What you will learn in Computational Thinking for Problem Solving
Grasp the four pillars of computational thinking: decomposition, pattern recognition, data representation and abstraction, and algorithms.
Develop and analyze algorithms, understanding their efficiency and application.
Comprehend the fundamental operations of modern computers, including the von Neumann architecture.
Translate problem-solving strategies into Python code, even without prior programming experience.
Program Overview
Module 1: Pillars of Computational Thinking
⏳ 3 hours
- Introduction to the core concepts of computational thinking and their application in problem-solving.
Module 2: Expressing and Analyzing Algorithms
⏳ 4 hours
- Learn to develop algorithms and assess their performance, including understanding algorithmic complexity.
Module 3: Fundamental Operations of a Modern Computer
⏳ 3 hours
- Explore how computers execute instructions and manage data, including an overview of the von Neumann architecture.
Module 4: Applied Computational Thinking Using Python
⏳ 6 hours
- Apply computational thinking by writing simple Python programs to solve problems.
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Job Outlook
Completing this course enhances problem-solving and analytical skills applicable across various industries.
Provides a foundation for further studies in computer science and programming.
Equips learners with skills relevant to roles requiring analytical and computational thinking.
Specification: Computational Thinking for Problem Solving
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FAQs
- No prior programming knowledge is required.
- The course uses Python only as a tool to demonstrate problem-solving.
- Beginners can follow along easily with guided examples.
- Programming focuses on syntax and coding skills.
- Computational thinking is about problem-solving strategies that can be applied with or without coding.
- It teaches decomposition, pattern recognition, abstraction, and algorithm design, which go beyond language-specific skills.
- Yes, computational thinking applies to business, science, research, and even daily decision-making.
- It trains you to break down complex issues into manageable steps.
- These problem-solving techniques are transferable to many industries.
- Only basic arithmetic and logical reasoning are needed.
- No advanced mathematics is required.
- The course emphasizes logical steps, not abstract formulas.
- Yes, it provides a strong base for future programming and algorithm courses.
- You’ll develop a problem-solving mindset that makes learning complex topics easier.
- It acts as a stepping stone toward fields like data science, AI, or software development.
