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dataScienceResources

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数据科学相关的学习资源，例如MOOC课程，OCW课程，电子书等等。

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# 说明
目前这里收集的资源主要是Coursera和edX上面免费的MOOC公开课程。我只上过其中的一部分课程，大部分课程我没上过，无法评价质量，仅作整理。

注明了“推荐”的课程，是我自己上过觉得质量比较高的课程，或者是群友中口碑比较好的课程。不过要注意没注明推荐，又没有评论的课程，可能只是大家都没人上过而已，未必代表质量不好。欢迎大家尝试提交反馈。

如果大家知道有其他Data Science方面好的学习资源(MOOC，非MOOC均可)，欢迎附上说明和链接，提交过来。不过请尊重相关的知识产权，不要提交盗版资源下载链接。

# MOOC课程资源
## 编程基础类
### Programming for Everybody (Python)
**提供学校** University of Michigan  
**开课时间** 6.1  
**持续时间** 11周  
**课程地址** https://www.coursera.org/course/pythonlearn  
**课程介绍**  
This course aims to teach everyone to learn the basics of programming computers using Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course.

### Introduction to Computer Science and Programming Using Python
**提供学校** MIT  
**开课时间** 6.10  
**持续时间** 9周  
**课程地址** https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-0  
**课程介绍**  
6.00.1x is an introduction to computer science as a tool to solve real-world analytical problems.

### Introduction to Programming with MATLAB
**提供学校** Vanderbilt University  
**开课时间** 7.1  
**持续时间** 9周  
**课程地址** https://www.coursera.org/course/matlab  
**课程介绍**  
This course teaches computer programming to those with little to no previous experience. It uses the programming system and language called MATLAB to do so because it is easy to learn, versatile and very useful for engineers and other professionals.

### An Introduction to Interactive Programming in Python Part 1
**提供学校** Rice  
**开课时间** 7.11  
**持续时间** 5周  
**课程地址** https://www.coursera.org/course/interactivepython1  
**课程介绍**  
This two-part course is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids.

### An Introduction to Interactive Programming in Python Part 2
**提供学校** Rice  
**开课时间** 7.11  
**持续时间** 5周  
**课程地址** https://www.coursera.org/course/interactivepython2  
**课程介绍**  
This two-part course is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids.

### Principles of Computing (Part 1)
**提供学校** Rice  
**开课时间** 5.22  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/principlescomputing1  
**课程介绍**  
This two-part course introduces the basic mathematical and programming principles that underlie much of Computer Science. Students will refine their programming skills as well as learn the basics of creating efficient solutions to common computational problems.

### Algorithmic Thinking Part 2
**提供学校** Rice  
**开课时间** 7.11  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/algorithmicthink1  
**课程介绍**  
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

## 数据库基础
### Querying with Transact-SQL
**提供学校** Microsoft  
**开课时间** 4.14  
**持续时间** 4周  
**课程地址** https://www.edx.org/course/querying-transact-sql-microsoft-dat201x#.VRTwpDGUefU  
**课程介绍**  
Transact-SQL is an essential skill for database professionals and developers working with SQL databases. This course takes you from your first SELECT statement to implementing transactional programmatic logic through a combination of expert instruction, demonstrations, and practical labs.

## 数理基础类
### Mathematical Methods for Quantitative Finance
**提供学校** University of Washinton  
**开课时间** 6.1  
**持续时间** 8  
**课程地址** https://www.coursera.org/course/mathematicalmethods  
**课程介绍**  
Mathematical Methods for Quantitative Finance covers topics from calculus and linear algebra that are fundamental for the study of mathematical finance. Students successfully completing this course will be mathematically well prepared to study quantitative finance at the graduate level.

### Calculus 1A: Differentiation
**提供学校** MIT  
**开课时间** 6.2  
**持续时间** 13周  
**课程地址** https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x  
**课程介绍**  
Discover the derivative---what it is, how to compute it, and when to apply it in solving real world problems. Part 1 of 3.

### Calculus: Single Variable(Self-Study)
**提供学校** The Ohio State University  
**开课时间** Self-Study  
**持续时间** Self-Study  
**课程地址** https://www.coursera.org/learn/calculus1  
**课程介绍**  
This course is a first and friendly introduction to calculus, suitable for someone who has never seen the subject before, or for someone who has seen some calculus but wants to review the concepts and practice applying those concepts to solve problems.

