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+---- Thread: Programming Fundamentals — Logic, Structure, Algorithms & Problem-Solving (/showthread.php?tid=99)
Programming Fundamentals — Logic, Structure, Algorithms & Problem-Solving - Leejohnston - 11-13-2025
Programming Fundamentals — Logic, Structure, Algorithms & Problem-Solving
Programming is the art of turning ideas into instructions a computer can follow.
This thread introduces the essential concepts needed to understand code, algorithms, and computational thinking.
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1. What Is Programming?
Programming is the process of writing instructions that computers execute.
Languages include:
• Python
• JavaScript
• C / C++
• Java
• Rust
• Go
• Swift
All languages share the same core ideas:
• variables
• data types
• logic
• loops
• functions
• algorithms
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2. Variables & Data Types
Variables store information.
Common data types:
• integers (whole numbers)
• floats (decimals)
• strings (text)
• booleans (True/False)
• lists/arrays
• dictionaries/maps
Example in Python:
Code:
age = 21
name = "Sarah"
height = 1.75
is_student = True-----------------------------------------------------------------------
3. Control Flow — Making Decisions
Programs make decisions using conditions.
Example:
Code:
if temperature > 30:
print("It's hot!")
else:
print("Nice weather.")Comparison operators:
• >
• <
• >=
• ==
• !=
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4. Loops — Repeating Instructions
Loops repeat code until a condition is met.
For loop:
Code:
for i in range(5):
print(i)While loop:
Code:
while x < 10:
x += 1Loops are vital for:
• simulations
• processing lists
• iterating over data
• automation
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5. Functions — Reusable Blocks of Code
Functions organise code into reusable pieces.
Example:
Code:
def add(a, b):
return a + bBenefits:
• avoids repetition
• clearer structure
• easier debugging
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6. What Is an Algorithm?
An algorithm is a step-by-step method for solving a problem.
Example:
“Find the largest number in a list”
1. assume first number is largest
2. check each number
3. if a number is bigger → update largest
4. output result
Programming = implementing algorithms in code.
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7. Common Algorithms
Sorting:
• bubble sort
• merge sort
• quicksort
Searching:
• linear search
• binary search
Graph algorithms:
• Dijkstra’s shortest path
• A* search
Data structure basics:
• stacks
• queues
• linked lists
• trees
• hash maps
These form the basis of computer science.
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8. Big O Notation (Time Complexity)
Big O describes how fast an algorithm grows as input size increases.
Examples:
• O(1) → constant
• O(n) → linear
• O(n²) → quadratic
• O(log n) → logarithmic
Why it matters:
• performance
• scalability
• optimisation
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9. Good Programming Practices
✔ write clean, readable code
✔ comment important sections
✔ use descriptive variable names
✔ test your code
✔ break problems into smaller parts
✔ avoid duplication (DRY principle)
✔ version control (Git)
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10. Common Mistakes
❌ Forgetting indentation (Python)
✔ indentation defines structure
❌ Infinite loops
✔ ensure loop conditions eventually become False
❌ Using = instead of == in conditions
✔ = assigns, == compares
❌ Not testing small pieces first
✔ build and test in steps
❌ Hardcoding everything
✔ use variables and functions
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11. Beginner Practice Questions
1. Write pseudocode for deciding if a number is even or odd.
2. What is the difference between a list and a dictionary?
3. Explain how binary search works.
4. What does O(n) mean in time complexity?
5. Write a function that returns the square of a number.
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Summary
This thread covered:
• variables
• decision making
• loops
• functions
• algorithms
• Big O notation
• good practices
• common mistakes
• practice questions
Programming is problem-solving — and this forum is your space to learn, experiment, and build.