Why Linear Algebra, Statistics , Probabilty for a coder? Why maths!
Most people who hate maths go down 2 career paths: They either take up biology or choose computers .
Hoping the beautiful devil don’t bother them. However, surprise ! Maths is the essense of coding!
Why Math and Science along with my CS journey?
I’m currently navigating the transition from the low-level world of C to Web Development and AI. Along the way, I’ve made a very specific decision. I am doubling down on Math and Science.
This isn't a compulsion for me. It is a choice I have carefully taken because I want to understand the soul of the machines I am building. I don't want to just type code. I want to know the logic that makes that code possible.
Linear Algebra is the base of AI and ML
If you want to move into AI, you quickly realize that every "cool" feature is actually built on a foundation of Linear Algebra.
It starts with the Vector. In Computer Science, we treat a vector as more than just a line in space. It is a way to represent data. Whether it is a pixel in an image or a word in a sentence, it gets converted into a vector $v$. When an AI "learns," it is essentially performing massive operations on these vectors.
Then you have Eigenvalues and Eigenvectors. In class, these can feel like abstract puzzles. But in the real world of AI, they are the secret to efficiency.
When we use something like Principal Component Analysis to compress data or find patterns, we are looking for the "essence" of a transformation. That essence is found in the equation $Av = \lambda v$. The eigenvector $v$ stays in its direction, while the eigenvalue $\lambda$ tells us how much it scales. Understanding this math is the difference between blindly using a library and actually understanding how an AI "thinks" about data.
Statistics and Probability are the universal language
I have realized that Statistics is needed for almost every field of study. It is the tool that helps us make sense of a chaotic world. When you link it up with Probability, you get a framework for handling uncertainty.
In CS, we are almost never looking for a "perfect" answer. Whether it is a search engine trying to find the most relevant result or a recommendation system predicting what you want to see next, we are dealing with likelihoods. If I do not understand the stats behind the data, I am just guessing. I want to build systems that are grounded in logic, not luck.
The Science grind and the DSA connection
There is a huge overlap between advanced scientific problem solving and Data Structures and Algorithms.
Solving a complex physics problem or a multi-part proof requires a specific kind of mental stamina. It is about breaking a massive and abstract problem into logical, manageable steps. This is the exact same skill I need for competitive programming or optimizing a backend system. The science grind is my gym for logical thinking. It prepares my brain for the "heavy lifting" that comes with high-level engineering.
My first step in public
This is my first post here on Hashnode. I am a student who decided to learn in public. I am choosing to master these foundations because I want to be an engineer who understands the "why" behind every line of code I write.
If you are also choosing the hard path of mastering the basics instead of just chasing the latest trend, just know you are building a foundation that will last forever.
About me?
Hey ! I am Ayush, a comp science student at Bits Pilani, currently at the start of my tech career , trying to learn and jot down some stuff.
My twitter:-https://x.com/Ayush1663537
My linkedin:-https://www.linkedin.com/in/ayush-kumar-bits/
do drop a msg excited to connect with you all!