### Calculus Two: Sequences and Series(Self-Study)
**提供学校** The Ohio State University  
**开课时间** Self-Study  
**持续时间** Self-Study  
**课程地址**  
英文版：https://www.coursera.org/learn/advanced-calculus  
中文版：https://www.coursera.org/learn/gaojie-wei-jifen  
**课程介绍**  
Calculus Two: Sequences and Series is an introduction to sequences, infinite series, convergence tests, and Taylor series.  The course emphasizes not just getting answers, but asking the question "why is this true?"

### Applications of Linear Algebra Part 1
**提供学校** DavidsonX  
**开课时间** 2.23  
**持续时间** 5周  
**课程地址** https://www.edx.org/course/applications-linear-algebra-part-1-davidsonx-d003x-1#.VRTxnzGUefU  
**课程介绍**  
Learn to use linear algebra in computer graphics by making images disappear in an animation or creating a mosaic or fractal and in data mining to measure similarities between movies, songs, or friends.

### Applications of Linear Algebra Part 2
**提供学校** DavidsonX  
**开课时间** 4.6  
**持续时间** 4周  
**课程地址** https://www.edx.org/course/applications-linear-algebra-part-2-davidsonx-d003x-2#.VRTuXDGUefU  
**课程介绍**  
Explore applications of linear algebra in the field of data mining by learning fundamentals of search engines, clustering movies into genres and of computer graphics by posterizing an image.

### Linear Algebra - Foundations to Frontiers
**提供学校** The University of Texas at Austin  
**开课时间** 6.3  
**持续时间** 15周  
**课程地址** https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-03x  
**课程介绍**  
Learn the mathematics behind linear algebra and link it to matrix software development.

### Probability
**提供学校** University of Pennsylvania  
**开课时间** 3.2  
**持续时间** 8周  
**课程地址** https://www.coursera.org/course/probability  
**课程介绍**  
How should we interpret chance around us? Watch beautiful mathematical ideas emerge in a glorious historical tapestry as we discover key concepts in probability, perhaps as they might first have been unearthed, and illustrate their sway with vibrant applications taken from history and the world around.

## 统计模型
### Data Analysis and Statistical Inference（推荐）
**提供学校** Duke  
**开课时间** 3.2  
**持续时间** 10周  
**课程地址** https://www.coursera.org/course/statistics  
**课程介绍**  
This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

**个人评价**  
**优点:**制作非常有诚意的统计入门课程，最大限度的回避了数学推理细节，非常强调直观理解和实际应用，同时会辅助R语言做计算实验以让学生更好的体会统计原理。没接触过统计的文科生应该也能顺利跟下来，学过统计的理科生跟着复习一遍也会有不少收获。讲解到位又没多余废话，做到这点老师肯定是花了大力气反复推敲过的。Project会让学生用R-Markdown写一个分析报告，分析本身不难，不过是个熟悉写数据分析报告流程和规范的好机会。  
**缺点：**有些不东西完全不用数学推理真的是没法讲清楚的，所以这门课有些结论只能给出结论但不解释了。R语言只会用到为了帮助课程理解，以及写报告所需要的最低限度知识，只会接触到皮毛，所以别指望通过这门课熟悉R语言。完整的点评见：http://www.moozhi.com/review/show/545cd166d27be66d39f39662

### Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation
**提供学校** Johns Hopkins  
**开课时间** 6.1  
**持续时间** 8周  
**课程地址** https://www.coursera.org/course/statreasoning  
**课程介绍**  
A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.

### I "Heart" Stats: Learning to Love Statistics
**提供学校** NotreDame  
**开课时间** 4.15  
**持续时间** 9周  
**课程地址** https://www.edx.org/course/i-heart-stats-learning-love-statistics-notredamex-soc120x#.VRfqGjGUefU  
**课程介绍**  
When you meet a new person, it is hard to know what to expect.  You may not be able to read the person or understand what they mean. Even if you want to have a good relationship with them, this lack of understanding can make interactions tense, unpredictable and scary! The same is true for a lot of people as they encounter statistics and mathematical ways of working with data.  Statistics can be confusing and opaque.  Symbols, Greek letters, very large and very small numbers, and how to interpret all of this can leave to feeling cold and disengaged—even fearful and resentful.

