一些我推荐的和想上的网络课程(Coursera, edX, Udacity)

从面向找工作的角度出发，我觉得以下课程有很大帮助：

首推Robert Sedgewick，也是我觉得对我帮助最大的老师，讲课特点是能把复杂的算法讲解清楚（典型例子：红黑树，KMP算法）

他在Coursera有四门课，循序渐进，也越来越理论，尤其是前三门，非常值得一上。个人认为上完前两门，你的理论基础（当然还要结合刷题的实践）已经可以虐普遍的小公司和大部分的大公司了。上完第三门可以虐一流公司如Google，Facebook，Linkedin等。第四门还没开，不过看过课程介绍，觉得上完可以去当大公司的算法工程师了。

下面列出这四门课：

Algorithms, Part I  内容：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  内容：Undirected Graphs，Directed Graphs，Minimum Spanning Trees，Shortest
Paths，Maximum Flow，String Sorts，Tries，Substring Search，Regular Expressions，Data Compression，Reductions，Linear Programming，Intractability     唯一的遗憾就是没有讲Dynamic Programming

Analysis of Algorithms  内容：Analysis of Algorithms，Recurrences，Solving recurrences with GFs，Asymptotics，The
symbolic method，Trees，Permutations，Strings and Tries，Words and Mappings  也是非常干货的一门课！

Analytic Combinatorics  内容请参考连接，感觉已经非常理论了。

然后我想上的课有:

Stanford的Machine Learning：https://www.coursera.org/course/ml

Functional Programming Principles in Scala  https://www.coursera.org/course/progfun

Principles of Computing  https://www.coursera.org/course/principlescomputing

Programming Cloud Services for Android Handheld Systems  https://www.coursera.org/course/mobilecloud 
云

Algorithmic Thinking  https://www.coursera.org/course/algorithmicthink

機器學習基石 (Machine Learning Foundations)  https://www.coursera.org/course/ntumlone   试试台湾大学的课程

程序设计实习 / Practice on Programming  https://www.coursera.org/course/pkupop    前半部分都是介绍C++比较无趣，后半部分讲算法。另外一个优点就是用POJ平台！

Web Intelligence and Big Data  https://www.coursera.org/course/bigdata   大数据

The Hardware/Software Interface  https://www.coursera.org/course/hwswinterface   其实就是CMU的15213，但据说讲的比CMU还好

Machine Learning   https://www.coursera.org/course/machlearning

Introduction to Data Science  https://www.coursera.org/course/datasci

Introduction to Recommender Systems  https://www.coursera.org/course/recsys   感觉非常有意思的一门课，能做出像Amazon一样的推荐系统~

Web Application https://www.coursera.org/course/webapplications

Software as a Service  https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-cs169-1x-software-service-1136

HTML5 Game Development   https://www.udacity.com/course/cs255   感觉是个挺有意思的项目

Software Testinghttps://www.udacity.com/course/cs258   了解一些Test是做什么的

Software Debugging   https://www.udacity.com/course/cs259    同上Debug

Programming Languages   https://www.udacity.com/course/cs262

Design of Computer Programs   https://www.udacity.com/course/cs212

Discrete Mathematics in Computer Science  http://www.math.dartmouth.edu/archive/m19w03/public_html/book.html

Stanford系列：

https://practicalunix.org/

http://callbackjs.me/

http://www.stanford.edu/class/cs101/

http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=IntroToAlgorithms

http://db.class.stanford.edu

MIT系列：

Introduction to Algorithm:

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/lecture-videos/

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005/video-lectures/

Mathematics for Computer Science

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/video-lectures/

Advanced Data Structures

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-851-advanced-data-structures-spring-2012/lecture-videos/

Computer System Engineering

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-033-computer-system-engineering-spring-2009/video-lectures/

Multicore Programming Primer

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-189-multicore-programming-primer-january-iap-2007/lecture-notes-and-video/

组合数学：http://v.ku6.com/playlist/index_2489333.html

图论：    http://v.ku6.com/playlist/index_3735438.html

初等数论：http://v.ku6.com/playlist/index_2489323.html

Distributed System (KTH)

http://www.ict.kth.se/courses/ID2203/readings.html

http://www.semantikoz.com/blog/9-free-online-data-science-courses/

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

Introduction
to 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

Data
Science Academy

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

School
of Data

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.”

Blogged
Data Science Course

You can read through the blog of Columbia’s fall 2012 data science course if you can not wait for Coursera in April 2013. The blog posts are very detailed and worthwhile reading if you are new to the field or want to get a broad view of it.

Free
Book: An Introduction to Data Science

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

Coursera

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.

Caltech

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

Introduction
to Infographics and Data Visualization

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

Statistical
Computing

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.

http://bigdatauniversity.com/

http://www.edureka.in/blog/install-apache-hadoop-cluster/

本list将保持不断更新。。。