Big Picture Overview: Zoom Out to understand.
Companies want to hire what and whom?
They want to build Products be it Apple, Google or Microsoft.
They want to hire Engineers who can make those products.
So where can they find those engineers?
Obviously from colleges and universities, where science and engineering are taught.
For marketing purposes, they often say anyone from diverse backgrounds can join. In reality, it resembles more of an elephant's trunk than actual teeth.
They always prioritise techies.
They do hire non-techs but are expected to think like Computer Engineers.
So to acquire the Computer Engineering mindset, we need to study some subjects, one of them is Data Structure & Algorithms.
These subjects provide the foundational knowledge and problem-solving skills that are essential for tackling real-world engineering challenges.
So Why learn DSA?
Because it's a college subject?
Because it's asked in Interviews.
If you have a mindset like that then it's not gonna work out.
We should study DSA because:
- It makes us a better Computer Engineer.
DSA equips us with the fundamental tools and techniques necessary for solving complex problems efficiently and designing robust, scalable software systems.
It enables us to write more efficient code, design optimal algorithms, and ultimately build better products.
So, the real motivation behind learning DSA should be to enhance our skills and become proficient Computer Engineers capable of tackling real-world challenges effectively.
What is DSA?
DSA is not just a subject it's a branch of Science and Mathematics.
It builds a scientific mindset.
Let's say what is better to go faster? Speed or Acceleration.
A non-science background student might say speed but a science background student will know to go faster we need to increase acceleration.
- DSA provides us with the tools to solve computational problems efficiently, including determining the best route between two points.
When faced with multiple routes, deciding which one is the shortest or most optimal involves considering various factors such as traffic, road quality, distance, and time.
DSA algorithms, such as Dijkstra's algorithm or A* search algorithm, help us evaluate these factors and find the most efficient path based on specified parameters. By applying these algorithms, we can make informed decisions and optimize solutions to real-world problems, like finding the shortest route between two locations.
DSA can be understood as comprising two main components:
Data Structures:
It deals with organizing and storing data in a specific way to facilitate efficient access, modification, and retrieval.
Data structures provide a framework for representing data elements and the relationships between them.
Examples include arrays, linked lists, stacks, queues, trees, and graphs.
Algorithms:
Algorithms are step-by-step procedures or methods for solving computational problems.
They define the process to acquire desired results by manipulating the data stored in various data structures.
Efficiency is a key focus when designing algorithms, as we aim to achieve the desired outcomes in the most optimized manner, considering factors such as time complexity, space complexity, and overall performance.
Together, data structures and algorithms form the backbone of computer science and engineering, enabling us to organize data effectively and devise efficient solutions to a wide range of problems. By mastering DSA, we develop the skills necessary to analyze, design, and implement robust and scalable software systems.
Before delving into Data Structures and Algorithms (DSA), it's beneficial to have a foundational understanding of two key concepts:
Inductive Reasoning: This involves drawing general conclusions based on specific observations or instances. For example, if you observe 10 dogs barking, you might conclude that all dogs bark. Inductive reasoning plays a crucial role in understanding patterns and making predictions, which are fundamental skills in problem-solving and algorithm design.
Discrete Mathematics: Discrete mathematics provides the theoretical framework for reasoning about discrete structures, such as sets, sequences, relations, and graphs. It also involves methods of proof, which are essential for establishing the correctness of algorithms and data structures. Understanding proofs helps in analyzing the correctness and efficiency of algorithms, as well as in developing new algorithms.
Algorithm Efficiency:
Count fundamental operations.
Mesures strength of an algorithm.
So in short it's all about which is better in which situation. And that it.
Credit for the information presented in this post goes to the insightful Why Learn DSA? video by Hitesh Choudhary, available on his YouTube channel Chai aur Code. Special thanks to Hitesh Choudhary for sharing his expertise and knowledge on this topic.