### Applied Regression Analysis
**提供学校** The Ohio State University  
**开课时间** 3.23  
**持续时间** 6周  
**课程地址** https://www.coursera.org/course/appliedregression  
**课程介绍**  
Regression modeling is the standard method for analysis of continuous response data. This course provides theoretical and practical training in statistical modeling with particular emphasis on linear and multiple regression.

### Applied Logistic Regression
**提供学校** The Ohio State University  
**开课时间** 5.11  
**持续时间** 8周  
**课程地址** https://www.coursera.org/course/logisticregression  
**课程介绍**  
This course provides theoretical and practical training on the increasingly popular logistic regression model, which has become the standard analytical method for use with a binary response variable.

### Mathematical Biostatistics Boot Camp 1
**提供学校** John Hopkins  
**开课时间** 7.13  
**持续时间** 7周  
**课程地址** https://www.coursera.org/course/biostats  
**课程介绍**  
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

### Mathematical Biostatistics Boot Camp 2
**提供学校** John Hopkins  
**开课时间** 5.18  
**持续时间** 7周  
**课程地址** https://www.coursera.org/course/biostats2  
**课程介绍**  
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

### Introduction to Statistics for the Social Sciences
**提供学校** University of Zurich  
**开课时间** 4.21  
**持续时间** 11周  
**课程地址** https://www.coursera.org/course/statistics101  
**课程介绍**  
This course exposes students to the logic of statistical reasoning and its application in the quantitative social sciences. It is meant as a thorough but accessible introduction to the topics of descriptive statistics, probability theory, and statistical inference with hands-on exercises.

## 数据挖掘
### Cluster Analysis in Data Mining
**提供学校** University of Illinois at Urbana-Champaign  
**开课时间** 4.27  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/clusteranalysis  
**课程介绍**  
Learn how to take scattered data and organize it into groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks.

### Text Mining and Analytics
**提供学校** University of Illinois at Urbana-Champaign  
**开课时间** 6.8  
**持续时间** 4  
**课程地址** https://www.coursera.org/course/textanalytics  
**课程介绍**  
Explore algorithms for mining and analyzing big text data to discover interesting patterns, extract useful knowledge, and support decision making.

### 机器学习基石（推荐，长期开放）
**提供学校** 台湾大学  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/ntumlone  
**课程介绍**  
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know.

评价：这门课是林轩田教授在MOOC上提供机器学习课程上半部分。上半部分介绍了机器学习技术中的一些基本理论框架，以及几个基本算法，同时也传授了应用机器学习算法的一些实用心得技巧。上半部分实用的算法介绍并不多，主要还是为了下半部分机器学习技法打基础。

台湾大学林轩田教授指导的学生队伍曾经多次在数据挖掘大赛KDD Cup夺得冠军，而且林教授是抱着要比实体课程做的更好的态度制作这个系列的MOOC课程。因此这门课体验除了非常高的诚意和品质，好好消化会有非常大的收获。

参加这门课需要至少熟悉一门编程语言。所需的数学知识方面并不是很高深，基本的微积分，线性代数和概率知识足以应付。但是理解课程概念，和完成作业也要花费相当的脑筋和耐心，还是有相当大的强度的。如果要参加请预留出充分的时间。

### 机器学习技法（推荐，长期开放）
**提供学校** 台湾大学  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/ntumltwo  
**课程介绍**  
The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features.

评价：这门课是林轩田教授在MOOC上提供机器学习课程下半部分。在上半部分基础算法的基础上，通过往三个方向的改进，引出了一系列实用强大的机器学习算法，弥补了上半部分对算法介绍的不足。

### Machine Learning（长期开放）
**提供学校** University of Washington  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/machlearning  
**课程介绍**  
Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

### Neural Networks for Machine Learning（长期开放）
**提供学校** University of Toronto  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/neuralnets  
**课程介绍**  
Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

## 概率图模型
### Probabilistic Graphical Models(长期开放)
**提供学校** Stanford  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/pgm  
**课程介绍**  
In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

## 推荐系统
### Introduction to Recommender Systems(自学)
**提供学校** University of Minnesota  
**开课时间** Self-Study  
**持续时间** Self-Study  
**课程地址** https://www.coursera.org/learn/recommender-systems  
**课程介绍**  
Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he has never experienced. The technology behind recommender systems has evolved over the past 20 years into a rich collection of tools that enable the practitioner or researcher to develop effective recommenders. We will study the most important of those tools, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses in practice.

