Advanced Coding Problems Every Programmer Should Master

Have you ever spent hours scrolling through a seemingly endless social media feed, or gotten lost in the rabbit hole of an addictive online game? Believe it or not, the code you don’t even see is the secret sauce behind those experiences! Programmers are the masterminds who craft the magic behind the apps, websites, and games you use every day. They are also logical masterminds as every successful programmer solves many advanced coding problems every day.

If you want to become a succefull programmer, conside reading this post.

But coding isn’t just about making cool and catchy stuff (although that’s a pretty awesome perk!). It’s also a valuable skill that can open doors to exciting careers, empower you to solve real-world problems, and even help you understand the technology that shapes our world.

Now, coding can get pretty complex, especially as you delve deeper. But fear not, fellow adventurer! This blog post is your friendly guide to mastering some of the most valuable advanced coding problems every programmer should tackle in 2024.

1. Fibonacci sequence

Have you ever observed the captivating arrangement of leaves on a stem, or the lovely spiral patterns found in sunflowers?

Unbelievably, the Fibonacci sequence—a remarkable mathematical sequence—is connected to these patterns!

Consider a string of integers where each one is equal to the sum of the two numbers that came before it. In a word, that is the Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21… and so on. Each step begins with 0 and 1, and the subsequent number is obtained by adding the two numbers that came before it.

This seemingly simple sequence has surprising applications in nature, from the branching patterns of trees to the arrangement of petals in a flower. It even pops up in financial markets and computer science!

The Fibonacci sequence isn’t about complex calculations, but rather its repetitive pattern. Understanding this pattern can unlock hidden connections in the world around us, making it a captivating concept for mathematicians and nature enthusiasts alike.

2. Longest increasing subsequence

Imagine you have a list of random numbers. The Longest Increasing Subsequence (LIS) challenge is like climbing a ladder made of these numbers,
but you can only go up! You can’t jump over numbers or take steps backward.

Here’s how it works:

Start at the first number. It’s automatically part of the subsequence.
Look at the next number. If it’s bigger than the current number, add it to the subsequence. If it’s smaller, keep searching.
Repeat step 2 for all remaining numbers. The goal is to find the longest sequence of numbers that keeps increasing.

Why is it important?

LIS is a fundamental problem in computer science because it applies to many real-world scenarios. For example, finding the longest common sequence in DNA analysis or scheduling tasks in a way that optimizes completion time.

By mastering LIS, you not only strengthen your problem-solving skills but also gain a valuable tool for tackling various programming challenges.

3. Breadth-First Search (BFS) and Depth-First Search (DFS)

Imagine exploring a maze.
Two popular search techniques can help you find the exit: Breadth-First Search (BFS) and Depth-First Search (DFS).

BFS, or breadth-first search:

Consider this methodical: BFS investigates every path, one level at a time. BFS visits every neighbor of a starting point before advancing on to their neighbors, much like going through every hallway on the first floor before going down to the second.
Guaranteed shortest path (for unweighted graphs): In an unweighted labyrinth (where all pathways take the same amount of time), BFS is guaranteed to locate the shortest path to the exit (if one exists) since it investigates all closest choices first.

Depth-First Search (DFS):

Think adventurous: DFS dives deep into a single path until it hits a dead end or the exit. Like following a winding tunnel, DFS explores one path as far as it can before backtracking and trying another branch.
Faster for some situations: DFS can sometimes find the solution faster, especially in situations where the exit is located deep within a single branch of the maze.

Choosing Your Path:

The best search method depends on the problem. BFS is ideal for finding the shortest path in unweighted graphs, while DFS might be faster for specific situations or exploring connected components (clusters of nodes) in a graph. Both techniques are powerful tools for programmers to navigate complex data structures and solve various problems.

4. Topological Sorting

Imagine a complex project where you can’t start task B until task A is finished, and task C depends on both A and B being done.

Topological sorting tackles this!

It’s like organizing dominoes: you line them up so only a falling domino topples the next. In coding, it arranges tasks (like code functions) with dependencies into a sequence where each task is completed before any that rely on it begin.

Here’s the gist:

Identify tasks and their dependencies (e.g., task B needs A to be done first).
Look for tasks with no incoming dependencies (no dominoes leaning on them). These are starting points.
Remove these tasks and any outgoing dependencies they create.
Repeat steps 2 and 3 until all tasks are processed.

