The ability to analyze data with Python is critical in data science. Learn the basics, and move on to create stunning visualizations.

STARTS

Sep 19th 2017

Created by:Delivered by:

Taught by:

Learn the basics of data structures and methods to design algorithms and analyze their performance. 本课程旨在围绕各类数据结构的设计与实现，揭示其中的规律原理与方法技巧；同时针对算法设计及其性能分析，使学生了解并掌握主要的套路与手段。

Data structures play a central role in computer science and are the cornerstones of efficient algorithms. Knowledge in this area has been at the kernel of related curriculums. This course aims at exploring the principles and methods in the design and implementation of various data structures and providing students with main tools and skills for algorithm design and performance analysis. Topics covered by this course range from fundamental data structures to recent research results. "Data Structures and Algorithm Design Part II" is an advanced course extending the materials in "Part I". We will cover more powerful and sophisticated data structures & algorithms, including: splay trees, B-trees, red-black trees, hash tables, priority queues, strings and sorting.

数据结构是计算机科学的关键内容，也是构建高效算法的必要基础。其中涉及的知识，在相关专业的课程系统中始终处于核心位置。本课程旨在围绕各类数据结构的设计与实现，揭示其中的规律原理与方法技巧；同时针对算法设计及其性能分析，使学生了解并掌握主要的套路与手段。讲授的主题从基础的数据结构，一直延伸至新近的研究成果。

**What you'll learn**

- Algorithms used to solve complex problems

- Principles and methods in the design and implementation of various data structures

- Skills for algorithm design and performance analysis

- Background on fundamental data structures and recent results

- 数据结构的设计与实现

- 揭示其中的规律原理与方法技巧

- 了解并掌握主要的套路与手法

Already taken this course?

Please rate.

Please rate.

or

Self Paced

The ability to analyze data with Python is critical in data science. Learn the basics, and move on to create stunning visualizations.

Self Paced

Learn the R statistical programming language, the lingua franca of data science. R is rapidly becoming the leading language in data science and statistics. Today, the R programming language is the tool of choice for data scientists in every industry and field. Whether you are a full-time number cruncher, or just the occasional data analyst, R will suit your needs.

Sep 25th 2017

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.

Sep 25th 2017

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Sep 25th 2017

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more obvious applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision or finding dense clusters in the advertiser-search query graphs at search engines. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements, call routing in telecommunications and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them approximately in a reasonable time. We finish with some applications to Big Data and Machine Learning which are heavy on algorithms right now.

Sep 25th 2017

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables.

Sep 25th 2017

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).

Sep 25th 2017

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.

Sep 25th 2017

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.

Sep 25th 2017

How does Google Maps plan the best route for getting around town given current traffic conditions? How does an internet router forward packets of network traffic to minimize delay? How does an aid group allocate resources to its affiliated local partners? To solve such problems, we first represent the key pieces of data in a complex data structure. In this course, you’ll learn about data structures, like graphs, that are fundamental for working with structured real world data.

Sep 25th 2017

Welcome to our course on Object Oriented Programming in Java using data visualization. People come to this course with many different goals -- and we are really excited to work with all of you! Some of you want to be professional software developers, others want to improve your programming skills to implement that cool personal project that you’ve been thinking about, while others of you might not yet know why you’re here and are trying to figure out what this course is all about.

Sep 18th 2017

这门课程将帮助学生学习如何运用基础的数据结构和相关算法解决实际应用问题。

- Page 1
- ››

Multiple Criteria

Self-Paced MOOCs

MOOC List Coupon Discount

Providers and Categories

University / Entity

Instructor

Country

Language

Type of Certificate

Tag

Self-Paced MOOCs

MOOC List Coupon Discount

Providers and Categories

University / Entity

Instructor

Country

Language

Type of Certificate

Tag

“MOOC List” is an aggregator (directory) of Massive Open Online Courses (MOOCs) from different providers.

For more information please see our FAQs.

Terms / Privacy Policy | Contact Us

For more information please see our FAQs.

Terms / Privacy Policy | Contact Us

- MOOC stands for a Massive Open Online Course.
- It is an online course aimed at large-scale participation and open (free) access via the internet.
- They are similar to university courses, but do not tend to offer academic credit.
- A number of web-based platforms (providers Aka initiatives) supported by top universities and colleges offer MOOCs in a wide range of subjects.

MOOCs – Massive Open Online Courses – enable students around the world to take university courses online. This guide, by the instructors of edX’s most successful MOOC in 2013-2014, Principles of Written English (based on both enrollments and rate of completion), advises current and future students how to get the most out of their online study, covering areas such as what types of courses are offered and who offers them, what resources students need, how to register, how to work effectively with other students, how to interact with professors and staff, and how to handle assignments. This second edition offers a new chapter on how to stay motivated. This book is suitable for both native and non-native speakers of English, and is applicable to MOOC classes on any subject (and indeed, for just about any type of online study).