This chapter introduces the fundamental concepts of how a computer works. From the basics of binary math and the essential components like the CPU and memory to the evolution of programming languages, learners will explore how computers process instructions and the difference between interpreted and compiled languages.

Introduction to a Computer: Architecture & Execution

Convert binary to decimal

Binary to decimal position

Connecting Programs to Computers

Compiled vs Interpreted Languages

Why is binary important?

History of programming

Classify what is Functional, Procedural, and Low Level Programming

Evolution of Programming

The Start : What is a Computer?

In this chapter, learners dive into algorithms—the instructions that computers follow to solve problems. Topics include an overview of algorithm types, the importance of time and space complexity, and hands-on practice with real-world examples of sorting and searching. The chapter also covers how to assess algorithm efficiency using Big-O Notation.

What is an algorithm

Algorithm complexity

Advancements in technology

Algorithm Efficiency in Running Time and Space Complexity

Identifying Time Complexity

Sort the order of complexity

Real-world algorithm practice

Which algorithms are more efficient?

Compare Online Shopping Experience

Dig Deeper: Instructing a computer efficiently

This chapter explores the limits of what computers can compute. Learners will study formal models like automata and Turing machines, learn about computational complexity (including P vs NP), and discover real-world applications in fields like cryptography, machine learning, and quantum computing.

Computability

Identify the type of Automata 1

Identify the type of Automata 2

What is true about computability?

Turing machine

Missing step in Turing Machine

Turing machine tapes

Computational complexity

P Problems

NP-Complete Problems

Options

Polynomial Time (P) Problems

NP-Hard Problems

Classify Problems by its Complexity

Real World Implications of Computational Complexity

Complexity in crypto

Cryptography

Machine Learning

Quantum Computing

Match the Concept with its Technology

Final Overview & AI

Expanding Out: What computers can & can't do

Unlock the secrets behind how computers handle complex tasks with ease! In this course, you'll dive into the essentials of computer science, from programming languages and algorithm design to computational theory. Learn about key concepts like Big-O Notation, Turing machines, and the limits of computation. By taking Concepts of Computer Science, you'll understand algorithms and evaluate their efficiency, bring your understanding of computing to the next level!

Unlock the Power of Computation
Ever wondered how computers perform complex tasks effortlessly? This comprehensive course on computation takes you from the basics of how computers work to the depths of computational theory. You'll gain practical skills and foundational knowledge that will empower you to understand and create efficient programs.

Explore Computer Fundamentals
Start with the core concepts of computing. Learn how computers function, the essence of binary math, and the history of programming languages. Discover the differences between functional, procedural, and low-level programming languages, and understand how programs are translated for computers through interpreted and compiled languages.

Master Algorithms and Efficiency
Delve into the world of algorithms, where you'll explore their design and application. Understand the significance of algorithm efficiency through Big-O Notation, and learn to assess time and space complexity. Practical lessons will enable you to solve real-world problems, such as sorting and searching in an online shopping context, using Python.

Discover Computability and Beyond
Expand your knowledge to the theoretical aspects of computation. Learn about automata, Turing machines, and the limits of what computers can solve. Gain insights into computational complexity, including P, NP, NP-Complete, and NP-Hard problems. Finally, explore the real-world implications of these concepts in fields like cryptography and machine learning, and get a glimpse into the future with Quantum Computing.

What You'll Achieve
By the end of this course, you'll have a solid understanding of computer operations, programming languages, and algorithms. You'll be equipped to evaluate algorithm efficiency and understand the theoretical limits of computation. This course will provide you with the skills to solve practical problems efficiently and appreciate the ongoing advancements in computational theory.

Join us to unlock the power of computation and transform your understanding of how computers work!

Introduction to Python for Developers

Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.

Concepts in Computer Science

Relies on NP-hard problems, such as factoring large numbers, for security.

Ensures secure communication through encryption algorithms like RSA.

Uses algorithms like gradient descent to optimize model performance.

Applies supervised and unsupervised methods to identify patterns in data.

Uses qubits to represent data in multiple states simultaneously.

Employs algorithms like Shor’s to factor large numbers efficiently.

Match the Concept with its Technology

Concepts in Computer Science

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