Design and Analysis of Algorithms (DAA) Course
Our Design and Analysis of Algorithms course covers asymptotic notation and recurrences, divide and conquer, greedy algorithms, dynamic programming, backtracking and branch and bound, graph algorithms (shortest paths, spanning trees and flows), string matching and NP-completeness. Lessons are live and one-to-one with a verified subject expert, who explains each concept with worked examples, solves problems from your own module and helps you prepare for exams and coursework. Suitable for computer science and engineering students, following university syllabi in the UK, US, Australia and India (including AKTU, VTU and Anna University style courses).
Reviewed by P. Gaurav, B.Tech in Computer Science and EngineeringLast updated
$14 – $34
per hour
Free demo class · pay only if you continue
What you will learn
Learning outcomes
- Analyze time and space complexity
- Use asymptotic notation
- Solve recurrence relations
- Apply divide-and-conquer techniques
- Design greedy algorithms
- Apply dynamic programming
- Use backtracking and branch-and-bound techniques
- Implement graph algorithms
- Compare algorithmic approaches
- Understand computational complexity
Prerequisites
- Basic programming
- Data structures
- Discrete mathematics
- Basic algorithms
- Mathematical reasoning
Tags
Course structure
Syllabus(6 modules)
- Time complexity
- Space complexity
- Asymptotic notation
- Recurrence equations
- Master theorem
What is Design and Analysis of Algorithms?
In short: this course gives one-to-one online tutoring in design and analysis of algorithms (DAA). A verified tutor explains divide and conquer, greedy methods, dynamic programming, graph algorithms, recurrences and complexity classes, with proofs and worked problems.
Design and Analysis of Algorithms (DAA) is the study of how to design efficient algorithms and prove how fast and correct they are. It covers design paradigms such as divide and conquer, greedy methods and dynamic programming, the mathematics of running-time analysis, and the limits of computation such as NP-completeness. It is a core module in computer science and engineering degrees.
This online course teaches Design and Analysis of Algorithms one to one with a verified tutor who has studied or taught it at university level, at your pace and aligned with your own module.
Related subject: Data structures and algorithms help.
What does the Design and Analysis of Algorithms course cover?
| Topic | Key content |
|---|---|
| Asymptotic analysis | Big-O, Omega and Theta notation, growth of functions |
| Recurrences | Substitution, recursion tree and master theorem |
| Divide and conquer | Merge sort, quicksort, binary search, Strassen's matrix multiplication |
| Greedy algorithms | Activity selection, Huffman coding, fractional knapsack, Kruskal and Prim |
| Dynamic programming | 0/1 knapsack, longest common subsequence, matrix chain multiplication, Floyd–Warshall |
| Graph algorithms | BFS, DFS, Dijkstra, Bellman–Ford, topological sort |
| Backtracking and branch and bound | N-Queens, subset sum, travelling salesman |
| Complexity classes | P, NP, NP-complete and NP-hard, reductions |
Greedy or dynamic programming: how do you decide?
Use a greedy algorithm when a locally best choice can be proved to lead to a global optimum (the greedy-choice property), such as activity selection. Use dynamic programming when a problem has overlapping subproblems and optimal substructure but greedy choices fail, such as 0/1 knapsack.
| Paradigm | When it works | Example |
|---|---|---|
| Divide and conquer | Independent subproblems | Merge sort |
| Greedy | Greedy-choice property holds | Huffman coding |
| Dynamic programming | Overlapping subproblems | Longest common subsequence |
| Backtracking | Search with pruning | N-Queens |
How do you solve recurrences with the master theorem?
For T(n) = aT(n/b) + f(n):
- Identify a, b and f(n).
- Compute n^(log_b a).
- Compare f(n) with n^(log_b a).
- If f(n) is polynomially smaller, T(n) = Θ(n^(log_b a)).
- If they are equal up to log factors, multiply by log n.
- If f(n) is polynomially larger and the regularity condition holds, T(n) = Θ(f(n)).
What does a worked recurrence look like?
Here is a typical master theorem question: solve T(n) = 2T(n/2) + n, the recurrence for merge sort.
| Step | Working |
|---|---|
| Identify a, b and f(n) | a = 2, b = 2, f(n) = n |
| Compute n^(log_b a) | n^(log₂ 2) = n |
| Compare | f(n) = Θ(n), the same order: case 2 |
| Result | T(n) = Θ(n log n) |
Where do students lose marks in Design and Analysis of Algorithms?
Students most often lose marks by applying greedy methods without proving they are correct, misusing the master theorem, giving complexities without derivation and writing dynamic programming solutions without a clear recurrence.
| Mark-loser | What to do instead |
|---|---|
| Greedy without proof | Use an exchange argument or counterexample |
| Master theorem misused | Check its conditions and the case carefully |
| Underived complexities | Show the recurrence or loop count |
| Unclear DP | State subproblems, recurrence and base cases |
| Weak NP-completeness proofs | Show the reduction direction correctly |
How is it assessed, and how can a tutor help?
