Coding Projects
Open in Telegram
Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data
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Network:📈 Analytical overview of Telegram channel Coding Projects
Channel Coding Projects (@programming_experts) in the English language segment is an active participant. Currently, the community unites 67 949 subscribers, ranking 1 910 in the Technologies & Applications category and 4 739 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 949 subscribers.
According to the latest data from 25 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 172 over the last 30 days and by 6 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.69%. Within the first 24 hours after publication, content typically collects 1.06% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 510 views. Within the first day, a publication typically gains 717 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
- Thematic interests: Content is focused on key topics such as |--, algorithm, array, framework, javascript.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Channel specialized for advanced concepts and projects to master:
* Python programming
* Web development
* Java programming
* Artificial Intelligence
* Machine Learning
Managed by: @love_data”
Thanks to the high frequency of updates (latest data received on 26 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
67 949
Subscribers
+624 hours
-197 days
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Channel Posts
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| 12 | Time complexity:
O(n)
This is a very important array interview problem because it tests whether you can optimize a seemingly expensive problem.
🔹 14. What is the difference between a subarray and a subsequence?
Answer:
A subarray must contain contiguous elements.
Example:
[2, 3, 4]
is a subarray of:
[1, 2, 3, 4, 5]
A subsequence does not need to be contiguous, but the original order must be maintained.
Example:
[1, 3, 5]
is a subsequence of:
[1, 2, 3, 4, 5]
but it is not a contiguous subarray.
This distinction becomes extremely important in Dynamic Programming problems such as Longest Common Subsequence.
🔹 15. How do you rotate an array?
Example:
Input:
[1, 2, 3, 4, 5]
Rotate right by 2:
[4, 5, 1, 2, 3]
One efficient approach uses the reversal technique.
Conceptually:
Reverse entire array
Reverse first k elements
Reverse remaining elements
For:
[1, 2, 3, 4, 5]
rotate right by "2":
Reverse all:
[5, 4, 3, 2, 1]
Reverse first 2:
[4, 5, 3, 2, 1]
Reverse remaining:
[4, 5, 1, 2, 3]
This technique can achieve:
Time: O(n)
Space: O(1)
Learn to recognize patterns:
✔ Two Pointers
✔ Sliding Window
✔ Prefix Sum
✔ HashMap / HashSet
✔ Binary Search
✔ Sorting
✔ Kadane's Algorithm
✔ In-place modification
Once you recognize the pattern, many seemingly different array problems become much easier to solve.
Double Tap ❤️ For More
-----
2.86 ₽ · /balance_help | 1 652 |
| 13 | Average time complexity:
O(n)
Additional space:
O(n)
This is usually faster than comparing every pair, which can take O(n²).
🔹 8. What is the Two Sum problem?
Answer:
You are given an array and a target value. Find two elements whose sum equals the target.
Example:
Array: [2, 7, 11, 15]
Target: 9
The answer is:
2 + 7 = 9
A HashMap can solve this efficiently.
arr = [2, 7, 11, 15]
target = 9
seen = {}
for i, num in enumerate(arr):
complement = target - num
if complement in seen:
print(seen[complement], i)
break
seen[num] = i
Output:
0 1
Average time complexity:
O(n)
Space complexity:
O(n)
This is one of the most important patterns to recognize in array interviews.
🔹 9. How do you move all zeroes to the end?
Example:
Input:
[0, 1, 0, 3, 12]
Output:
[1, 3, 12, 0, 0]
A Two Pointer approach can solve this in-place.
arr = [0, 1, 0, 3, 12]
position = 0
for i in range(len(arr)):
if arr[i] != 0:
arr[position], arr[i] = arr[i], arr[position]
position += 1
print(arr)
Output:
[1, 3, 12, 0, 0]
Time complexity:
O(n)
Space complexity:
O(1)
🔹 10. How do you find the missing number?
Suppose the array contains numbers from "0" to "n", but one number is missing.
Example:
[3, 0, 1]
The missing number is:
2
One efficient approach uses the expected sum:
arr = [3, 0, 1]
n = len(arr)
expected_sum = n * (n + 1) // 2
actual_sum = sum(arr)
missing = expected_sum - actual_sum
print(missing)
Output:
2
Time complexity:
O(n)
Space complexity:
O(1)
🔹 11. What is the difference between Linear Search and Binary Search?
Answer:
Linear Search checks elements one by one.
Time: O(n)
It can work on unsorted data.
Binary Search repeatedly divides the search space approximately in half.
Time: O(log n)
It requires suitable sorted data.
For example:
Linear Search:
10 → 20 → 30 → 40 → 50
Binary Search:
30
/ \
10 40...
