CS-218 is where Southern New Hampshire University computer science students learn the classic data structures and how to judge an algorithm's cost.
SNHU's catalog describes it as exploring fundamental data structures and algorithms such as the stack, queue, tree, graph and hash table, discussing various implementations, and examining commonly used fast sorting algorithms with in-depth analysis of time and memory use. Students implement solutions using appropriate structures and algorithms.
Prerequisites combine programming (CS-210 or CS-217) with mathematics (MAT-230 or MAT-239), and the course needs both: you write the code and reason about its efficiency.
Course at a Glance
| Item | Details |
|---|---|
| University | Southern New Hampshire University (SNHU) |
| Course code | CS-218 (catalog title: Data Structure and Algorithms) |
| Level | Undergraduate, 3 credits |
| Prerequisites | CS-210 or CS-217, and MAT-230 or MAT-239 |
| Typical work | Implementations, complexity analyses, comparisons of sorting algorithms |
What CS-218 Covers
| Structure | Key idea | Typical use |
|---|---|---|
| Stack | Last in, first out | Undo, expression evaluation |
| Queue | First in, first out | Scheduling, breadth-first search |
| Tree | Hierarchy of nodes | Sorted data, file systems |
| Graph | Nodes joined by edges | Networks, routes |
| Hash table | Key mapped to a slot | Fast lookup |
Key Concepts Explained
Big-O Notation
Big-O describes how running time or memory grows as input size n grows, ignoring constants. It lets you compare approaches before writing code.
| Operation | Typical cost |
|---|---|
| Array access by index | O(1) |
| Search in an unsorted list | O(n) |
| Search in a balanced binary search tree | O(log n) |
| Hash table lookup | O(1) on average, O(n) worst case |
Fast Sorting: Merge Sort and Quicksort
Merge sort splits the list in half, sorts each half and merges them. It runs in O(n log n) in every case but needs extra memory for merging. Quicksort picks a pivot and partitions around it; it averages O(n log n) and sorts in place, but poor pivot choices can push it to O(n²).
Example: Sorting one million records. O(n²) means on the order of 1012 comparisons; O(n log n) means roughly 2 × 107. That gap is why the course focuses on fast sorts and on the memory each one uses.
Hash Collisions
Two keys can map to the same slot. Chaining stores a list at each slot; open addressing probes for another free slot. Keeping the load factor (items divided by slots) low keeps lookups fast.
Example: With 10 slots and the hash "key mod 10", keys 15 and 25 both land in slot 5. With chaining, slot 5 holds both; a lookup for 25 checks just those two items.
Graph Traversal
Breadth-first search uses a queue and explores neighbors level by level, which finds the shortest path in an unweighted graph. Depth-first search uses a stack (or recursion) and goes deep before backtracking.
Typical Assignments and How to Approach Them
| Assignment type | What it tests | How to approach it |
|---|---|---|
| Implement a structure | Understanding of operations | Draw the structure before and after each operation |
| Sorting comparison | Time and memory analysis | Time runs on growing inputs and compare with Big-O |
| Problem solving | Choosing a structure | Justify the choice by the operations needed most |
Binary Search Trees in Practice
A binary search tree keeps every left descendant smaller than its node and every right descendant larger. Searching compares the target with the current node and moves left or right, discarding half the remaining tree at each step when the tree is balanced.
Example: Inserting 50, 30, 70, 20, 40 gives a balanced tree three levels deep; finding 40 takes three comparisons (50, 30, 40). Inserting 10, 20, 30, 40, 50 in sorted order instead produces a chain where every node has only a right child, and finding 50 takes five comparisons. That is the O(n) worst case.
Self-balancing trees, such as AVL and red-black trees, rotate nodes after inserts and deletes to keep the height close to log n. Even if you do not implement one, explaining why balance matters strengthens an analysis.
Memory as Well as Time
SNHU's description calls for analysis of memory use, not just speed. Merge sort typically needs O(n) extra space; in-place quicksort needs only the recursion stack, O(log n) on average. Linked structures use extra memory for pointers, while arrays may waste space when over-allocated.
Where Students Get Stuck
- Pointer and reference bugs. Lost links in linked structures. Draw boxes and arrows.
- Recursion. Missing or wrong base cases cause infinite recursion.
- Analysis without evidence. Back claims about speed with reasoning or measured timings.
Study Tips for CS-218
- Trace each sorting algorithm by hand on an eight-item list.
- Implement each structure once from scratch, even if libraries exist.
- Keep a table of operations and their costs for every structure you study.
How We Help with CS-218
Send the prompt, starter code, your work and feedback. A tutor can explain structures and analysis, help you debug or prepare a commented model implementation to study.
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Frequently Asked Questions
SNHU lists CS-210 or CS-217, plus MAT-230 or MAT-239.
SNHU lists it as a 3-credit undergraduate course.
The catalog mentions commonly used fast sorting algorithms; merge sort and quicksort are the standard examples, often compared with simpler O(n²) sorts.
If pivots repeatedly split the list very unevenly, for example always picking the smallest item, each partition removes only one element.
CS-218 focuses on implementing classic structures and analyzing fast sorts; CS-260's description stresses algorithmic design using coding and non-coding methods.
No. We provide tutoring and model implementations for study; you submit your own work.