1. Coursera

• 吳恩達(dá) Coursera Machine Learning
  無(wú)需翻墻典徊，可購(gòu)買(mǎi)證書(shū)，不買(mǎi)也能聽(tīng)
• Coursera的其他課程現(xiàn)在好像都要收費(fèi)了，有free trial，也可以申請(qǐng)financial aid

2. edX

• edX 目前不收費(fèi)勺馆，如果需要購(gòu)買(mǎi)證書(shū)也需要付費(fèi)咐鹤，一般在400-600RMB不等
• edX上不僅有大學(xué)出品的課程拗秘，還有Microsoft等公司出品的課程
• MIT Introduction to computational thinking and data science
  雖然名字里有data science, 歸類(lèi)是computer science
  課程在Youtube上也有
  每周需要15hours
  部分課程簡(jiǎn)介：
  This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course.
  Topics covered include:
  Advanced programming in Python 3
  Knapsack problem, Graphs and graph optimization
  Dynamic programming
  Plotting with the pylab package
  Random walks
  Probability, Distributions
  Monte Carlo simulations
  Curve fitting
  Statistical fallacies
  總的來(lái)說(shuō)，這個(gè)課程對(duì)python要求較高祈惶，如果不達(dá)標(biāo)雕旨，請(qǐng)補(bǔ)前課
• MIT Introduction to Computer Science and Programming Using Python
  也就是上門(mén)課的前一門(mén)，教如何使用python, 也涉及部分算法和數(shù)據(jù)結(jié)構(gòu)捧请，同樣是每周15hours凡涩，同樣在Youtube上有
• 如果上面兩門(mén)太難了：
  Microsoft Introduction to Python for Data Science
  課程的syllabus如下：
  Explore Python language fundamentals, including basic syntax, variables, and types
  Create and manipulate regular Python lists
  Use functions and import packages
  Build Numpy arrays, and perform interesting calculations
  Create and customize plots on real data
  Supercharge your scripts with control flow, and get to know the Pandas DataFrame
  總的來(lái)說(shuō)，就是教你用python處理數(shù)據(jù)
  證書(shū)比較貴疹蛉，要657CNY（上面兩個(gè)是300多/each）
• Columbia Statistical Thinking for Data Science and Analytics
  課程內(nèi)容：
  Data collection, analysis and inference
  Data classification to identify key traits and customers
  Conditional Probability-How to judge the probability of an event, based on certain conditions
  How to use Bayesian modeling and inference for forecasting and studying public opinion
  Basics of Linear Regression
  Data Visualization: How to create use data to create compelling graphics
  偏重統(tǒng)計(jì)活箕，課程也涉及了應(yīng)用（也是657= =）
• Columbia Machine Learning for Data Science and Analytics
  同樣來(lái)自Columbia大學(xué)，和上面那門(mén)一看就很像可款，這門(mén)課目前評(píng)價(jià)不高
• 除此之外育韩，edX上還有不少好課克蚂，比如Harvard的Justice，如果你需要拿個(gè)證書(shū)的話......

3. Udacity（待補(bǔ)充）

• 目前接觸的比較少筋讨，似乎有data anlytics的納米學(xué)位埃叭？

4. Youtube

• 油管上還有很多其他的好課，以及Open Course比如Yale Open Course和MIT Open Course
  列舉幾個(gè)：
• ISLR
• MIT introduction to psychology

5.以及MIT Open Course（Sorry, 只發(fā)現(xiàn)了這一個(gè)Open Course系統(tǒng)）

• 里面有相當(dāng)多的課悉罕，有些需要下載專(zhuān)用軟件
• MIT Statistical Thinking and Data Analysis
  This course follows the main outline of the course textbook very closely, skipping over various parts:
  Tamhane, Ajit C., and Dorothy D. Dunlop.
  Statistics and Data Analysis: From Elementary to Intermediate. Prentice Hall, 1999. ISBN: 9780137444267.
  This is an introductory statistics class, assuming probability as a prerequisite. We will review probability (Chapter 2), discuss sampling techniques (Chapter 3), data summarization (Chapter 4), common sampling distributions (Chapter 5), statistical inference and hypothesis testing (Chapters 6-9), regression (Chapters 10-11), and nonparametric inference (Chapter 14).