windows仍在设置此类_如果您想学习数据科学，请参加一些此类统计课程

windows仍在设置此类

by David Venturi

大卫·文图里(David Venturi)

如果您想学习数据科学，请参加一些此类统计课程 (If you want to learn Data Science, take a few of these statistics classes)

A year ago, I was a numbers geek with no coding background. After trying an online programming course, I was so inspired that I enrolled in one of the best computer science programs in Canada.

一年前，我是没有编码背景的数字极客。 尝试了在线编程课程后，我受到了极大的启发，以至于我报名参加了加拿大最好的计算机科学程序之一。

Two weeks later, I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. So I dropped out.

两周后，我意识到我可以通过edX，Coursera和Udacity学习所需的一切。 所以我退学了。

The decision was not difficult. I could learn the content I wanted to faster, more efficiently, and for a fraction of the cost.

决定并不困难。 我可以更快，更高效地学习想要的内容，而费用却很少。

I already had a university degree and, perhaps more importantly, I already had the university experience. Paying $30K+ to go back to school seemed irresponsible.

我已经拥有大学学位，也许更重要的是，我已经拥有大学经验。 支付3万美元以上重返学校似乎是不负责任的。

I started creating my own data science master’s degree using online courses shortly afterwards, after realizing it was a better fit for me than computer science. I scoured the introduction to programming landscape. For the first article in this series, I recommended a few coding classes for the beginner data scientist.

不久之后，我开始意识到自己比计算机科学更适合我，然后通过在线课程开始创建自己的数据科学硕士学位 。 我搜寻了编程领域的简介。 对于本系列的第一篇文章 ，我为初学者数据科学家推荐了一些编码类。

If you want to learn Data Science, start with one of these programming classesA comprehensive guide to online intro to programming courses.medium.freecodecamp.com

如果您想学习数据科学，请从以下编程课程之一开始。有关 在线编程课程简介的全面指南。 medium.freecodecamp.com

现在来看统计和概率。 (Now onto statistics and probability.)

I have taken a few courses, and audited portions of many. I know the options out there, and what skills are needed for learners preparing for a data analyst or data scientist role.

我参加了一些课程，并审核了许多课程。 我知道那里的选择，以及学习者准备数据分析师或数据科学家角色需要哪些技能。

For this guide, I spent 15+ hours trying to identify every online intro to statistics and probability course offered as of November 2016, extracting key bits of information from their syllabi and reviews, and compiling their ratings. For this task, I turned to none other than the open source Class Central community and its database of thousands of course ratings and reviews.

对于本指南，我花了15个多小时来尝试确定截至2016年11月提供的每个在线统计和概率论课程介绍，从其课程大纲和评论中提取关键信息，并编制其评分。 对于此任务，我只选择了开放源码的Class Central社区及其包含数千个课程评分和评论的数据库。

Since 2011, Class Central founder Dhawal Shah has kept a closer eye on online courses than arguably anyone else in the world. Dhawal personally helped me assemble this list of resources.

自2011年以来， Class Central的创始人Dhawal Shah一直在关注在线课程，这一点可以说是世界上其他任何人所不及的。 达瓦尔亲自帮助我整理了这份资源清单。

我们如何选择要考虑的课程 (How we picked courses to consider)

Each course must fit four criteria:

每门课程必须符合四个条件：

1. It must be an introductory course with little to no statistics or probability experience required.

   它必须是入门课程，几乎不需要或不需要统计或概率经验。

2. It must be on-demand or offered every few months.

   必须按需或每几个月提供一次。

3. It must be of decent length: at least ten hours in total for estimated completion.

   它必须有适当的长度 ：估计至少需要十个小时才能完成。

4. It must be an interactive online course, so no books or read-only tutorials. Though these are viable ways to learn statistics and probability, this guide focuses on courses.

   它必须是交互式的在线课程，因此没有书籍或只读教程 。 尽管这些是学习统计数据和概率的可行方法，但本指南重点关注课程。

We believe we covered every notable course that fits the above criteria. Since there are seemingly hundreds of courses on Udemy, we chose to consider the most-reviewed and highest-rated ones only. There’s always a chance that we missed something, though. So please let us know in the comments section if we left a good course out.

