Lecture 1: Introduction to programming

What Is Programming?

Programming is the process of writing instructions for a computer to execute.
It involves problem-solving, logic, and creativity.

Why Learn Programming?

• It's essential in fields such as software development, data science, AI, and automation.
• It enhances logical thinking and problem-solving skills.

Problem Solving

Solving problems is at the core of computer science.
A programmer must:

1. Understand how a human solves a problem.
2. Translate this solution into a computer algorithm.
3. Write the correct syntax for the computer to execute.

Sometimes, a computer solves a problem differently from a human.

Stages of Problem Solving

1. Analyze the problem.
2. Write the algorithm steps:
   • Pseudocode
   • Flowchart
3. Write the program in a programming language.
4. Translate the program into machine code.
5. Execute, test, and debug the program.
6. Document the software.

Problem Elements Analysis

Analyzing the problem involves identifying:

• Inputs: Required data and their format.
• Processing: Necessary mathematical and logical operations.
• Outputs: Desired results and their format.

Key Questions

1. What are the desired results or outputs?
2. What data (inputs) are available?
3. What processes will convert inputs into outputs?

Algorithms

Definition

An algorithm is a well-defined set of rules or steps for solving a problem in a finite number of steps.

Characteristics of a Good Algorithm

1. Each step must be clear and unambiguous.
2. The algorithm must terminate after a finite number of steps.
3. The process must lead to the correct solution.

Characteristics of an Algorithm

1. Well-Defined Inputs and Outputs: Clear input values and definite output.
2. Definiteness: Each step is precise and unambiguous.
3. Finiteness: Must terminate after a finite number of steps.
4. Effectiveness: Steps must be simple and executable within reasonable time and resources.
5. Correctness: Produces accurate results for the problem.
6. Generality: Applicable to a range of similar problems.
7. Sequential Execution: Steps follow a logical order.

Algorithm Design Techniques

Algorithms can be represented by:

• Flowcharts: Graphical representation of logical steps.
• Pseudocode: English-like representation of algorithm steps.

Pseudocode

Definition

Pseudocode is a simplified, language-like representation of an algorithm that focuses on logic, not syntax.

Features

• Focuses on problem-solving steps.
• Can be implemented in any programming language.
• Easier to understand and modify before coding.

Components

• Keywords
• Sections
• Statements

Writing Pseudocode

Guidelines

• Use meaningful variable names.
• Keep instructions simple, clear, and unambiguous.

Advantages

1. Easy to understand.
2. No need to memorize symbols (unlike flowcharts).
3. No strict syntax.
4. Easy to convert to code.
5. Space-efficient.
6. Based on simple, clear rules.

Disadvantages

• Can become lengthy for complex problems.

Statement Structures in Pseudocode

1. Sequential Statements

Executed one after another.

Example: Add two numbers

BEGIN
  INPUT N1, N2
  SUM = N1 + N2
  OUTPUT "The sum is: ", SUM
END

2. Decision (Selection) Statements

Used when the program must choose between alternatives based on a condition.

Example: Division with validation

BEGIN
  INPUT number1, number2
  IF number2 ≠ 0 THEN
    result = number1 / number2
    OUTPUT "The result is: ", result
  ELSE
    OUTPUT "Error: Division by zero is not allowed."
  ENDIF
END

3. Repetition (Looping) Statements

Used to repeat instructions multiple times until a condition is false.

Example: Print "Welcome" five times

BEGIN
  SET counter = 1
  WHILE counter ≤ 5 DO
    OUTPUT "Welcome"
    counter = counter + 1
  ENDWHILE
END

Note: The repetition structure can be implemented using loops such as while, for, or do-while.

Summary

• Programming involves writing logical instructions for a computer.
• Problem-solving is the foundation of programming.
• Algorithms provide structured solutions.
• Pseudocode and flowcharts help design algorithms before implementation.
• Key control structures: Sequence, Decision, and Repetition.