The key interview point is not simply memorizing the complexities. You should understand when Binary Search can actually be applied.
🔹 12. What is an in-place algorithm?
Answer:
An in-place algorithm modifies the input data structure while using only a small amount of additional memory.
For example, reversing an array using two pointers:
arr = [1, 2, 3, 4]
left = 0
right = len(arr) - 1
while left < right:
arr[left], arr[right] = arr[right], arr[left]
left += 1
right -= 1
No second array is created.
Therefore, the extra space used is:
O(1)
🔹 13. What is the Maximum Subarray problem?
Answer:
You are given an array of integers and need to find the contiguous subarray with the maximum sum.
Example:
[-2, 1, -3, 4, -1, 2, 1, -5, 4]
The maximum-sum contiguous subarray is:
[4, -1, 2, 1]
Its sum is:
6
A famous solution uses Kadane's Algorithm.
arr = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
current = arr[0]
maximum = arr[0]
for i in range(1, len(arr)):
current = max(arr[i], current + arr[i])
maximum = max(maximum, current)
print(maximum)
Output:
6 | 1 162 |
| 14 | 📦 Common Array Interview Questions with Answers
After understanding the fundamentals of Arrays, let's look at the questions frequently asked in coding and technical interviews.
🔹 1. What is an Array?
Answer:
An array is a data structure used to store multiple elements in an ordered sequence.
Each element can generally be accessed using an index.
Example:
arr = [10, 20, 30, 40]
print(arr[2])
Output:
30
The index of "30" is "2".
The main advantage of an array is efficient index-based access.
🔹 2. What is the time complexity of accessing an array element?
Answer:
Accessing an element by its index is typically:
O(1)
Example:
arr = [10, 20, 30, 40, 50]
print(arr[3])
The system can directly access the element at index "3" rather than searching through all previous elements.
This is one of the biggest advantages of arrays.
🔹 3. What is the difference between an Array and a Linked List?
Answer:
An array stores elements in a contiguous structure in traditional array implementations, while a linked list stores elements as separate nodes connected using references/pointers.
Arrays provide fast index-based access:
arr[i] → O(1)
Linked lists generally require traversal to reach a particular position:
Access → O(n)
However, linked lists can be advantageous when inserting or deleting nodes at a known position because elements do not need to be shifted like they may in an array.
The choice depends on the problem.
🔹 4. How do you find the largest element in an array?
Answer:
Traverse the array while maintaining the largest value found so far.
Example:
arr = [10, 45, 23, 67, 12]
largest = arr[0]
for num in arr:
if num > largest:
largest = num
print(largest)
Output:
67
Time complexity:
O(n)
Space complexity:
O(1)
🔹 5. How do you find the second-largest element?
Answer:
We can maintain two variables:
largest
second_largest
Example:
arr = [10, 45, 23, 67, 12]
largest = float('-inf')
second_largest = float('-inf')
for num in arr:
if num > largest:
second_largest = largest
largest = num
elif num > second_largest and num != largest:
second_largest = num
print(second_largest)
Output:
45
This can be solved in:
Time: O(n)
Space: O(1)
An important interview follow-up is:
"What if all elements are equal?"
A good solution should explicitly handle the case where a distinct second-largest element does not exist.
🔹 6. How do you reverse an array?
Answer:
One approach is to use two pointers.
Example:
arr = [1, 2, 3, 4, 5]
left = 0
right = len(arr) - 1
while left < right:
arr[left], arr[right] = arr[right], arr[left]
left += 1
right -= 1
print(arr)
Output:
[5, 4, 3, 2, 1]
Time complexity:
O(n)
Space complexity:
O(1)
This is an example of an in-place algorithm because we modify the original array instead of creating another array.
🔹 7. How do you find duplicate elements in an array?
Answer:
A HashSet can be used to keep track of elements already encountered.
Example:
arr = [1, 2, 3, 2, 4, 1]
seen = set()
duplicates = set()
for num in arr:
if num in seen:
duplicates.add(num)
else:
seen.add(num)
print(duplicates)
Output:
{1, 2} | 1 171 |
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| 19 |  𝗧𝗢𝗣 𝗙𝗥𝗘𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦 𝗧𝗢 𝗕𝗢𝗢𝗦𝗧 𝗬𝗢𝗨𝗥 𝗦𝗞𝗜𝗟𝗟𝗦 𝗜𝗡 𝟮𝟬𝟮𝟲!
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| 20 |  🚀 𝗪𝗵𝗶𝗰𝗵 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹 𝗪𝗶𝗹𝗹 𝗬𝗼𝘂 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲?
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