This course will guide you through the essential principles of algorithms and their significance in computational problem-solving. You'll begin by exploring what an algorithm is, its core characteristics, and how it applies to real-world scenarios—from simple everyday tasks to complex computing challenges. As you progress, you will learn about the critical role algorithms play in improving efficiency and scalability across various fields. We’ll break down key concepts such as algorithmic complexity, helping you evaluate the efficiency of different approaches, which will ultimately guide your decision-making.
Data Structures & Algos: Software Development Skills
Gain insight into a topic and learn the fundamentals.
18 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
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December 2024
Assessments
18 assignments
Taught in English
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There are 4 modules in this course
In this module, you will be introduced to the foundational concept of algorithms, including their characteristics and how they are integral to solving computational problems. You’ll explore the basics of algorithmic complexity and efficiency, providing a strong foundation for the advanced topics in subsequent modules.
What's included
1 video9 readings2 assignments1 discussion prompt
In this module, you will explore the powerful and elegant Gale-Shapley algorithm, originally developed to solve the stable marriage problem. This algorithm, widely used in real-world applications such as college admissions and job matching, ensures that individuals are paired in a way that avoids instability—where two participants could form a better match with someone else. By understanding the principles behind stable matching and the mechanics of this algorithm, you'll gain insight into one of the most influential solutions in game theory, optimization, and computer science.
What's included
1 video12 readings5 assignments
What's included
1 video13 readings5 assignments
In this module, you'll begin by summarizing key concepts like computational tractability, asymptotic growth, and the notations used to evaluate algorithm efficiency. You'll then dive into time complexity, learning how to optimize algorithms for different scenarios and classify them into appropriate complexity classes. By the end, you'll be able to apply these analysis techniques to real-world problems, optimizing solutions while considering the implications and limitations of algorithm analysis.
What's included
1 video14 readings6 assignments
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