, tested code with a clear walkthrough so you can explain it in class.