## NLP
### Natural Language Processing(长期开放)
**提供学校** Columbia University  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/nlangp  
**课程介绍**  
Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.

## 大规模数据处理
### Introduction to Big Data with Apache Spark
**提供学校** UC Berkeley  
**开课时间** 6.1  
**持续时间** 5周  
**课程地址** https://www.edx.org/course/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x  
**课程介绍**  
Organizations use their data for decision support and to build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term Data Science. This course will attempt to articulate the expected output of Data Scientists and then teach students how to use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments include Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.

### Scalable Machine Learning
**提供学校** UC Berkeley  
**开课时间** 6.29  
**持续时间** 5周  
**课程地址** https://www.edx.org/course/scalable-machine-learning-uc-berkeleyx-cs190-1x  
**课程介绍**  
Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark.

### Web Intelligence and Big Data(长期开放)
**提供学校** Indian Institute of Technology Delhi  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/bigdata  
**课程介绍**  
This course is about building 'web-intelligence' applications exploiting big data sources arising social media, mobile devices and sensors, using new big-data platforms based on the 'map-reduce' parallel programming paradigm. In the past, this course has been offered at the Indian Institute of Technology Delhi as well as the Indraprastha Institute of Information Technology Delhi.

## 综合入门
### Data Science Specialization (8.3)
**提供学校** Johns Hopkins University  
**开课时间** 5.4  
**持续时间** 4周x9  
**课程地址** https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage  
**课程介绍**  
In this course you will learn:
Formulate context-relevant questions and hypotheses to drive data scientific research
Identify, obtain, and transform a data set to make it suitable for the production of statistical evidence communicated in written form. Build models based on new data types, experimental design, and statistical inference  
**我的评价**  
**优点：**这个系列比较完整的展现了用R语言从数据收集，数据处理，一直到数据可视化或是写分析报告所需要的各种工作以及R中相应的package用法。可以作为一个不错的R学习Pathway。编程作业的设计也还不错，跟着做一下能学到不少有用的技能。  
**缺点：**用视频课的形式讲Package用法本身就是非常低效的方式，有强烈的干念PPT的无聊感，而且只能点到即止。如果要真的掌握那些Package，需要课后好好研究通过链接给出的课外资料。相比较其他课程，这个系列对于数据分析理论的讲解我只能用惨不忍睹来形容。学过相关知识的人用来复习还行，如果是初学者那基本会听得云里雾里。  
**总结**好好跟一遍，可以了解数据分析的流程，以及各个环节有哪些R的package可用，虽然只能算入门，但收获还是不少的。至于数据分析理论部分，如果听不懂，那多半不是你的错，建议用其他同类课程补充或替代。此外这系列的课并不算编程入门课，零编程经验来跟这门课可能会比较痛苦。建议无编程经验的同学先去学一些基础的编程课训练一下基本的编程能力。

### Introduction to Computational Thinking and Data Science（推荐）
**提供学校** MIT  
**开课时间** 3.4  
**持续时间** 9  
**课程地址** https://www.edx.org/course/introduction-computational-thinking-data-mitx-6-00-2x-0#.VPR5xDGUefU  
**课程介绍**  
6.00.2x is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. We have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics, so that they will have an idea of what’s possible when the time comes later in their career to think about how to use computation to accomplish some goal. That said, it is not a “computation appreciation” course. Students will spend a considerable amount of time writing programs to implement the concepts covered in the course. Topics covered include plotting, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization problems, and clustering.

### Introduction to Data Science(长期开放)
**提供学校** University of Washington  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/datasci  
**课程介绍**  
Join the data revolution. Companies are searching for data scientists. This specialized field demands multiple skills not easy to obtain through conventional curricula. Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data.

