Our Data Structures course covers arrays, linked lists, stacks and queues, recursion, trees and binary search trees, AVL trees, heaps and priority queues, hashing, graphs and their traversals, and sorting and searching, with complexity analysis and code in C, C++, Java or Python. 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
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What you will learn
Prerequisites
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In short: this course gives one-to-one online tutoring in data structures for university and college students. A verified tutor explains arrays, linked lists, stacks, queues, trees, heaps, hash tables and graphs, their complexities and implementations, and reviews your own code.
Data structures are ways of organising and storing data in a computer so it can be accessed and changed efficiently, such as arrays, linked lists, stacks, queues, hash tables, trees and graphs. A data structures course teaches how each structure works, its time and space complexity, and when to use it, and is a core second-year module in most computer science degrees.
This online course teaches Data Structures 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 help.
| Topic | Key content |
|---|---|
| Complexity analysis | Big-O, Big-Theta and Big-Omega, best, average and worst case |
| Linear lists | Arrays, singly, doubly and circular linked lists |
| Stacks and queues | Array and linked implementations, deques, priority queues, applications |
| Hashing | Hash functions, chaining, open addressing, load factor |
| Trees | Binary trees, traversals, expression trees |
| Search trees | Binary search trees, AVL trees, red-black trees |
| Multiway trees | B-trees and B+ trees |
| Heaps | Binary heaps, heap sort |
| Graphs | Representations, BFS, DFS, shortest paths, minimum spanning trees |
Choose by the operations you need most and their cost.
| Need | Good choice | Typical cost |
|---|---|---|
| Fast access by index | Array | O(1) |
| Frequent insertion and deletion | Linked list | O(1) at a known node |
| Last in, first out | Stack | O(1) push and pop |
| Fast lookup by key | Hash table | O(1) average |
| Sorted data with fast search | Balanced BST | O(log n) |
| Repeated minimum or maximum | Heap | O(log n) insert and extract |
| Relationships between items | Graph | Depends on representation |
Here is a typical binary search tree question: insert 50, 30, 70, 20 and 40 into an empty BST, then give the in-order traversal.
| Step | Result |
|---|---|
| Insert 50 | 50 is the root |
| Insert 30 and 70 | 30 goes left of 50; 70 goes right |
| Insert 20 and 40 | 20 goes left of 30; 40 goes right of 30 |
| In-order traversal | 20, 30, 40, 50, 70 (sorted order) |
Students most often lose marks by stating complexities without justification, forgetting edge cases such as empty structures, mixing up traversal orders, and writing code that leaks memory or breaks pointers.
| Mark-loser | What to do instead |
|---|---|
| Unjustified complexities | Explain where O(log n) or O(n) comes from |
| Edge cases ignored | Test empty, single-element and full structures |
| Traversal orders confused | Practise pre-, in- and post-order on paper |
| Broken pointers | Draw the structure before and after each operation |
| Wrong structure chosen | Match the structure to the operations needed |
University data structures modules are usually assessed by programming assignments, lab work and a written exam on tracing operations, complexity and choosing structures.
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.
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 Design and Analysis of Algorithms, Data Structures and Algorithms for interviews and our Computer Science subject page.
A data structures tutor online gives you live 1-to-1 lessons on lists, stacks, hash tables, trees and graphs, tracing each operation by hand and then coding it in the language your course uses.
This data structures course suits first- and second-year computer science and IT students taking a data structures module, and anyone who has learned a programming language and now needs to write efficient code.
Prerequisites: basic programming in any language, including variables, loops, conditions and functions.
Course length: 40 one-hour lessons across 8 modules.
| Stage | Modules | What you can do afterwards |
|---|---|---|
| 1 | Complexity analysis, linear lists | Analyse code with Big-O and implement linked lists |
| 2 | Stacks and queues, hashing | Use stacks, queues and hash tables, and handle collisions |
| 3 | Trees, search trees, multiway trees | Traverse trees, build and balance binary search trees, explain B-trees |
| 4 | Graphs | Represent graphs and run BFS, DFS and shortest-path algorithms |
Where it leads: continue with Design and Analysis of Algorithms, or Data Structures and Algorithms for coding interviews.
Ways of organising data so it can be accessed and modified efficiently, such as arrays, lists, trees and graphs.
C or C++ show memory clearly; Java and Python are also common. Tutors teach in your course language.
A way of describing how an algorithm's running time or memory grows as the input size grows.
A stack is last in, first out; a queue is first in, first out.
Most students who already know one programming language need two to three months of regular practice to become comfortable with the core data structures and their time complexity. Graphs and balanced trees usually take the longest. Your tutor sets problems after each topic, so you build speed as well as understanding before your exam.
It is challenging at first, but drawing structures and implementing them yourself makes it much easier.
Yes. Tutors practise tracing, complexity and past-style exam questions.
Basic programming helps. See our Programming Fundamentals course if you are starting out.
The price depends on the tutor and session length. You see the hourly rate before booking, with no subscription.
No. Tutors explain, guide and give feedback so you can complete your own work, in line with our academic integrity policy.
The keys in sorted order, for example 20, 30, 40, 50, 70.
O(n) for search when the tree becomes a chain, which is why balanced trees are used.
When two keys map to the same slot; it is handled with chaining or open addressing.
A heap only guarantees the parent-child order needed for a priority queue; a BST keeps all keys in sorted order.
When you need frequent insertions and deletions in the middle and don't need random access.
With an adjacency list for sparse graphs or an adjacency matrix for dense graphs.
Averaging the cost of operations over a sequence, such as dynamic array resizing being O(1) amortised.
Trees, heaps, hash tables, graphs and their traversals and complexities.
Not exactly. Data structures is the study of ways to store and organise data, such as arrays, lists, trees and graphs. DSA, data structures and algorithms, adds the algorithms that work on them, such as sorting, searching, greedy methods and dynamic programming. A university data structures module usually comes before a separate algorithms module.
Yes. Lessons are online, so you can book times that suit your time zone.
Yes. You can learn with a 1-to-1 data structures tutor online through live lessons with a shared screen. Your tutor traces each structure by hand, helps you code it in C, C++, Java or Python, explains time complexity and works through past-paper questions before your exam.
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