我们相信，我们涵盖了符合上述条件的所有重要课程。 由于关于Udemy的课程似乎有数百种，因此我们选择只考虑评论次数最多和评分最高的课程。 不过，总有可能我们错过了一些东西。 因此，如果我们留下了好的课程，请在评论部分让我们知道。

我们如何评估课程 (How we evaluated courses)

We compiled average rating and number of reviews from Class Central and other review sites. We calculated a weighted average rating for each course. If a series had multiple courses (like the University of Texas at Austin’s two-part “Foundations of Data Analysis” series), we calculated the weighted average rating across all courses. We read text reviews and used this feedback to supplement the numerical ratings.

我们汇总了Class Central和其他评论网站的平均评分和评论数量。 我们计算了每门课程的加权平均评分。 如果一个系列有多个课程(例如德克萨斯大学奥斯汀分两部分的“数据分析基础”系列)，我们将计算所有课程的加权平均评分。 我们阅读了文字评论，并使用此反馈来补充数字等级。

We made subjective syllabus judgment calls based on three factors:

我们基于以下三个因素进行了主观的课程提纲判断：

1. The degree to which each course teaches statistics through coding up examples — preferably in R or Python.

   每门课程通过编写示例(最好使用R或Python)教统计的程度。

2. Coverage of the fundamentals of probability and statistics. Covering descriptive statistics, inferential statistics, and probability theory is ideal.

   涵盖概率和统计的基础知识。 涵盖描述性统计，推论统计和概率论是理想的。

3. How much of the syllabus is relevant to data science? Does the syllabus have specialized content like genomics, as several biostatistics courses do? Does the syllabus cover advanced concepts not often used in data science?

   课程大纲中有多少与数据科学相关？ 教学大纲是否像某些生物统计学课程一样具有专门的内容，如基因组学？ 教学大纲是否涵盖了数据科学中不经常使用的高级概念？

为什么要进行目标编码？ (Why Target Coding?)

William Chen, a data scientist at Quora who has a master’s in Applied Mathematics from Harvard, wrote the following in this popular Quora answer to the question: “How do I learn statistics for data science?”

Quora的数据科学家，拥有哈佛大学应用数学硕士学位的William Chen在这个颇受欢迎的Quora回答中写道：“我如何学习数据科学统计学？”

For any aspiring data scientist, I would highly recommend learning statistics with a heavy focus on coding up examples, preferably in Python or R.

对于任何有抱负的数据科学家，我强烈建议学习统计学，重点是编写示例，最好使用Python或R。

Since a lot of a data scientist’s statistical work is carried out with code, getting familiar with the most popular tools is beneficial.

由于许多数据科学家的统计工作都是通过代码进行的，因此熟悉最受欢迎的工具将是有益的。

统计与概率 (Statistics AND Probability)

Probability is not statistics and vice versa. My favorite explanation of their differences is from Stony Brook University:

概率不是统计数字，反之亦然。 关于它们之间的区别，我最喜欢的解释是来自石溪大学：

Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.

概率用于预测未来事件的可能性，而统计涉及对过去事件发生频率的分析。

They explain that “probability is primarily a theoretical branch of mathematics, which studies the consequences of mathematical definitions,” while “statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world.”

他们解释说，“概率主要是数学的理论分支，研究数学定义的后果”，而“统计主要是数学的应用分支，试图使现实世界中的观察有意义。”

Statistics is generally regarded as one of the pillars of data science. Probability — though it generates less attention — is also an important part of a data science curriculum.

统计学通常被认为是数据科学的Struts之一 。 概率(尽管它引起的关注较少)也是数据科学课程的重要组成部分。

Joe Blitzstein, a Professor in the Harvard Statistics Department, stated in this popular Quora answer that aspiring data scientists should have a good foundation in probability theory as well.

哈佛大学统计系教授乔·布利兹斯坦(Joe Blitzstein)在这个颇受欢迎的Quora回答中指出，有抱负的数据科学家也应在概率论上有良好的基础。

Justin Rising, a data scientist with a Ph.D. in statistics from Wharton, clarified that this “good foundation” means being comfortable with undergraduate level probability.

