Data Structures And Algorithms 課程

Data Structures and Algorithms work together to solve computational problems, usually by enabling an algorithm to manipulate data efficiently. The algorithm uses a set of rules (the data) to find the greatest common divisor, with one example being YouTube tracking a user’s activities to display videos relevant to them. Actions such as “liking” or “disliking” a video create data structures that inform the direction of the algorithm, bringing content to users that they are more likely to find engaging.

In the field of Marketing, Data Structures and Algorithms are commonly used to help organizations determine how to attract an audience to their online content—but they’re also used in the field of healthcare in Medical Algorithms. These are important to learn in order to calculate someone’s BMI, drug dosages, and more.‎

Data Structures and Algorithms go together like the tech industry and career opportunities—as long as people are using computers, they’ll both be in abundance. When concepts like running times, binary searches, dynamic programming, and others are nailed down, learners can begin to explore the wide variety of roles available to them. These roles include Platform Engineer, Graphics Engineer, Full-Stack Engineer, Backend Engineer, Product Analyst, Data Scientist, Data Engineer, Big Data Engineer, Data Architect, Application Developer, Mobile Developer, and others that are related.

A search of “Data Structures and Algorithms” on LinkedIn’s job portal shows roughly 11,500 results in the U.S. alone, with opportunities at large and small tech firms.‎

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.

Lessons on Data Structures and Algorithms are taught by instructors from major universities, including University of California at San Diego and Tsinghua University. Learners can enjoy exploring Data Structures and Algorithms with instructors specializing in Computer Science, Technology, Mathematics, and other disciplines. Course content on Data Structures and Algorithms is delivered via video lectures, hands-on projects, readings, quizzes, and other types of assignments.‎

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.‎

Learning data structures and algorithms may be right for you if you would like to advance your engineering or data science career. If you would like to learn how to apply basic algorithmic techniques, such as greedy algorithms, binary search, sorting, and dynamic programming to solve programming challenges, then studying the subject may be right for you. Learning data structures and algorithms might benefit you if you’d like to understand how to apply various data structures such as a stack, queue, hash table, priority queue, binary search tree, graph, and string to solve programming challenges, as well. But to understand how to reach a good algorithm, you’ll need to understand how to create a set of good data structures. Studying data sets and algorithms can benefit you if you need to learn how data structures are implemented in different programming languages.‎