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This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course.

Data Structures and Algorithms courses offered through Coursera equip learners with knowledge in common data structures that are used in various computational problems; typical use cases for certain data structures; principles and methods in the design and implementation of various data structures; and more.

Some of the skills or experience you may need to have before learning data structures and algorithms include coding, some programming concepts, and a basic understanding of Java and Object-Oriented Programming (OOP). If you understand the concept of sorting algorithms, you may already have some skills needed to study the subject. Also, if you have an understanding of basic data structures such as linked lists, queues, matrices, stacks, and trees, you may have some solid skills needed to learn data structures and algorithms.

The kind of people who are best suited for roles in data structures and algorithms are focused on becoming programmers or software engineers/developers who have an emphasis on applications and scientific performance analysis. They are comfortable thinking outside the box for innovative ways to save a company money by using algorithms to solve problems. These professionals enjoy learning about giving computers the right sets of instructions so that they can skillfully solve very complex problems. They may also enjoy working in roles related to data structures and algorithms because they are energized by wanting to make software run properly and efficiently.

The key to successful technical interviews is practice. In this course, you'll review common Python data structures and algorithms. You'll learn how to explain your solutions to technical problems. This course is ideal for you if you've never taken a course in data structures or algorithms. It's also a good refresher if you have some experience with these topics. You'll learn the concepts through video tutorials. You'll watch experienced engineers review supplementary examples and discuss different interview approaches. Then, apply your skills and practice in mock interviews with Pramp.

Great code has its foundation built upon data structures and algorithms. One must have a deep understanding of how data structures operate and designing efficient algorithms. Implementing algorithmic techniques that efficiently manipulate data structures is the essence of this program.

Work with the principles of data storage in Arrays, ArrayLists & LinkedList nodes. Understand their operations and performance with visualizations. Implement low-level linear, linked data structures with recursive methods, and explore their edge cases. Extend these structures to the Abstract Data Types, Stacks, Queues and Deques.

Become familiar with nonlinear and hierarchical data structures. Study various tree structures: Binary Trees, BSTs and Heaps. Understand tree operations and algorithms. Learn and implement HashMaps that utilize key-value pairs to store data. Explore probabilistic data structures like SkipLists. Course tools help visualize the structures and performance.

Learn more complex tree data structures, AVL and (2-4) trees. Investigate the balancing techniques found in both tree types. Implement these techniques in AVL operations. Explore sorting algorithms with simple iterative sorts, followed by Divide and Conquer algorithms. Use the course visualizations to understand the performance.

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