数据科学家贾斯汀·瑞辛(Justin Rising) 沃顿商学院(Wharton)的统计数据表明，“良好的基础”意味着对本科水平的概率感到满意。

我们为数据科学家选择的最佳统计和概率课程是…… (Our picks for the best statistics and probability courses for data scientists are…)

• Foundations of Data Analysis — Part 1: Statistics Using R by the University of Texas at Austin (edX)

  数据分析的基础—第1部分 ：德克萨斯大学奥斯汀分校(edX)使用R进行统计

• Foundations of Data Analysis — Part 2: Inferential Statistics by the University of Texas at Austin (edX)

  数据分析的基础—第2部分：德克萨斯大学奥斯汀分校(edX)的推理统计

“Foundations of Data Analysis” includes two of the top reviewed statistics courses available with a weighted average rating of 4.48 out of 5 stars over 20 reviews. The series is one of the only courses in the upper echelon of ratings to teach statistics with a focus on coding up examples. Though not mentioned in either course titles, the syllabi contain sufficient probability content to satisfy our testing criteria. These courses together have a great mix of fundamentals coverage and scope for the beginner data scientist.

“数据分析基础”包括两门最受好评的统计学课程，它们在20条评价中的5星加权平均评分为4.48。 该系列是等级较高的课程中仅有的一门教授统计学的课程，其重点是编写示例。 尽管在这两个课程的标题中均未提及，但课程大纲包含足够的概率内容以满足我们的测试标准。 这些课程为初学者数据科学家提供了广泛的基础知识覆盖范围和范围。

Michael J. Mahometa, Lecturer and Senior Statistical Consultant at the University of Texas at Austin, is the “Foundations of Data Analysis” series instructor. Both courses in the series are free. The estimated timeline is 6 weeks at 3–6 hours per week for each course. One prominent reviewer said:

德克萨斯大学奥斯汀分校的讲师兼高级统计顾问Michael J. Mahometa是“数据分析基础”系列讲师。 该系列的两个课程都是免费的。 每个课程的估计时间表为6周，每周3-6小时。 一位著名的评论家说 ：

Excellent course! I took part 1 and enjoyed it a lot, so it was very easy to decide to go on with part 2. Dr. Mahometa and team are very good teachers and their material is of a very high quality. The exercises are interesting and the materials (videos, labs and problems) are appropriate and well chosen. I recommend this course to anyone interested in statistical analysis (as an introduction to machine learning, big data, data science, etc.). On a scale from 1 to 10, I give 50!

优秀的课程！ 我参加了第1部分，并且对此非常满意，因此决定继续进行第2部分非常容易。Mahometa博士和团队是非常好的老师，他们的材料质量很高。 练习很有趣，并且材料(视频，实验和问题)适当且经过精心挑选。 我向对统计分析感兴趣的人(作为机器学习，大数据，数据科学等的简介)推荐本课程。 从1到10的比例，我给50！

Please note each course’s description and syllabus are accessible via the links provided above.

请注意，可以通过上面提供的链接访问每个课程的描述和课程提纲。

一流的专业 (A stellar specialization)

Update (December 5, 2016): Our original second recommendation, UC Berkeley’s “Stat2x: Introduction to Statistics” series, closed their enrollment a few weeks after the release of this article. We promoted our top recommendation in “The Competition” section accordingly.

更新(2016年12月5日)：我们最初的第二条建议，即加州大学伯克利分校的“ Stat2x：统计入门”系列，在本文发布几周后停止了他们的注册。 因此，我们在“竞争”部分中推广了我们的最高推荐。

• Statistics with R Specialization by Duke University on Coursera

  杜克大学在Coursera上的R专业化统计

…which contains the following five courses:

…包含以下五门课程：

• Introduction to Probability and Data

  概率与数据导论

• Inferential Statistics

  推论统计

• Linear Regression and Modeling

  线性回归与建模

• Bayesian Statistics

  贝叶斯统计

• Statistics with R Capstone

  R Capstone统计

This five-course specialization is based on Duke’s excellent Data Analysis and Statistical Inference course, which had a 4.82-star weighted average rating over 55 reviews. The specialization is taught by the same professor, plus a few additional faculty members. The early reviews on the new individual courses, which have a 3.6-star weighted average rating over 5 reviews, should be taken with a grain of salt due to the small sample size. The syllabi are comprehensive and has full sections dedicated to probability.