### The Caltech-JPL Summer School on Big Data Analytics长期开放)
**提供学校** Caltech  
**开课时间** 长期开放  
**持续时间** 长期开放  
**课程地址** https://www.coursera.org/course/bigdataschool  
**课程介绍**  
This is an intensive, advanced summer school (in the sense used by scientists) in some of the methods of computational, data-intensive science. It covers a variety of topics from applied computer science and engineering, and statistics, and it requires a strong background in computing, statistics, and data-intensive research.

## 社会学研究
### Questionnaire Design for Social Surveys
**提供学校** University of Michigan  
**开课时间** 6.1  
**持续时间** 6周  
**课程地址** https://www.coursera.org/course/questionnairedesign  
**课程介绍**  
This course will cover the basic elements of designing and evaluating questionnaires. We will review the process of responding to questions, challenges and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode specific questionnaire characteristics, and review methods of standardized and conversational interviewing.

### Solid Science Research Method (8.31)
**提供学校** University of Amsterdam  
**开课时间** 8.31  
**持续时间** 6周  
**课程地址** https://www.coursera.org/course/solidsciencemethods  
**课程介绍**  
Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research!

## 体育
### Math in Sports
**提供学校** Univerity of Notre Dame  
**开课时间** 6.15  
**持续时间** 8周  
**课程地址** https://www.edx.org/course/math-sports-notredamex-mat150x#!  
**课程介绍**  
Come learn how you can use mathematics to get a deeper insight into both the sports you love and everyday life

### Sabermetrics 101: Introduction to Baseball Analytics
**提供学校** BUx  
**开课时间** 7.7  
**持续时间** 10周  
**课程地址** https://www.edx.org/course/math-sports-notredamex-mat150x#!  
**课程介绍**  
An introduction to sabermetrics, baseball analytics, data science, the R Language, and SQL.

## 商业管理
### The Analytics Edge（推荐）
**提供学校** MIT  
**开课时间** 3.3  
**持续时间** 12周  
**课程地址** https://www.edx.org/course/analytics-edge-mitx-15-071x-0  
**课程介绍**  
Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.
我没有上过这门课，不过很多阅课无数的资深Moocer对这门课的评价都很高。根据道听途说和课程介绍的结果，这门课会比较详细的教你用R语言结合各种真实的案例做数据分析。后期这门课会在Kaggle上发布一个项目让大家练习。

### Quality Engineering & Management
**提供学校** TUMx  
**开课时间** 7.1  
**持续时间** 周  
**课程地址** https://www.edx.org/course/quality-engineering-management-tumx-qemx#!  
**课程介绍**  
Cover the fundamentals for quality engineering and management, applied to the DMAIC (Define, Measure, Analyze, Improve, Control) process-improvement cycle.

### Statistics for Business – I
**提供学校** IIMBx  
**开课时间** 7.7  
**持续时间** 5周  
**课程地址** https://www.edx.org/course/statistics-business-i-iimbx-qm101-1x  
**课程介绍**  
This introduction to statistics course examines data from the perspective of business scenarios and demonstrates how to apply this data to make better decisions.

### Process Mining: Data science in Action
**提供学校** Eindhoven University of Technology  
**开课时间** 4.1  
**持续时间** 6周  
**课程地址** https://www.coursera.org/course/procmin  
**课程介绍**  
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

### An Introduction to Credit Risk Management
**提供学校** Delft  
**开课时间** 4.14  
**持续时间** 8周  
**课程地址** https://www.edx.org/course/introduction-credit-risk-management-delftx-tw3421x  
**课程介绍**  
Imagine that you are a bank and a main part of your daily business is to lend money. Unfortunately, lending money is a risky business - there is no 100% guarantee that you will get all your money back. If the borrower defaults, you will face losses in your portfolio. Or, in a bit less extreme scenario, if the credit quality of your counterparty deteriorates according to some rating system, the loan will become more risky. These are typical situations in which credit risk manifests itself.

## 教育
### Big Data in Education
**提供学校** TeachersCollegeX  
**开课时间** 7.1  
**持续时间** 8周  
**课程地址** https://www.edx.org/course/big-data-education-teacherscollegex-bde1x#!  
**课程介绍**  
Learn how and when to use key methods for educational data mining and learning analytics on large-scale educational data.

