For each programming challenge in this course, we provide starter solutions in C++, Java, and Python. We expect you to be able to implement programs that: 1) read data from the standard input (in most cases, the input is a sequence of integers) 2) compute the result (in most cases, a few loops are enough for this) 3) print the result to the standard output. Basic knowledge of at least one programming language: C++, Java, Python, C, C#, Javascript, Haskell, Kotlin, Ruby, Rust, Scala.
We will finish with minimum spanning trees which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms.ġ. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. We will then talk about shortest paths algorithms - from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. In this online course, you will first learn what a graph is and what are some of the most important properties. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect a set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs. If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space! What are good strategies to keep a binary tree balanced?
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How to implement a hash table so that the amortized running time of all operations is O(1) on average?Ĥ. How priority queues are implemented in C++, Java, and Python?ģ. What is a good strategy of resizing a dynamic array?Ģ. You will also learn typical use cases for these data structures.Ī few examples of questions that we are going to cover in this class are the following:ġ. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. In this online course, we consider the common data structures that are used in various computational problems. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.Ī good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice.
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While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it.
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