DAA modules are usually assessed by written exams on designing algorithms, solving recurrences and proving correctness, plus problem sheets and sometimes programming tasks.
Tutors explain concepts, work through practice problems with you and give feedback on your approach. Graded work you submit must be your own, as set out in our academic integrity policy. For deadline-driven guidance, see assignment help.
How the lessons work
Lessons are live and one to one on video with screen sharing and a shared whiteboard. You can follow a full course or book sessions on the topics you find hardest before exams. See also Data Structures, Theory of Computation and Discrete Structures.
Design and analysis of algorithms assignment help: who this course is for, prerequisites and study plan
Design and analysis of algorithms assignment help from a 1-to-1 tutor means working through your own DAA problems, recurrences, greedy proofs and dynamic programming tables, so you understand each method and can solve similar questions alone in the exam.
This course suits second- and third-year computer science students taking a DAA module.
Prerequisites: programming, data structures, discrete mathematics and mathematical reasoning.
Course length: 36 one-hour lessons across 6 modules.
| Stage | Modules | What you can do afterwards |
|---|---|---|
| 1 | Program performance | Use asymptotic notation and solve recurrences with the master theorem |
| 2 | Algorithmic techniques | Apply divide and conquer, greedy, dynamic programming and backtracking |
| 3 | Graph algorithms | Run BFS, DFS, Dijkstra, Prim and Kruskal and analyse them |
| 4 | Infeasibility, parallel and other algorithms | Explain P, NP and NP-completeness and reduce one problem to another |
Where it leads: DAA builds on our Data Structures course and Discrete Mathematics.
Design and Analysis of Algorithms (DAA) Course: frequently asked questions
What is DAA?
Design and Analysis of Algorithms: designing efficient algorithms and analysing their correctness and running time.
What is the master theorem?
A formula for solving divide-and-conquer recurrences of the form T(n) = aT(n/b) + f(n).
What is the difference between greedy and dynamic programming?
Greedy makes the best local choice; dynamic programming combines solutions to overlapping subproblems.
What does NP-complete mean?
A problem in NP to which every NP problem can be reduced in polynomial time; no polynomial-time algorithm is known.
Is DAA hard?
It is mathematical, but practising proofs and recurrences step by step makes it manageable.
Do I need discrete maths for DAA?
Yes. Logic, proofs, sets and graph basics help a lot.
Can you help me prepare for my DAA exam?
Yes. Tutors practise exam-style design, recurrence and proof questions.
Which algorithms are most commonly examined?
Sorting, Dijkstra, minimum spanning trees, knapsack and LCS are frequent.
How much does a DAA tutor cost?
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
Can a tutor do my assignment for me?
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
What is recursion in design and analysis of algorithms?
Recursion is when an algorithm solves a problem by calling itself on smaller instances until it reaches a base case. In DAA, recursive algorithms such as merge sort and binary search are analysed with recurrence relations, which you solve with substitution, recursion trees or the master theorem to find their time complexity.
What is the time complexity of merge sort?
Θ(n log n), from the recurrence T(n) = 2T(n/2) + n.
When does a greedy algorithm work?
When the problem has the greedy-choice property and optimal substructure, which you should prove.
What is hashing in design and analysis of algorithms?
Hashing maps keys to positions in a table using a hash function, so search, insert and delete take constant time on average. In DAA you analyse collisions and their handling, such as chaining and open addressing, and the effect of the load factor. Dynamic programming often uses a hash table to store solved subproblems.
What is the difference between P and NP?
P problems are solvable in polynomial time; NP problems have solutions verifiable in polynomial time.
How does Dijkstra's algorithm work?
It repeatedly picks the closest unvisited vertex and relaxes its edges; it needs non-negative weights.
What is a minimum spanning tree?
A tree connecting all vertices with minimum total edge weight, found by Kruskal's or Prim's algorithm.
How do I prove an algorithm correct?
Use loop invariants, induction or exchange arguments, depending on the algorithm.
What is backtracking in design and analysis of algorithms?
Backtracking builds a solution step by step and abandons a partial solution as soon as it cannot lead to a valid answer. It is used for problems such as N-Queens, sum of subsets, graph colouring and Hamiltonian cycles. Exams often ask you to draw the state-space tree, so your tutor practises tracing it by hand.
Do you offer lessons in my time zone?
Yes. Lessons are online, so you can book times that suit your time zone.
Can I get help with my design and analysis of algorithms assignment?
Yes. A tutor works through your design and analysis of algorithms assignment topics with you, such as recurrences, greedy proofs and dynamic programming tables, explaining each method on similar examples. You then complete and submit your own work, so you understand it fully and can answer the same types of question in your exam.
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