## 生物学场景
### Integrated Analysis in Systems Biology
**提供学校** Mount Sinai  
**开课时间** 4.20  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/integratedanalysis  
**课程介绍**  
This course will focus on developing integrative skills through directed reading and analysis of the current primary literature to enable the student to develop the capstone project as the overall final exam for the specialization in systems biology.

### Advanced Statistics for the Life Sciences（推荐）
**提供学校** Harvard X  
**开课时间** 3.2  
**持续时间** 4周  
**课程地址** https://www.edx.org/course/advanced-statistics-life-sciences-harvardx-ph525-3x#.VPNH9DGUefU  
**课程介绍**  
We dive into the best methods for analyzing complex data in the life sciences with a focus on genomics.

来自@Nutastray 的评价：
这个课程是整个基因分析系列的第三章，这个章节的主题是如何从高通量数据中辨识真正的信号，如何纠正p value从而不至于出现大量的false positive，还有些许聚类的内容以及如何画出版级别的heatmap。难度上来讲较第一章和第二章有质的提升，对R的数组操作熟练度有更高要求，并且和之前不同的是这次分析的数据是真正的基因组数据，需要习惯col是sample而row是feature的格式。 
优点：课程的视频比较简短，主要的重点是quiz，或者说大部分的内容是通过quiz来表现的。quiz通过实际操作和探索的方式告诉你为什么这么做，怎么做，以及不同做的方法的区别，让你系统的了解整个知识点。quiz本身不难，也有足够多的机会让你尝试，可以尽情探索。尽管视频比较简单，但是课件（markdown）做的很精良，值得下载收藏。该教研组也提供了一些比较方便的自制package。 
缺点：可能有人又会抱怨数学细节少了，但我们的目标不是学习数学，不是么？就算全日制统计课又有多少深究数学细节的？不过有概率论和线代的知识更能帮助理解内容。

### Finding Hidden Messages in DNA (Bioinformatics I)
**提供学校** University of California, San Diego  
**开课时间** 7.21  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/hiddenmessages  
**课程介绍**  
This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. After warming up our algorithmic muscles, we will learn how randomized algorithms can be used to solve problems in bioinformatics.

### Assembling Genomes and Sequencing Antibiotics (Bioinformatics II) (8.31)
**提供学校** University of California, San Diego  
**开课时间** 8.31  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/assembly  
**课程介绍**  
Biologists still cannot read the nucleotides of an entire genome or the amino acids of an antibiotic as you would read a book from beginning to end. However, they can read short pieces of DNA and weigh small antibiotic fragments. In this course, we will see how graph theory and brute force algorithms can be used to reconstruct genomes and antibiotics.

### Comparing Genes, Proteins, and Genomes (Bioinformatics III)
**提供学校** University of California, San Diego  
**开课时间** 7.21  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/comparinggenomes  
**课程介绍**  
After sequencing genomes, we would like to compare them. We will see that dynamic programming is a powerful algorithmic tool when we compare two genes (i.e., short sequences of DNA) or two proteins. When we "zoom out" to compare entire genomes, we will employ combinatorial algorithms.

### Deciphering Molecular Evolution (Bioinformatics IV) (8.31)
**提供学校** University of California, San Diego  
**开课时间** 8.31  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/molecularevolution  
**课程介绍**  
In this course, we will see how evolutionary trees resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. We will then use methods from computational proteomics to test whether we can reconstruct Tyrannosaurus rex proteins and prove that birds evolved from dinosaurs.

### Introduction to Bioconductor（推荐）
**提供学校** Harvard X  
**开课时间** 3.30  
**持续时间** 4周  
**课程地址** https://www.edx.org/course/introduction-bioconductor-harvardx-ph525-4x#.VPNH9TGUefU  
**课程介绍**  
We will cover some common uses of the software packages within the Bioconductor project. You will get to decide if you learn methods for next generation sequencing, microarrays or both. We will cover a number of normalization, batch correction, and testing methods for high throughput data.

