Algorithmic Thinking (Part 2)

Go to Course: https://www.coursera.org/learn/algorithmic-thinking-2

Introduction

## Course Review: Algorithmic Thinking (Part 2) ### Overview Algorithmic Thinking (Part 2) is an advanced course on Coursera that aims to deepen your understanding of computational problems and their solutions through sophisticated strategies. It is part of a two-part series designed to equip students with the mathematical concepts critical for implementing efficient algorithms that solve complex issues in computer science. This course caters specifically to those who have a foundational knowledge of algorithms and programming concepts. If you want to learn how to manipulate data and solve problems effectively at a high level of abstraction, you've landed in the right place. ### Course Content The syllabus for Algorithmic Thinking (Part 2) is thoughtfully structured into modules, each focusing on core materials and practical applications. Here’s a closer look at the key components: **Module 3: Advanced Algorithms** - **Core Materials:** - Learn about sorting and searching algorithms, understand big-O notation, and delve into the Master Theorem—tools that form the foundation for analyzing algorithm efficiency. - **Project and Application:** - Engage in real-world projects, such as determining the closest pairs of points and exploring clustering methods. You'll get hands-on experience comparing various clustering algorithms, which is essential in data analysis. **Module 4: Dynamic Programming** - **Core Materials:** - Gain a comprehensive understanding of dynamic programming (DP), including its running time and crucial concepts like local and global sequence alignment. - **Project and Application:** - Apply dynamic programming to compute sequence alignments, with specific applications in genomics and text comparison. This project emphasizes not just learning the algorithmic concepts but also exploring their practical utilities in modern science and technology. ### Learning Experience The course is delivered through video lectures, interactive exercises, and quizzes that assess your understanding. Each module seamlessly transitions from theory to application, ensuring that you not only grasp complex concepts but also have the opportunity to apply them to tangible projects. The instructors, experienced computer scientists, guide you through intricate topics in an engaging manner. Their ability to break down challenging material into digestible sections enhances your learning experience. Moreover, the community aspect of Coursera allows you to connect with peers, discuss challenging topics, and seek help, fostering a collaborative learning environment. ### Recommendations I highly recommend Algorithmic Thinking (Part 2) for anyone looking to bolster their algorithm design skills. The comprehensive approach taken with advanced topics equips you with the tools to tackle high-level computing challenges with confidence. If you're an individual with a background in computer science or programming, this course will be an indispensable addition to your skill set. You'll emerge with a deeper mastery of algorithmic strategies that are widely utilized across industries, particularly in fields like data science, software engineering, and computational biology. ### Conclusion In a world driven by data and computational efficiency, understanding algorithms is paramount. Algorithmic Thinking (Part 2) will not only enhance your problem-solving capabilities but also promote a mindset that appreciates the elegance of algorithmic solutions. Enroll in this course today, and take your first step towards becoming an adept algorithm designer!

Syllabus

Module 3 - Core Materials

Sorting, searching, big-O notation, the Master Theorem

Closest pairs of points, clustering of points, comparison of clustering algorithms

Dynamic programming, running time of DP algorithms, local and global sequence alignment

Computation of sequence alignments, applications to genomics and text comparison