When is inheritance useful?

A major disadvantage of inheritance is not being able to follow the program flow by ctrl + clicking function calls due to some functionality being implemented in ancestor classes. Therefore, I prefer composition over inheritance whenever possible. The one case where inheritance shines is reusing workflows. Let's say all your child classes have a run() method that calls f1(), f2() and f2() in order, where f1, f2, f3 functions are implemented in child classes. If we move the run() method to the ancestor class, child classes would just call that run method, saving you from duplicating the f1, f2, f3 calls in each child.

Studying Algorithms and Data Structures Effectively

There are many ways to solve programming problems. The ideal solution is the one running the fastest and consuming the least amount of memory. The field of algorithms and data structures equips you with the tools to find the best solution. A good solution might run  a million times faster than a naive/brute force solution, which might be the difference between a successful and failed project. It has similarities to mathematics because mathematics always tries to find more concise formulae. For example you can find the sum of consecutive integers from 1 to n by summing each number one by one which has a time complexity of O(n). Or you could think a little more about the problem and observe that there are n/2 pairs of the value of n+1, yielding the famous Gauss formula of n/2*(n+1), which has a time complexity of O(1), i.e. the brute force method gets slower and slower as n increases while the formula always runs at constant time! See a similar Codility problem. A good solution is also environmentally friendly since it consumes less energy.

When studying algorithms and data structures, besides course material, I recommend solving the relevant leetcode questions. Start with the easy ones but note that if this is your first attempt at solving "easy" questions, they won't be easy at all, watch this video for a general approach. Don't just focus on the answer, analyze your solution process even after your solution passes all tests so that it really sinks in.

The good news is that once you solve easy questions, medium ones are not that difficult. The website NeetCode organizes problems nicely according to data structure type and difficulty, I personally use the NeetCode 150 group (see my notes). Python is a concise language to solve coding problems, this 26 minute video teaches you enough python for that. After you finish 29 easy questions on NeetCode 150, move to medium questions, and finally solve the hard ones. It will probably take a couple of months to solve all 150 questions. Besides strengthening you algorithms and data structures foundation, you also will have practiced for coding interviews.

If you want to practice more in a specific area, you can use chatGPT to generate additional questions/problems, e.g. "generate a medium level coding question related to trie data structure"

Visualizing algorithms: Visualgo

05.10.2025: For junior roles, consider this smaller set of 49 questions curated by Aman Manazir, designed to be completed in 8 weeks:

ARRAYS & HASHING

01	Easy	Contains Duplicate
02	Easy	Valid Anagram
03	Easy	Two Sum
04	Medium	Group Anagrams
05	Medium	Top K Frequent Elements
06	Medium	Valid Sudoku
07	Medium	Product of Array Except Self
08	Medium	Longest Consecutive Sequence

TWO POINTERS

09	Easy	Valid Palindrome
10	Medium	Two Sum II Input Array Is Sorted
11	Medium	3Sum
12	Medium	Container With Most Water

SLIDING WINDOW

13	Easy	Best Time to Buy And Sell Stock
14	Medium	Longest Substring Without Repeating Characters
15	Medium	Longest Repeating Character Replacement

STACK

16	Easy	Valid Parentheses
17	Medium	Min Stack
18	Medium	Daily Temperatures

BINARY SEARCH

19	Easy	Binary Search
20	Medium	Find Minimum In Rotated Sorted Array
21	Medium	Search In Rotated Sorted Array

LINKED LIST

22	Easy	Reverse Linked List
23	Easy	Merge Two Sorted Lists
24	Medium	Reorder List
25	Medium	Remove Nth Node From End of List
26	Easy	Linked List Cycle
27	Medium	LRU Cache

TREES

28	Easy	Invert Binary Tree
29	Easy	Maximum Depth of Binary Tree
30	Easy	Diameter of Binary Tree
31	Easy	Balanced Binary Tree
32	Easy	Same Tree
33	Easy	Subtree of Another Tree
34	Medium	Lowest Common Ancestor of a Binary Search Tree
35	Medium	Binary Tree Level Order Traversal
36	Medium	Binary Tree Right Side View
37	Medium	Count Good Nodes In Binary Tree
38	Medium	Validate Binary Search Tree
39	Medium	Kth Smallest Element In a Bst

HEAP / PRIORITY QUEUE

40	Easy	Kth Largest Element In a Stream
41	Easy	Last Stone Weight
42	Medium	Kth Largest Element In An Array

GRAPHS

43	Medium	Number of Islands
44	Medium	Max Area of Island
45	Medium	Clone Graph
46	Medium	Pacific Atlantic Water Flow
47	Medium	Surrounded Regions
48	Medium	Course Schedule
49	Medium	Course Schedule II