这个五门课程的专业课程基于杜克大学出色的数据分析和统计推断课程，该课程在55条评论中获得4.82星加权平均评分。 该专业由同一位教授教授，另外还有一些其他教职员工。 由于样本量较小，因此对新课程的早期评价(在5个评价中具有3.6星的加权平均评分)应与一小撮盐一起服用。 该大纲很全面，并有完整的章节专门介绍概率。

Dr. Mine Çetinkaya-Rundel is the main instructor for the specialization. The individual courses can be audited for free, though you don’t have access to grading. Reviews suggest that the specialization is “well worth the money.” Each course has an estimated timeline of 4–5 weeks at 5–7 hours per week. One prominent reviewer said the following about the original course that the specialization was based upon:

MineÇetinkaya-Rundel博士是该专业的主要讲师。 尽管您没有评分的权限，但是可以免费审核各个课程。 评论表明，专业化“ 物有所值”。 ”每门课程的时间表估计为4-5周，每周5-7小时。 一位著名的评论家对专业化所基于的原始课程说了以下几点：

One of the greatest courses I’ve taken so far. [Dr. Mine Çetinkaya-Rundel is] a great teacher, very much involved in exchanges with her students. A large variety of teaching approaches and tools. Lots of practice through short tests, R-programming labs, and an in-depth project. A very lively forum with lots of help to cope with difficulties. The course is not too difficult, but the variety of the proposed material requires that students get involved quite substantially. A very nice book available for free with plenty of practice exercises.

到目前为止，我参加过的最伟大的课程之一。 [博士 Çetinkaya-Rundel矿山是一位了不起的老师，非常参与与学生的交流。 各种各样的教学方法和工具。 通过短期测试，R编程实验室和深入项目进行大量练习。 一个非常活跃的论坛，为您提供许多帮助以应对困难。 该课程并不算太难，但是建议的材料种类繁多，要求学生充分参与。 一本非常不错的书，可以免费进行大量练习。

想要更多的可能性？ (Want more probability?)

• Introduction to Probability — The Science of Uncertainty by the Massachusetts Institute of Technology (MIT)

  概率概论—麻省理工学院(MIT) 的不确定性科学

Consider the above MIT course if you want a deeper dive into the world of probability. It is a masterpiece with a weighted average rating of 4.91 out of 5 stars over 34 reviews. Be warned: it is a challenge and much longer than most MOOCs. The level at which the course covers probability is also not necessary for the data science beginner.

如果您想更深入地研究概率世界，请考虑上述MIT课程。 这是一部杰作，在34条评论中，它的加权平均评分为4.91星(满分5星)。 请注意：这是一个挑战，比大多数MOOC都要长。 对于数据科学初学者来说，该课程涵盖概率的级别也不是必需的。

John Tsitsiklis and Patrick Jaillet, both of whom are professors in the Department of Electrical Engineering and Computer Science at MIT, teach the course. The contents of this course are essentially the same as those of the corresponding MIT class (Probabilistic Systems Analysis and Applied Probability) — a course that has been offered and continuously refined over more than 50 years. The estimated timeline is 16 weeks at 12 hours per week. One prominent reviewer said:

麻省理工学院电气工程和计算机科学系的教授John Tsitsiklis和Patrick Jaillet教授这门课程。 该课程的内容与相应的MIT类( 概率系统分析和应用概率 )的内容基本相同，该课程已提供并持续50多年。 预计时间表为16周，每周12小时。 一位著名的评论家说 ：

Many online courses are watered down in some way, but this one feels like a proper rigorous exercise-driven course similar to what you’d get in-person at a top school like MIT. The professors present concepts in lectures that have obviously been honed to a laser focus through years of pedagogical experience — there is not a single wasted second in the presentations and they go exactly at the right pace and detail for you to understand the concepts. The exercises will make you work for your knowledge and are critical for really internalizing the concepts. This is the best online course I have taken in any subject.

许多在线课程都以某种方式被淡化，但这就像是一门严格的运动驱动课程，类似于您在麻省理工学院这样的顶级学校亲自上课的课程。 教授们在演讲中介绍了一些概念，这些概念显然已经通过多年的教学经验而精打细算-演讲中没有浪费任何时间，而且它们以正确的步伐和细节进行了精确的显示，供您理解这些概念。 练习将使您努力学习知识，并且对于真正内化概念至关重要。 这是我选修的最佳在线课程。

I encourage you to visit Class Central’s page for this course to read the rest of the reviews.

