Introduction:
New students entering computer science often feel confused by the abundance of `#include <stdio.h>`:
“It’s 2025, and the school is still teaching the ancient C language? Isn’t Java or Python better?”
Today, we will dissect this classic debate—what languages should computer science students actually learn?
I. C Language: The “Atomic Structure” of the Computer World
1. Directly Interacting with Hardware, Understanding the Essence of Computers
When you write `new Object()` in Java, C language is dissecting the birth of memory:
“`c
int *p = (int*)malloc(sizeof(int)); // Manually allocating 4 bytes of space on the heap
*p = 42; // Directly manipulating memory address
“`
Pointers, memory management, stack overflow… these low-level concepts are the foundation for understanding operating systems and compiler principles.
2. The Benchmark of Performance
The Linux kernel is written in C, and Redis handles millions of requests per second—**when performance is pushed to the limit, C remains the ultimate weapon**. Learning C is like learning physical formulas; understanding F=ma is essential to building a supercar.
3. The “Programming Gene” Across Time and Space**
The RAII of C++, the memory safety mechanisms of Rust, all fundamentally address the pain points of C. **Without experiencing the pain of manual memory management, it is hard to understand the innovative value of modern languages.**
II. Java/Python: Underestimated “Engineering Tools”?
Controversy Focus: Since C is so important, is it still necessary to learn Java and Python?
The answer is not just “necessary” but “essential”—but timing is crucial.
▶ Java: The “Air” of Enterprise Development
Dominance of the Backend Ecosystem: 80% of global financial systems and the entire Apache big data stack (Hadoop/Kafka) are based on Java
Engineering Model: Interface design, design patterns, Spring framework… learning Java is essentially learning software engineering methodologies.
▶ Python: The “Swiss Army Knife” of the AI Era
King of Development Efficiency: Writing websites with Django is three times faster than Java, and processing data with Pandas saves hundreds of lines of code compared to C++.
First Language of AI: The APIs of TensorFlow/PyTorch are written in Python; not understanding Python means giving up on the AI track.
III. The Key to Breaking the Deadlock: Don’t Ask “Which to Learn”, Ask “When to Learn”
Recommended Golden Learning Path:
“`mermaid
graph LR
A[Freshman: C Language] –> B[Understanding Memory/Pointers/Compilation]
B –> C[Sophomore: Data Structures Implemented in C]
C –> D[Junior: Learning Engineering Architecture with Java]
C –> E[Junior: Focusing on AI/Data Analysis with Python]
D & E –> F[Senior: Deepening Knowledge in Chosen Direction]
Real Corporate Needs (2025 Data):
Embedded/Operating System Positions: C language proficiency is a hard requirement.
Backend Development: Java/Go dominate, but those who understand JVM tuning must know C.
AI Algorithm Positions: Python is the shell, but model optimization requires C++ acceleration.
Conclusion: Language is the Sail, Computational Thinking is the Rudder
When you see a Python interpreter rewritten in C (like PyPy) on GitHub, or discover a garbage collector written in C within the Java Virtual Machine—**you will realize: all languages ultimately converge to the same underlying logic.
So there is no need to worry about “whether it is outdated”:
Use C to cultivate internal skills (system principles)
Use Java to master engineering (large-scale collaboration)
Use Python to create value (rapid implementation)
The true advantage of formal education is the ability to see the essence through syntax—that is the confidence of programmers who are not afraid of technological changes.