来自@Nutastray 的评价。这个课程是基因分析系列的第四章，介绍了生物信息学一个重要的工具——bioconductor——的基本使用，也是目前我所知唯一一个教授它的课程。不夸张地说，bioconductor是打开生物数据世界大门的钥匙，所以课程的信息量与之前的系列课程相比有质的增长，这个增长从视频的数目就可以一目了然。当然quiz还是比较简单，尝试次数很多，难度也不会太为难。但如果要吃透课程所传授的所有内容，不好好花些时间是不成的，反过来讲，如果只是满足于完成quiz，你只能知道生物信息分析的大致流程，走马观花一下，而无法领略bioconductor的各中精妙。

优点：新世界的大门向你敞开，不解释。 
缺点：个人觉得这些内容合理的授课长度应该是8周而不是4周这么赶。由于时间限制而内容颇多，有些细节生物背景依赖性强（话说有不是生物医学背景在听这个么？），有些函数的使用只是略微带过而以分析思路为主，我还是建议学习者不但完成视频观看，最好还能仔细阅读附带课程材料和函数的帮助档，一定会很有收获。

### Case study: DNA methylation data analysis（推荐）
**提供学校** Harvard  
**开课时间** 4.27  
**持续时间** 2周  
**课程地址** https://www.edx.org/course/case-study-dna-methylation-data-analysis-harvardx-ph525-8x#.VRfrmzGUefU  
**课程介绍**  
Basic workflow for analyzing DNA methylation data.

### Case study: ChIP-seq data analysis（推荐）
**提供学校** Harvard  
**开课时间** 4.27  
**持续时间** 2周  
**课程地址** https://www.edx.org/course/case-study-rna-seq-data-analysis-harvardx-ph525-5x#.VRftLzGUefU  
**课程介绍**  
In the PH525 case studies, we will explore the data analysis of an experimental protocol in depth, using various open source software, including R and Bioconductor. We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment.

### Case study: Variant Discovery and Genotyping（推荐）
**提供学校** Harvard  
**开课时间** 4.27  
**持续时间** 2周  
**课程地址** https://www.edx.org/course/case-study-variant-discovery-genotyping-harvardx-ph525-6x#.VRftYTGUefU  
**课程介绍**  
We will learn the basic steps involved in finding genetic variants in DNA re-sequencing datasets, from read alignment to calling and aggregating variant data across many samples.

### Genomic Data Science (Coursera Specilization) (8.3)
**提供学校** JOHNS HOPKINS  
**开课时间** 8.3  
**持续时间** 4周  
**课程地址** https://www.coursera.org/specialization/genomics/41?utm_medium=courseDescripTop  
**课程介绍**  
This specialization will teach you to understand, analyze, and interpret data from next-generation sequencing experiments. You will learn common tools of genomic data science, including Python, R, Bioconductor, and Galaxy. These courses can serve as a stand-alone introduction to genomic data science or can compliment to a primary degree or postdoc in biology, molecular biology, or genetics. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses.

## 数据可视化
### Data Visualization
**提供学校** University of Illinois at Urbana-Champaign  
**开课时间** 7.20  
**持续时间** 4周  
**课程地址** https://www.coursera.org/course/datavisualization  
**课程介绍**  
Learn how to transform information from a format efficient for computation into a format efficient for human perception, cognition, and communication. Explore elements of computer graphics, human-computer interaction, perceptual psychology, and design in addition to data processing and computation.

## QQ学习交流群
如果大家对数据科学类的MOOC课程感兴趣的话，欢迎加入QQ讨论组339390736(也可以点击链接加入，http://jq.qq.com/?_wv=1027&k=dzgwzU)

群里已有完成各类种数据科学相关MOOC课程的同学近2000人，包括很多已经完成这门课程的同学。群员热心友善，讨论氛围比较好。不管你是菜鸟还是专家，只要是对数据科学感兴趣，我们都非常欢迎你加入。

但是群里有两条规矩希望大家遵守

1.尊重Honor Code。除非任课教授允许，否则禁止在Hard Deadline前讨论分享作业，题目的具体解答。违者依据严重程度会被警告，退群甚至永久加入黑名单处理。(有些Self-study课没有honor code不受此限制，但是其他非Self-study的课请尊重Honor Code)
2.只讨论和数据分析有关的内容，不要迎讨论无关内容。（如果真的有其他方面的交流想和大家分享，可以加到配套的水群）

入群后请将群名片改成"称呼+所在地+行业"的形式

[1]: http://moozhi.qiniudn.com/540569dc34ae6ae65493ce28-1425259683260-1.png?

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