Module 3: Advanced Challenges 3

Module Overview

In this module, you will continue to practice advanced coding challenges with a focus on optimization, edge cases, and more complex algorithms. You'll build upon the skills developed in previous modules to tackle harder problems and refine your problem-solving approach.

Learning Objectives

Solve more complex algorithmic challenges
Focus on optimizing solutions for time and space complexity
Handle edge cases and input validation effectively
Explore additional algorithmic patterns and techniques
Practice explaining your thought process in technical interviews

Technical Preparation: Advanced Challenges 3

String Basics

String Case and Concatenation

Formatted Strings

For Loop Refresher

For Loop String Demo

Tackling more complex algorithmic challenges and optimizing solutions.

Time and Space Complexity

When solving technical challenges, it's important to analyze your solution's efficiency:

Time Complexity: How the runtime of your algorithm grows as the input size increases
Space Complexity: How much additional memory your algorithm uses as the input size increases

Common time complexities (from best to worst):

O(1): Constant time - operations don't depend on input size
O(log n): Logarithmic time - operations divide the problem size (e.g., binary search)
O(n): Linear time - operations grow linearly with input size
O(n log n): Linearithmic time - common for efficient sorting algorithms
O(n²): Quadratic time - often seen in nested loops over the data
O(2ⁿ): Exponential time - typically seen in recursive algorithms without memoization

Construct arrays and looping with while-loops

Array Length

Array Access

Array Ops 1

Array Push

Array Delete

Array Filtering

Array Ops 2

Common Algorithmic Patterns

Understanding common algorithmic patterns can help you recognize and solve different types of problems more efficiently.

Hash Tables and Sets

Hash tables provide O(1) average time for lookups, insertions, and deletions, making them powerful tools for many problems.

Common applications include:

Frequency counting (counting occurrences of elements)
Checking for duplicates or unique elements
Implementing caches for storing computed results
Solving problems like Two Sum, where you need to find pairs with a specific sum

Dynamic Programming

Dynamic programming is a technique for solving problems by breaking them down into simpler subproblems and storing the results to avoid redundant calculations.

Key characteristics of dynamic programming problems:

Overlapping subproblems: The same subproblems are solved multiple times
Optimal substructure: The optimal solution contains optimal solutions to subproblems

Common dynamic programming patterns:

Memoization (top-down approach)
Tabulation (bottom-up approach)
2D grid problems
Sequence problems