Overview

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Build a strong foundation in algorithmic thinking, data structures, and core algorithm design techniques. Learn to analyze, implement, and optimize algorithms for a variety of computational problems. This course introduces the essential concepts of algorithms, including performance analysis, algorithmic complexity, and the use of fundamental data structures. Learners will explore classic sorting and searching algorithms, as well as key strategies for designing efficient algorithms such as dynamic programming, greedy methods, and linear programming. By the end of the course, participants will be able to select and apply appropriate algorithms to solve real-world problems and understand the underlying logic that drives their effectiveness. Through clear explanations, practical examples, and real-world case studies, this course emphasizes both the theoretical and practical aspects of algorithm design. Learners will develop the skills to analyze algorithm performance and make informed choices for diverse computational challenges. This course is part one of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on 50 Algorithms Every Programmer Should Know, by Imran Ahmad. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Overview of Algorithms
- This module introduces the fundamental concepts of algorithms, including their design, performance analysis using Big O notation, and validation techniques. Learners will explore how algorithms are implemented in Python and how their efficiency is affected by input data. The module emphasizes the importance of selecting and optimizing algorithms for real-world applications.
Data Structures Used in Algorithms
- This module introduces learners to key Python data structures used in algorithm development, including tuples, stacks, matrices, and pandas structures. It covers their characteristics, time complexity, and practical applications in data processing and algorithm design.
Sorting and Searching Algorithms
- This module covers the fundamentals of sorting and searching algorithms, their performance characteristics, and real-world applications. Learners will gain an understanding of how to choose the most appropriate algorithm for different data scenarios and improve computational efficiency.
Designing Algorithms
- This module delves into the principles of algorithm design, focusing on evaluating efficiency, scalability, and optimality. Learners will explore key strategies such as dynamic programming, greedy algorithms, and linear programming, and apply them to real-world problems like the Traveling Salesman Problem. The module equips students with the skills to analyze and improve algorithm performance.
Graph Algorithms
- This module delves into the fundamentals of graph algorithms, focusing on graph representations, network analysis, and traversal techniques. Learners will explore how to model and analyze complex relationships using real-world examples such as fraud detection and social networks. By the end, they will understand how to calculate and interpret centrality metrics in graph structures.

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