我鼓励您访问本课程的Class Central页面，以阅读其余的评论。

竞赛 (The competition)

Our #1 pick had a weighted average rating of 4.48 out of 5 stars over 20 reviews. Let’s look at the other alternatives.

我们的第一选择在20条评论中的5星加权平均评分为4.48。 让我们看看其他选择。

• MedStats: Statistics in Medicine (Stanford University/Stanford OpenEdx): Great syllabus where the examples have a medical focus. Covers a bit of R programming at the end, though not as much as UT Austin’s series. A worthy option for anyone, even those not targeting medicine. It has a 4.58-star weighted average rating over 32 reviews.

  MedStats：医学统计学 (斯坦福大学/斯坦福大学OpenEdx)：课程大纲丰富，示例着重医学。 尽管不如UT Austin的系列那么多，但最后涵盖了一些R编程。 对于任何人来说都是一个值得选择的选择，即使不是针对药物的人也可以。 它拥有超过32条评论的4.58星级加权平均评分。

• SOC120x: I “Heart” Stats: Learning to Love Statistics (University of Notre Dame/edX): Targets a non-technical audience, though likely would be good for anyone. No coding. Good production value. Course and instructors look really fun. It has a 4.54-star weighted average rating over 12 reviews.

  SOC120x：我的“心脏”统计：学习爱统计 (巴黎圣母大学/ edX)：针对非技术受众，尽管可能对任何人都有好处。 没有编码。 良好的生产价值。 课程和讲师看起来真的很有趣。 它在12条评论中拥有4.54星级加权平均评分。

• QM101x: Statistics for Business (Indian Institute of Management Bangalore/edX): Part of a 4-course series. Business focus. Good syllabus that uses coding. The last two courses in the series are unreleased as of November 2016 so can’t make a judgment yet. It has a 4.43-star weighted average rating over 27 reviews.

  QM101x：商业统计 (印度管理学院，班加罗尔/ edX)：4门课程系列的一部分。 业务重点。 使用编码的良好教学大纲。 该系列的最后两门课程于2016年11月尚未发布，因此无法做出判断。 它在27条评论中获得4.43星级加权平均评分。

• Workshop in Probability and Statistics (Udemy): Taught by Dr. George Ingersoll, Associate Dean of Executive MBA Programs at the UCLA Anderson School of Management. Costs money. Uses Excel. It has a 4.4-star weighted average rating over 452 reviews.

  概率统计研讨会 (Udemy)：由UCLA安德森管理学院高管MBA课程副院长George Ingersoll博士讲授。 花费金钱。 使用Excel。 它拥有452条评论中的4.4星级加权平均评分。

• Intro to Descriptive Statistics (San Jose State University/Udacity): Part of a 2-course series. Bite-sized videos. No coding. It has a 3.88-star weighted average rating over 8 reviews.

  描述性统计简介 (圣何塞州立大学/ Udacity)：2课程系列的一部分。 咬合大小的视频。 没有编码。 它拥有超过8条评论的3.88星级加权平均评分。

• Intro to Inferential Statistics (San Jose State University/Udacity): Part of a 2-course series. I took both courses as refreshers for my undergrad statistics classes and came away with a deeper understanding. Really enjoyed Katie Kormanik’s teaching style (see video below). Bite-sized videos. No coding. It has a 4.4-star weighted average rating over 5 reviews.

  推理统计入门 (圣何塞州立大学/ Udacity)：2课程系列的一部分。 我将这两门课程都作为本科统计课的复习课程，对自己的理解更加深刻。 非常喜欢Katie Kormanik的教学风格(请参见下面的视频)。 咬合大小的视频。 没有编码。 它有超过5条评论的4.4星级加权平均评分。

• 6.008.1x: Computational Probability and Inference (Massachusetts Institute of Technology/edX): One of two courses/series to teach statistics with a focus of coding up examples in Python. Reviews suggest prior stats experience is needed and that the course is a bit unorganized. It has a 4-star weighted average rating over 12 reviews.

  6.008.1x：计算概率和推理 (麻省理工学院/ edX)：讲授统计学的两门课程/系列之一，重点是用Python编写示例。 评论表明需要有以前的统计经验，并且该课程有点杂乱无章。 该酒店在12条评论中拥有4星级加权平均评分。

• Basic Statistics (University of Amsterdam/Coursera): One of two statistics courses in the University of Amsterdam’s Methods and Statistics in Social Sciences Specialization. One exceedingly positive review on the series and its instructors. No coding. It has a 4.06-star weighted average rating over 8 reviews.

