Design and Analysis of Algorithms (DAA)
AKTU B.Tech (BCS503 / KCS503) Complete Tutorial
Course Introduction
Welcome to the Design and Analysis of Algorithms (DAA) tutorial developed specially for AKTU B.Tech students. This course covers the complete AKTU syllabus with detailed explanations, solved examples, illustrations, complexity analysis, interview questions and placement-oriented content. Whether you are preparing for university examinations, coding interviews or GATE, this tutorial will help you understand every topic from basic concepts to advanced algorithm design techniques.
Course Information
| Course Name | Design and Analysis of Algorithms |
|---|---|
| Course Code | BCS503 / KCS503 |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Programme | B.Tech Computer Science & Engineering / Information Technology |
| Semester | Fifth Semester |
| Prepared By | NoidaTut E-Learning Platform |
Detailed AKTU Syllabus
| Unit | Topics | Hours |
|---|---|---|
| I | Introduction: Algorithms, Analysis of Algorithms, Complexity of Algorithms, Growth of Functions, Performance Measurement, Sorting and Order Statistics, Shell Sort, Quick Sort, Merge Sort, Heap Sort, Comparison of Sorting Algorithms and Linear Time Sorting. | 08 |
| II | Advanced Data Structures: Red-Black Trees, AVL Trees, B-Trees, B+ Trees, Binomial Heaps, Fibonacci Heaps, Trie and Skip List. | 08 |
| III |
Divide and Conquer
with examples such as Sorting, Matrix Multiplication, Convex Hull and Searching.
Greedy Methods with examples such as Optimal Reliability Allocation, Knapsack, Minimum Spanning Trees, Prim's and Kruskal's Algorithms, Single Source Shortest Paths, Dijkstra's and Bellman-Ford Algorithms. |
08 |
| IV |
Dynamic Programming
with examples such as Knapsack, All Pair Shortest Paths, Floyd-Warshall Algorithm, Resource Allocation Problem.
Backtracking including Branch and Bound with examples such as Travelling Salesman Problem, Graph Coloring, N-Queen Problem, Hamiltonian Cycle and Sum of Subsets. |
08 |
| V | Selected Topics: Algebraic Computation, Fast Fourier Transform, String Matching, Theory of NP-Completeness, Approximation Algorithms and Randomized Algorithms. | 08 |
Learning Roadmap
The tutorial follows the exact sequence of the AKTU syllabus. Every unit has been divided into multiple chapters with algorithms, solved examples, illustrations, complexity analysis, interview questions and practical applications.
Figure 1 : Complete Roadmap of the DAA Tutorial
Why Learn Design and Analysis of Algorithms?
- Develop efficient problem-solving skills.
- Learn techniques to reduce execution time.
- Prepare for coding interviews.
- Build a strong foundation for competitive programming.
- Understand advanced data structures.
- Prepare for GATE and placement examinations.
- Understand how Google, Microsoft, Amazon and Meta solve computational problems.
Course Outcomes
- Design efficient algorithms.
- Analyse time and space complexity.
- Apply Divide and Conquer techniques.
- Solve optimization problems using Greedy methods.
- Design Dynamic Programming solutions.
- Apply Backtracking and Branch & Bound techniques.
- Understand advanced data structures used in databases and operating systems.
Recommended Text Books
| S.No. | Book |
|---|---|
| 1 | Introduction to Algorithms — Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein. |
| 2 | Fundamentals of Computer Algorithms — E. Horowitz and S. Sahni. |
| 3 | The Design and Analysis of Computer Algorithms — Aho, Hopcroft and Ullman. |
| 4 | Analysis of Algorithms — Jeffrey McConnell. |
| 5 | Foundations of Algorithms — Richard Neapolitan. |
| 6 | Algorithm Design — Jon Kleinberg and Éva Tardos. |