Algorithms are not a boring theory, but a language of efficiency.
Even if you are just starting out in programming, understanding simple algorithms like sorting and loops makes a "jun" a person who not only writes code, but understands how it works under the hood.
Knowledge of basic algorithmic principles helps you solve problems faster, write cleaner code, and feel confident in interviews. This article provides a simple explanation of why algorithms are needed, where you have already encountered them (although you may not have noticed 🕵️♂️), and how to start pumping your developer's thinking.
Why do you need to know this right now?
Interviews.
8 out of 10 technical interviews for juniors test logical thinking through tasks on cycles, conditions and simple algorithms.
Daily work.
Any data processing is "go through the list → calculate something → sort → display".
Time is money.
Understanding complexity (Big-O) saves hours and server resources. Even choosing the “right” sorting on big data is a tangible difference in performance.
Mini picture of the world: what solutions consist of
Cycles — to go through the data:
for,while.Branches — to make a decision:
if / else.Sorting - to bring chaos to order.
Search — linear or binary (if the data is ordered).
Data structures — lists, dictionaries, sets.
Everything complex is built from these bricks. Understanding the base, you will confidently "collect" catalog filters, reports, and a backend handle.
Where sorting happens every day
Task lists: by priority, deadline.
Products in the store: by price, rating, novelty.
Logs: by event time.
Tapes/feeds: by "weight" and relevance.
Quick guide to sorting
Algorithm | Worst case | Memory | When it is appropriate |
|---|---|---|---|
Bubble / Insertion | O(n²) | O(1) | Training tasks, very small arrays |
Merge sort | O(n log n) | O(n) | Stable sorting, big data |
Quick sort | O(n²)* | O(log n) | Fast on average, system implementations |
Timsort | O(n log n) | O(n) | Real collections, "almost sorted" data |
Loops: the heart of any data processing
sum = 0
count = 0
for each product in products:
if product.price > 1000:
sum = sum + product.rating
count = count + 1
if count > 0:
avg = sum / count
else:
avg = 0
The same example in JavaScript:
const avg = (products
.filter(p => p.price > 1000)
.reduce((acc, p) => (acc.sum += p.rating, acc.count++, acc), {sum:0, count:0}));
const result = avg.count ? avg.sum / avg.count : 0;
Sorting: not only "beautiful" but also "fast"
Binary search works for O (log n), but requires ordering. So, once sorted (O(n log n)), many times searched very quickly.
const users = [...rawUsers].sort((a, b) => a.id - b.id);
function binarySearchById(arr, id) {
let l = 0, r = arr.length - 1;
while (l <= r) {
const m = (l + r) >> 1;
if (arr[m].id === id) return arr[m];
if (arr[m].id < id) l = m + 1; else r = m - 1;
}
return null;
}
const user = binarySearchById(users, 12345);
How do they ask questions at interviews?
Sort the array by field and explain the choice of algorithm.
Find the second largest element without full sorting.
Group and count the aggregates (sum, average).
Explain the difference between O(n), O(n log n), O(n²).
Code is not just an application for learning programming, but a whole world where learning turns into an adventure. Here you take short lessons, solve problems with auto-checking and pump skills from basic cycles to real projects.
And we also have Telegram channel, where we discuss news from the IT world, analyze real tasks, share memes about code and conduct mini-challenges.
Join us - it's cozy, fun and useful.
💬 What algorithm problem has stalled you the most? Write the condition in the comments — we will analyze it and come up with the most understandable solution.