Data Structures & Algorithms are two main topics every Software Engineer should master to become a better programmer. Data Structures & Algorithms helps you make more informed design choices and write programs that are more efficient & easier to change.
If getting into Big Tech is your dream, you need to have a solid understanding of Data Structures & Algorithms as it improves your problem-solving abilities to a great extent.
In this article, we will be looking at:
- The similarities between Data Structures & Algorithms
- Where should you start learning Data Structures & Algorithms?
- What is the best way to learn Data Structures & Algorithms?
- Important Data Structures & Algorithms to master
How do Data Structures & Algorithms relate to each other?
Generally speaking, a Data Structure is a collection of data organized in a particular way to help you perform computational operations. In contrast, an Algorithm is a set of logical instructions used to carry out a predefined task.
The objective of any algorithm is to:
- Solve a specific problem
- And to solve it efficiently in terms of the time taken for execution.
An algorithm would need to process the data efficiently to complete its objective. Having structured or organized data helps the algorithm solve the problem faster.
Thus both Data Structures and Algorithms are directly dependent on each other.
What is the best way to learn Data Structure & Algorithms?
Mastering Data Structure & Algorithms is a step-by-step learning process. Before you start with your preparations, you should have a roadmap in mind. Here’s how you should structure and plan out your learning:
- Start with learning an object-oriented language:
Pick a language you’re most comfortable with, such as C++, Java, or Python. You can also start with C if you’re facing difficulties with an OOPS language and then switch to C++ or Java.
- Beginning to learn about Data Structures:
Once you feel confident in your chosen programming language concepts like C, C++, Python, or Java, you can then start learning about Data Structures. There are mainly 3 parts of Data Structures you need to master:
- Time and space complexity:
You need to learn how to write an efficient code with time & space complexity.
- Arrays & Strings:
They are the building blocks of any Data Structure and need to be practiced thoroughly.
- Linked lists, Stacks & Queues:
These form the foundation of binary trees, heaps, etc.
3. Learn some of the basics of Algorithms:
Once you’ve understood & mastered the basics of Data Structures, you can then move on to learning Algorithms. Try to build a strong understanding of the following topics in Algorithms:
- Mathematical Logic
- Searching & Sorting Algorithms
- Pattern Matching & String Algorithms
Where should you start learning Data Structures & Algorithms?
If the terms Data Structures & Algorithms sound obscure & abstract to you, then it’s most likely you haven’t found the right method that’d help you understand the topics in an engaging manner.
There are many places you can start learning Data Structures & Algorithms. Some of the free as well as paid options, are listed below:
- Data Structure Concepts in C
- Coursera- Algorithms Part I & II
- Data Structures in Java
- Udemy- Easy to Advanced Data Structures
- Interview Kickstart’s 2-Month Data Structure & Algorithms Masterclass
Important Data Structures & Algorithms to master:
- Linked Lists
- Stacks & Queues
- Bit Operations
- Searching & Sorting
- Dynamic programming
As you start getting better at practicing Data Structures & Algorithms problems, make sure you pick up on some real interview questions as well. Continue polishing and learning various techniques to solve the problems.
Start practicing on paper. Solving on paper forces you to plan your code, making you less likely to make mistakes. Most importantly, it’s the perfect simulation of an actual whiteboard interview.
There you have it, with a detailed roadmap on the best way to learn Data Structure & Algorithms and important topics to master in the area. You’re well on your way to nail your next technical interview at your dream company.