Data Structures

 

 


 

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Dec 5th 2016

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).

Average: 7.4 (9 votes)
Dec 5th 2016

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.

Average: 9 (4 votes)
Dec 5th 2016

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.

Average: 6.7 (7 votes)
Dec 5th 2016

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.

Average: 6.4 (10 votes)
Dec 5th 2016

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.

Average: 6.4 (10 votes)
Dec 5th 2016

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).

Average: 5 (1 vote)
Dec 5th 2016

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.

Average: 6.3 (12 votes)
Dec 5th 2016

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.

Average: 6.3 (4 votes)
Dec 5th 2016

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.

Average: 5.5 (8 votes)
Nov 28th 2016

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis.

Average: 4.6 (9 votes)
Nov 28th 2016

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.

Average: 7.3 (12 votes)
Nov 14th 2016

这门课程将帮助学生学习如何运用高级的数据结构和相关算法解决复杂的应用问题。

Average: 3 (1 vote)
Nov 14th 2016

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。

Average: 4.5 (2 votes)
Oct 24th 2016

Learn about repetition statements, data structures, methods and recursion in Java, as you prepare for the AP Computer Science A exam. In this computer science course, you will learn the basics of programming in the Java language, and cover topics relevant to the AP Computer Science A course and exam. This course will cover repetition statements (for, while, do-while and for-each), the array data structure, methods and recursion.

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Sep 26th 2016

Functional programming is a programming paradigm which is rapidly attracting interest from a broad range of developers because it allows to write expressive, concise and elegant programs. In this course you will discover the power of Functional Programming, using the OCaml language to write concise, efficient and elegant programs.

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Sep 20th 2016

Learn how to structure and use algorithms to solve real life problems. Algorithms power the biggest web companies and the most promising startups. Interviews at tech companies start with questions that probe for good algorithm thinking.

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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.

Average: 7.3 (3 votes)
Sep 19th 2016

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

Average: 5.1 (10 votes)
Self Paced

This is CS50 AP, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for students in high school, which satisfies the College Board's new AP CS Principles curriculum framework.

Average: 4.2 (6 votes)
Aug 30th 2016

A new and updated introduction to computer science as a tool to solve real-world analytical problems using Python 3.5

Average: 3.2 (5 votes)

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