轉載自 http://blog.csdn.net/fightforyourdream/article/details/21314929

感覺是很好的CS 網上公開課總結，在終身學習時代，好好利用網路學習資源對於個人成長和發展非常重要。

從面向找工作的角度出發，我覺得以下課程有很大幫助：

首推Robert Sedgewick，也是我覺得對我幫助最大的老師，講課特點是能把複雜的演算法講解清楚（典型例子：紅黑樹，KMP演算法）

他在Coursera有四門課，循序漸進，也越來越理論，尤其是前三門，非常值得一上。個人認為上完前兩門，你的理論基礎（當然還要結合刷題的實踐）已經可以虐普遍的小公司和大部分的大公司了。上完第三門可以虐一流公司如Google，Facebook，Linkedin等。第四門還沒開，不過看過課程介紹，覺得上完可以去當大公司的演算法工程師了。

下面列出這四門課：

Algorithms, Part I  內容：Union-Find，Analysis of Algorithms，Stacks and Queues，Elementary Sorts，Mergesort，Quicksort，Priority Queues，Elementary Symbol Tables，Balanced Search Trees，Geometric Applications of BSTs，Hash Tables

Algorithms, Part II  內容：Undirected Graphs，Directed Graphs，Minimum Spanning Trees，Shortest Paths，Maximum Flow，String Sorts，Tries，Substring Search，Regular Expressions，Data Compression，Reductions，Linear Programming，Intractability     唯一的遺憾就是沒有講Dynamic Programming

Analysis of Algorithms  內容：Analysis of Algorithms，Recurrences，Solving recurrences with GFs，Asymptotics，The symbolic method，Trees，Permutations，Strings and Tries，Words and Mappings  也是非常乾貨的一門課！

Intro to Data Science

然後我想上的課有:

程式設計實習 / Practice on Programming  https://www.coursera.org/course/pkupop    前半部分都是介紹C++比較無趣，後半部分講演算法。另外一個優點就是用POJ平臺！（有課）

Introduction to Recommender Systems  https://www.coursera.org/course/recsys  感覺非常有意思的一門課，能做出像Amazon一樣的推薦系統~   （watchlist）

Intro to Parallel Programming

TsinghuaX: 60240013X 組合數學

Stanford系列：

MIT系列：

Introduction to Algorithm:

Advanced Data Structures

Computer System Engineering

Multicore Programming Primer

Python:

Distributed System (KTH)

Data Science is a hot topic and there are plenty of courses and resources available for anyone interested. Try out these 9 free resources to get started if you are new to the topic or want to refresh on one of the subjects.

Data Science

A Coursera course specifically about data science, due to start in April 2013. I am very curious about it since its broad syllabus appears to capture many of the experiences data scientists need. Much of it had to be gathered in the field until now. Having a dedicated course for it is an appealing idea.
Course Syllabus – Specific Topics

• Data modeling: relations, key-value, trees, graphs, images, text
• Relational algebra and parallel query processing
• NoSQL systems, key-value stores
• Tradeoffs of SQL, NoSQL, and NewSQL systems
• Algorithm design in Hadoop (and MapReduce in general)
• Basic statistical analysis at scale: sampling, regression
• Introduction to data mining: clustering, association rules, decision trees
• Case studies in analytics: social networking, bioinformatics, text processing

The academy is due to start early 2013 with some interesting workshops:

• Dive into Cloudera Impala
• NumPy for Data Scientists
• Couchbase for Data Scientists
• MapReduce Algorithm Design
• Integrating SAP HANA with R
• Scikit-learn: Machine Learning with Python

The School of Data recently started with their first course, Data Fundamentals. It is a great starting point for anyone interested in (big) data and data science and lays the foundations for more serious work.

“The mission of the School of Data is to promote data literacy and data ‘wrangling’ skills – the ability to find, clean, retrieve, manipulate, analyse, interpret and represent different types of data – across the world. The more people who have the skills to understand and work with data effectively, the greater its value and impact, and the more likely it is that data will be able to bring about positive social benefits.”

This free book is available under a Creative Commons licence. So download it and read it for free. It utilises R and lots of examples to introduce the topic.

Machine Learning

Data Science and machine learning are tightly related and should be of interest to any data science enthusiast. The Coursera machine learning course by Stanford Associate Professor Andrew Ng comes highly recommended to anyone interested in a solid introduction into machine learning with a hands-on approach, and great lecture material and videos.

The California Institute of Technology ran a free online machine learning course with video lectures earlier in 2012. The lectures are still online for anyone to watch and another course will start in January 2013.

Visualisation

An important aspect of data science can be data visualisation. The best analytics and models are not effective if the information and insight gained can not be easily and transparently shared with your client, consumer, or customer. The Knight Center is running their second massive open online course early 2013 about infographics and data visualisation.

Statistics

Statistics and data analysis are, of course, the bread and butter of data science. This fall 2012 Carnegie Mellon University course is not as fancy as Coursera one. In fact, it is little more than a page with all the lecture slides, homework, lab sheets and solutions. But it is free and comprehensive so give it a try.

Update

I know I wrote 9 resources but as I come across something good I might just append it here to the end.

Try R

This is a fun way to get started with R. It is a web site that teaches you, interactively, R. Not much more to say than give it a go.

Wiki Books

Head over to Wiki Books to read ‘Data Science: An Introduction‘. There is already some signifcant material. Nevertheless, it is a work in progress and you can contribute.

Nearly complete is ‘Statistics‘ a book, you guessed it, about statistics.

Python, OpenStack, Docker

感覺coursera的很多課都受制於開課時間，Udacity到時時間很靈活，如果選非付費版也可以看所有的視訊和做作業，除了不能做專案罷了，還是挺不錯的。

本list將保持不斷更新。。。