This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.
データ構造とアルゴリズム専門講座
Master Algorithmic Programming Techniques. Learn algorithms through programming and advance your software engineering or data science career
提供：
- Machine Learning Engineers
- Data Scientists
- Software Engineers
- Data Engineers
- Traders
学習内容
Apply basic algorithmic techniques such as greedy algorithms, binary search, sorting and dynamic programming to solve programming challenges.
Apply various data structures such as stack, queue, hash table, priority queue, binary search tree, graph and string to solve programming challenges.
Apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.
Solve complex programming challenges using advanced techniques: maximum flow, linear programming, approximate algorithms, SAT-solvers, streaming.
習得するスキル
専門講座の仕組み
コースを受講しましょう。
Coursera（コーセラ）の専門講座は、一連のコース群であり、技術を身に付ける手助けとなります。開始するには、専門講座に直接登録するか、コースを確認して受講したいコースを選択してください。専門講座の一部であるコースにサブスクライブすると、自動的にすべての専門講座にサブスクライブされます。1つのコースを修了するだけでも結構です。いつでも、学習を一時停止したり、サブスクリプションを終了することができます。コースの登録状況や進捗を追跡するには、受講生のダッシュボードにアクセスしてください。
実践型プロジェクト
すべての専門講座には、実践型プロジェクトが含まれています。専門講座を完了して修了証を獲得するには、成功裏にプロジェクトを終了させる必要があります。専門講座に実践型プロジェクトに関する別のコースが含まれている場合、専門講座を開始するには、それら他のコースをそれぞれ終了させる必要があります。
修了証を取得
すべてのコースを終了し、実践型プロジェクトを完了すると、修了証を獲得します。この修了証は、今後採用企業やあなたの職業ネットワークと共有できます。
この専門講座には6コースあります。
Algorithmic Toolbox
The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second). Do you have technical problems? Write to us: coursera@hse.ru
データ構造
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. 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 also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space! Do you have technical problems? Write to us: coursera@hse.ru
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. 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 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. In this course, you will first learn what a graph is and what are some of the most important properties. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. 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. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. 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. Do you have technical problems? Write to us: coursera@hse.ru
文字列アルゴリズム
World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome. Do you have technical problems? Write to us: coursera@hse.ru
講師
Daniel M Kane
Assistant Professor
Neil Rhodes
Adjunct Faculty
Pavel Pevzner
Professor
Michael Levin
Lecturer
Alexander S. Kulikov
Visiting Professor業界パートナー
カリフォルニア大学サンディエゴ校について
ロシア国立研究大学経済高等学院（National Research University Higher School of Economics）について
よくある質問
返金ポリシーについて教えてください。
1つのコースだけに登録することは可能ですか？
はい。まず始めに興味のあるコースカードをクリックして登録します。コースに登録して修了することによって、共有できる修了証を取得するか、無料でコースを聴講してコースの教材を確認することができます。専門講座の一部であるコースにサブスクライブすると、専門講座全体に自動的にサブスクライブされます。進捗を追跡するには、受講生のダッシュボードにアクセスしてください。
学資援助はありますか？
無料でコースを受講できますか？
このコースは100%オンラインで提供されますか？実際に出席する必要のあるクラスはありますか？
このコースは完全にオンラインで提供されているため、実際に教室に出席する必要はありません。Webまたはモバイル機器からいつでもどこからでも講義、学習用教材、課題にアクセスできます。
What will I be able to do upon completing the Specialization?
You will be able to apply the right algorithms and data structures in your day-to-day work and write programs that work in some cases many orders of magnitude faster. You'll be able to solve algorithmic problems like those used in the technical interviews at Google, Facebook, Microsoft, Yandex, etc. If you do data science, you'll be able to significantly increase the speed of some of your experiments. You'll also have a completed Capstone either in Bioinformatics or in the Shortest Paths in Road Networks and Social Networks that you can demonstrate to potential employers.
What background knowledge is necessary?
1. Basic knowledge of at least one programming language: C++, Java, Python, C, C#, Javascript, Haskell, Kotlin, Ruby, Rust, Scala.
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. For each programming challenge in this course, we provide starter solutions in C++, Java, and Python. The best way to check whether your programming skills are enough to go through problems in this specialization is to solve two problems from the first week. If you are able to pass them (after reading our tutorials), then you will definitely be able to pass the course.
2. Basic knowledge of discrete mathematics: proof by induction, proof by contradiction.
Knowledge of discrete mathematics is necessary for analyzing algorithms (proving correctness, estimating running time) and for algorithmic thinking in general. If you want to refresh your discrete mathematics skills, we encourage you to go through our partner specialization — Introduction to Discrete Mathematics for Computer Science (https://www.coursera.org/specializations/discrete-mathematics). It teaches the basics of discrete mathematics in try-this-before-we-explain-everything approach: you will be solving many interactive puzzles that were carefully designed to allow you to invent many of the important ideas and concepts yoursel
What is the difference between this course and other courses covering algorithms?
We believe that learning the theory behind algorithms (like in most Algorithms 101 courses taught at 1000s universities) is important but not sufficient for a professional computer scientist today. This specialization combines the theory of algorithms with many programming challenges. In contrast with many Algorithms 101 courses, you will implement over 100 algorithmic problems in the programming language of your choice. And you will see yourself that the best way to understand an algorithm is to implement it!
専門講座を修了するのにどのくらいの期間かかりますか？
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-8 months.
専門講座の各コースはどのくらいの頻度で提供されますか？
Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over.
Do I need to take the courses in a specific order?
We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.
専門講座を修了することで大学の単位は付与されますか？
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
Do I need to buy a textbook for this specialization?
The lectures in this specialization will be self-contained. Most lectures will be based on the bestselling textbook "Algorithms" co-authored by Sanjoy Dasgupta from University of California at San Diego as well as Christos Papadimitriou and Umesh Vazirani from University of California at Berkeley. In addition to UCSD and Berkeley, the textbook has been adopted in over 100 top universities and is available on Internet.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
