Uncodemy Data Structures Course Syllabus Explained in Detail

Course Guide · Data Structures

Uncodemy Data Structures Course Syllabus Explained in Detail

MODULE 01Programming fundamentals & complexity

The first two weeks set the foundation. Students pick a language (Java or Python) and learn to think in terms of data structures.

What this module covers

• Variables, loops, functions, recursion basics
• Big-O notation — time and space complexity
• Best, average, and worst-case analysis
• How to read a problem and design an approach

O(1)     → Constant time
          → Same time regardless of input size
          → Example: array access by index

O(log n) → Logarithmic time
          → Time grows very slowly
          → Example: binary search

O(n)     → Linear time
          → Time grows with input size
          → Example: single loop over an array

O(n^2)   → Quadratic time
          → Time grows with the square
          → Example: nested loops

O(2^n)   → Exponential time
          → Explodes quickly
          → Example: naive Fibonacci

Rule of thumb:
  → O(1) < O(log n) < O(n) < O(n log n) < O(n^2) < O(2^n)

Why complexity matters:

Interviews
  → Every coding interview starts here
  → You must know the complexity of your solution
  → "It works" is not enough — it must be efficient

Real systems
  → A slow algorithm on millions of records fails
  → A fast algorithm scales
  → That is the difference in production

Debugging
  → If your code times out, the complexity is the issue
  → You need to know what to optimize

The shortcut
  → Count the loops in your code
  → One loop = O(n)
  → Nested loops = O(n^2)
  → Dividing by half = O(log n)

Learning tip:
  → Practice writing 5–10 examples of each complexity
  → You will start seeing patterns

MODULE 02Arrays and strings

Arrays are the simplest data structure — and the most tested. This module covers every pattern you need to know about them.

What this module covers

• 1D and 2D arrays
• Two-pointer technique
• Sliding window
• Prefix sums
• String manipulation and pattern matching
• Common interview problems (remove duplicates, rotate array, etc.)

MODULE 03Linked lists, stacks, and queues

Linear data structures with different trade-offs. Each is tested in interviews and used in real systems.

What this module covers

• Singly and doubly linked lists
• Reverse, merge, and cycle detection
• Stacks — LIFO principle
• Queues — FIFO principle, circular queues
• Deques and priority queues
• Applications (undo/redo, expression evaluation, scheduling)

MODULE 04Trees and heaps

This is where Data Structures starts getting genuinely interesting — and where most candidates start to struggle. The course goes deep.

What this module covers

• Binary trees and traversals (inorder, preorder, postorder, level-order)
• Binary search trees (BST) — insert, delete, search
• AVL trees and balanced trees (concept)
• Heaps (min-heap and max-heap)
• Priority queue applications
• Common problems (lowest common ancestor, diameter, etc.)

A binary tree looks like this:

        10
       /  \
      5    15
     / \   / \
    3   7 12  20

Each node has at most 2 children.
The top is called the root.
The nodes without children are leaves.

Why trees are useful:
  → They model hierarchies (file systems, org charts)
  → They enable fast search (O(log n) if balanced)
  → They are the base for many algorithms

Common operations:
  → Insert a value
  → Search for a value
  → Delete a value
  → Traverse (visit every node)

Rule:
  → Binary search trees have a special property
  → Left child < parent < right child
  → This enables binary search

Why trees matter:

Interviews
  → Trees appear in ~30% of coding interviews
  → Recursion + trees is a classic combination
  → You must be comfortable with traversals

Real systems
  → File systems are trees
  → Database indexes use B-trees
  → JSON is a tree structure

Problem patterns
  → Lowest common ancestor
  → Diameter of a tree
  → Path sum problems
  → Validate BST
  → Serialize/deserialize

Heaps
  → A special tree used for priority queues
  → Used in Dijkstra's algorithm, task scheduling
  → Top-K problems are solved with heaps

Learning tip:
  → Draw the tree on paper before coding
  → Write all 4 traversals by hand
  → Then implement them in code

MODULE 05Hashing and graphs

Two of the most powerful topics in the course — and the two most frequently asked in interviews for mid-level and senior roles.

What this module covers

Hashing:

• HashMaps and HashSets
• Collision handling strategies
• Common problems (two-sum, group anagrams, etc.)
• When to use hashing vs sorting

Graphs:

• Representations (adjacency list, adjacency matrix)
• BFS and DFS
• Topological sort
• Shortest path basics (Dijkstra)
• Union-Find (Disjoint Set Union)

MODULE 06Searching and sorting

Classic algorithms that form the base of computer science. Knowing them deeply pays off in both interviews and real systems.

What this module covers

• Linear search vs binary search
• Bubble, selection, and insertion sort
• Merge sort and quicksort
• Counting sort and radix sort (concepts)
• Searching on sorted arrays (two-pointer)
• Sorting as a preprocessing step

MODULE 07Dynamic programming and recursion

The hardest module — and the most valuable. Dynamic programming is where most candidates fall apart, and where the strongest ones stand out.

What this module covers

• Recursion patterns and base cases
• Memoization (top-down DP)
• Tabulation (bottom-up DP)
• Classic problems (Fibonacci, knapsack, longest common subsequence)
• Recognizing DP problems
• Bit manipulation basics

MODULE 08Interview patterns and mock rounds

The final module brings everything together — with a focus on interview performance, not just theory.

What this module covers

• Most common interview patterns (fast/slow pointers, merge intervals, etc.)
• How to approach an unfamiliar problem
• Mock coding interviews
• How to talk through your solution
• Debugging under pressure
• Common mistakes and how to avoid them

QUIZTest yourself — Data Structures basics

FAQFrequently asked questions

RELATEDRelated reads