  基础统计学 (阿姆斯特丹大学/库塞拉大学)：阿姆斯特丹大学社会科学方法与统计学专业的两门统计学课程之一。 对该系列及其讲师的评价极为正面。 没有编码。 它拥有4.06星级加权平均评分，超过8条评论。

• Inferential Statistics (University of Amsterdam/Coursera): One of two statistics courses in the University of Amsterdam’s Methods and Statistics in Social Sciences Specialization. One exceedingly positive review on the series and its instructors. No coding. It has a 4-star weighted average rating over 3 reviews.

  推论统计学 (阿姆斯特丹大学/库塞拉大学)：阿姆斯特丹大学社会科学方法与统计学专业的两门统计学课程之一。 对该系列及其讲师的评价极为正面。 没有编码。 它具有3条评论的4星级加权平均评分。

• PH525.1x: Statistics and R (Harvard University/edX): Part of a 7-course series on edX. Life sciences focus. Uses R programming, but the reviews suggest UT Austin’s series is better. It has a 3.96-star weighted average rating over 26 reviews.

  PH525.1x：Statistics and R (哈佛大学/ edX)：edX的7门课程系列的一部分。 生命科学的重点。 使用R编程，但评论表明UT Austin的系列更好。 在26条评论中，它获得了3.96星级加权平均评分。

• PH525.3x: Statistical Inference and Modeling for High-throughput Experiments (Harvard University/edX): Part of a 7-course series on edX. Life sciences focus. Uses R programming, but the reviews suggest UT Austin’s series is better. It has a 4.63-star weighted average rating over 4 reviews.

  PH525.3x：高通量实验的统计推断和建模 (哈佛大学/ edX)：edX的7门课程系列的一部分。 生命科学的重点。 使用R编程，但评论表明UT Austin的系列更好。 它在4条评论中获得4.63星级加权平均评分。

• Intro to Statistics (Udacity): This is one of Udacity’s earliest courses and it has its shortcomings, as described in this memorable review by a college educator. No coding. It has a 3.93-star weighted average rating over 41 reviews.

  统计入门 (Udacity)：这是Udacity最早的课程之一，但也有其不足之处，正如大学教育工作者在这份令人难忘的评论中所述。 没有编码。 它有超过41条评论的3.93星级加权平均评分。

• Mathematical Biostatistics Boot Camp 1 (Johns Hopkins University/Coursera): Part of a 2-course series. Biostatistics focus. It has a 3.13-star weighted average rating over 23 reviews.

  数学生物统计学新手训练营1 (约翰霍普金斯大学/库塞拉)：2课程系列的一部分。 生物统计学重点。 它在23条评论中拥有3.13星级加权平均评分。

• Mathematical Biostatistics Boot Camp 2 (Johns Hopkins University/Coursera): Part of a 2-course series. Biostatistics focus. It has a 3.83-star weighted average rating over 3 reviews.

  数学生物统计学新手训练营2 (约翰霍普金斯大学/库塞拉)：2课程系列的一部分。 生物统计学重点。 它在3条评论中拥有3.83星级加权平均评分。

• KIexploRx: Explore Statistics with R (Karolinska Institutet/edX): More of a data exploration course than a statistics course. Uses coding. It has a 3.77-star weighted average rating over 22 reviews.

  KIexploRx：使用R探索统计数据 (Karolinska Institutet / edX)：更多的是数据探索课程，而不是统计课程。 使用编码。 在22条评论中，它获得了3.77星级加权平均评分。

• Statistical Inference (Johns Hopkins University/Coursera): One of two statistics courses in JHU’s data science specialization. Bad reviews. It has a 2.9-star weighted average rating over 29 reviews.

  统计推断 (约翰霍普金斯大学/库塞拉分校)：JHU的数据科学专业的两个统计课程之一。 差评。 它在29条评论中获得2.9星级加权平均评分。

• Regression Models (Johns Hopkins University/Coursera): One of two statistics courses in JHU’s data science specialization. Bad reviews. It has a 2.73-star weighted average rating over 30 reviews.

