Familiarize yourself with algorithmic thinking and problem-solving techniques.

Certainly! Algorithmic thinking and problem-solving techniques are crucial skills in computer programming. Let's explore some examples and concepts to help Marshawn master this objective for her computer programming certification.

1. Pseudocode: Pseudocode is a high-level description of a program or algorithm that uses a mixture of natural language and programming language-like syntax. It helps programmers outline the logical flow of their solutions before writing actual code.

Example:

Explanation: This pseudocode describes a simple program that reads an input value and prints whether the value is large or small based on a condition. Pseudocode helps programmers plan their logic and algorithmic steps before diving into the specifics of a programming language.

2. Flowcharts: Flowcharts visually represent the steps and decisions in an algorithm or program using various symbols to denote different actions and control flow.

Example:

Explanation: This flowchart represents the same logic as the previous pseudocode. It visually illustrates the flow of actions and decisions in a structured manner, making it easier to understand and analyze complex algorithms.

3. Problem-Solving Techniques: a. Divide and Conquer: Divide and Conquer is a problem-solving technique that involves breaking down a complex problem into smaller, more manageable subproblems. It simplifies the problem-solving process by tackling each subproblem individually and then combining their solutions.

b. Dynamic Programming: Dynamic Programming is an algorithmic technique used to solve problems by breaking them into overlapping subproblems and solving each subproblem only once. It stores the solutions to subproblems in a table and reuses them to avoid redundant calculations, leading to more efficient solutions.

c. Greedy Algorithms: Greedy Algorithms make locally optimal choices at each step to find an overall optimal solution. It involves making the best immediate decision without considering the global optimal solution. Greedy algorithms are often used in optimization problems.

These problem-solving techniques are widely used in programming to devise efficient and effective solutions to complex problems. Applying these techniques along with appropriate data structures and algorithms can greatly enhance the problem-solving skills of a programmer.

While pseudocode and flowcharts aid in designing algorithms, problem-solving techniques help programmers approach and solve problems systematically. By familiarizing herself with these concepts and practicing their application, Marshawn can sharpen her algorithmic thinking and problem-solving skills, which are crucial for her computer programming certification.