Master Algorithmic Programming Techniques. Learn algorithms through programming and advance your software engineering or data science career

提供方

University of California San Diego

National Research University Higher School of Economics

University of California San Diego

关于此专项课程

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.

立即开始，按照自己的计划学习。

设置并保持灵活的截止日期。

建议 7 小时/周

字幕：English, Spanish

Data StructureDynamic ProgrammingGraph AlgorithmsAlgorithms On Strings

立即开始，按照自己的计划学习。

设置并保持灵活的截止日期。

建议 7 小时/周

字幕：English, Spanish

Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习，请直接注册专项课程，或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时，您将自动订阅整个专项课程。您可以只完成一门课程，您可以随时暂停学习或结束订阅。访问您的学生面板，跟踪您的课程注册情况和进度。

每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。

在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。

1**课程**

4.7

3,589 个评分

•

797 个审阅

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)....

2**课程**

4.7

1,566 个评分

•

273 个审阅

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!...

3**课程**

4.7

844 个评分

•

147 个审阅

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....

4**课程**

4.5

460 个评分

•

94 个审阅

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....

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory....

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.
Learn more on www.hse.ru...

What is the refund policy?

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Can I take the course for free?

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

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: Python, C++, Java, C#, Javascript, C, Haskell, Ruby, 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 yourself.

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!

How long does it take to complete the Specialization?

Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-8 months.

How often is each course in the Specialization offered?

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.

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.

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