5. Fractional knapsack problem

Imagine you’re a thief (but a good-natured one, for educational purposes!) planning a big heist. You can only carry a certain weight of loot (knapsack capacity). There are various treasures (items) with different weights and values. You want to steal the most valuable loot possible without going over the weight limit.

You can choose to take the entire thing or leave it behind in a standard knapsack problem. How would you, however, feel about fitting a half-broken diamond necklace (item) next to a gold statue (another thing)?

That’s the beauty of the fractional knapsack problem! It allows you to take a fraction of an item if it maximizes your total value without exceeding the weight limit. You calculate the value per weight ratio for each item and prioritize those with the highest ratio.

6. Huffman Coding

Imagine packing for a trip.

You’d use more space for bulky clothes and less for socks. Huffman coding works similarly! It’s a data compression technique used in files and transmissions. It is a very very essential skill if you work in big companies.

Here’s the magic about it:

Character Frequency: The code analyzes how often each character appears in the data (like letters in a text file).
Character Assignment: More frequent characters get shorter code sequences, just like packing fewer bulky items saves space. Less frequent characters get longer sequences.
Compressed Data: The original data is replaced with these shorter codes, resulting in a smaller file size.

7. N-Queens problem

The N-Queens problem is a classic brainteaser for programmers. Imagine an N x N chessboard.

You want to place N queens on the board such that no two queens can attack each other (diagonally, horizontally, or vertically).

Although, It sounds simple, but as N increases, the number of possible placements explodes! Backtracking algorithms come into play. One row at a time, we deliberately strive to place queens on the board. We “backtrack” and attempt an alternative queen placement in that row if a queen placement breaks the rules or is assaulted by another queen. We are able to identify solutions where all queens live in peace thanks to this systematic exploration.

8. Quick Sort

Have you ever sorted a messy pile of cards? Quick Sort uses a similar divide-and-conquer strategy for code! Imagine a list of numbers. Quick Sort picks a value (pivot) and rearranges the list. Numbers less than the pivot are placed on one side, and larger ones on the other. Now, it sorts these smaller sub-lists recursively, effectively conquering the larger problem by tackling smaller chunks. This continues until the entire list is sorted, putting each number in its rightful place!

9. Merge Sort

Merge sort is a sorting algorithm that follows the divide-and-conquer approach.
To create the sorted array, it recursively divides the input array into smaller subarrays, sorts those subarrays, and then merges them back together.

To put it simply, merge sort involves splitting an array in half, sorting each half separately, and then merging the sorted halves back together. Until the entire array is sorted, these steps are repeated.

Here is how it works:

Divide: Split the array or list recursively into two halves until there is no more division possible.
Conquer: The merge sort method is used to sort each subarray separately.
Merge: The sorted subarrays are combined once again in the original sequence of sorting. Until every element from both subarrays has been combined, the operation is repeated.

10. Binary Search

Imagine searching a giant, sorted haystack for a specific needle. Brutal, right? Binary search offers a much faster way. It works by constantly picking the hay in the exact middle. If the needle is there, you’re done If it’s to the left, you discard the right half. If it’s to the right, you discard the left half. This keeps halving the haystack you need to search, making it super efficient for finding things in sorted data

Practise resources:

Conquering these advanced coding problems takes dedication and practice. Here are some stellar resources to help you solidify your understanding and become a problem-solving whiz:

Online Coding Platforms:

LeetCode: A popular platform offering a vast collection of coding problems categorized by difficulty level, algorithms, and companies. It includes solutions, discussions, and interview preparation resources. (https://leetcode.com/)
HackerRank: Another well-known platform with a diverse range of coding challenges, tutorials, and competitions. It allows you to track your progress and compare yourself with other programmers. (https://www.hackerrank.com/)
Codewars: This platform gamifies the learning process with coding katas – small, focused exercises to practice specific skills. It uses a belt ranking system to motivate you as you progress. (https://www.codewars.com/)

Always Remember:
Consistent practice is key! Set aside dedicated time each day or week to tackle coding challenges. Don’t be afraid to experiment, make mistakes, and learn from the online community as you embark on your journey to mastering these advanced coding problems. Happy coding!