  回归模型 (约翰霍普金斯大学/库塞拉)：JHU数据科学专业的两个统计课程之一。 差评。 它拥有2.73星级加权平均评分，超过30条评论。

• DS101X: Statistical Thinking for Data Science and Analytics(Columbia University/edX): Part of the Microsoft Professional Program Certificate in Data Science. Short syllabus. Bad reviews. It has a 2.77-star weighted average rating over 24 reviews.

  DS101X：数据科学和分析的统计思维 (哥伦比亚大学/ edX)：数据科学Microsoft专业课程证书的一部分。 简短的课程提纲。 差评。 它在24条评论中获得2.77星级加权平均评分。

• Understanding Clinical Research: Behind the Statistics (University of Cape Town/Coursera): “This isn’t a comprehensive statistics course, but it offers a practical orientation to the field of medical research and commonly used statistical analysis.” Health care focus. It has a 5-star weighted average rating over 15 reviews.

  了解临床研究：统计学的背后 (开普敦大学/库尔塞拉大学)：“这不是一门综合的统计学课程，但是它为医学研究和常用的统计分析领域提供了实用的指导。” 卫生保健重点。 它具有15条评论的5星级加权平均评分。

• MED101x: Introduction to Applied Biostatistics: Statistics for Medical Research (Osaka University/edX): Biostatistics focus. Uses coding. It has a 4.5-star weighted average rating over 3 reviews.

  MED101x：应用生物统计学导论：医学研究统计学 (大阪大学/ edX)：以生物统计学为重点。 使用编码。 该酒店在3条评论中获得4.5星级加权平均评分。

• Probability and Statistics (Stanford University/Stanford OpenEdx): Curriculum looks great. The one review is really positive. No coding. It has a 4.5-star weighted average rating over 1 review.

  概率与统计 (斯坦福大学/斯坦福OpenEdx)：课程看起来很棒。 一篇评论真的很积极。 没有编码。 它在1条评论中拥有4.5星级加权平均评分。

• Inferential and Predictive Statistics for Business (University of Illinois at Urbana-Champaign/Coursera): Part of a 7-course Managerial Economics and Business Analysis Specialization. Uses Excel. It has a 5-star weighted average rating over 1 review.

  商业推论和预测统计 (伊利诺伊大学香槟分校/库塞拉分校)：管理经济学和商业分析专业7门课程的一部分。 使用Excel。 在1条评论中，它具有5星级加权平均评分。

• Exploring and Producing Data for Business Decision Making (University of Illinois at Urbana-Champaign/Coursera): Part of a 7-course Managerial Economics and Business Analysis Specialization. Uses Excel. It has a 5-star weighted average rating over 1 review.

  探索和生产用于业务决策的数据 (伊利诺伊大学香槟分校/库塞拉分校)：管理经济学和业务分析专业7门课程的一部分。 使用Excel。 在1条评论中，它具有5星级加权平均评分。

• Introduction to Probability, Statistics, and Random Processes (University of Massachusetts Amherst/Independent): Videos not available for the whole course. It has a 2.5-star weighted average rating over 2 reviews.

  概率，统计和随机过程简介 (马萨诸塞州大学阿默斯特分校/独立学院)：整个课程都不提供视频。 该酒店在2条评论中拥有2.5星级加权平均评分。

• 005x: Introduction to Statistical Methods for Gene Mapping (Kyoto University/edX): Genetics focus. Need prior statistics and R knowledge. It has a 2.5-star weighted average rating over 1 review.

  005x：基因作图统计方法简介 (京都大学/ edX)：遗传学重点。 需要先验统计和R知识。 在1条评论中，它具有2.5星级加权平均评分。

• Statistics for Genomic Data Science (Johns Hopkins University/Coursera): Genomic focus. Not a good introductory course: “A fair class for someone with an interest in this field who also happens to have a decent background in R programming.” It has a 2-star weighted average rating over 2 reviews.

  基因组数据科学统计学 (约翰霍普金斯大学/库塞拉)：关注基因组。 入门课程不是一个很好的课程：“对于在此领域有兴趣并且恰好在R编程方面也有不错背景的人来说，这是一个公平的课程。” 该酒店在2条评论中拥有2星级加权平均评分。

The following courses had no reviews as of November 2016.

截至2016年11月，以下课程没有任何评论。

• Statistical Thinking in Python (Part 1) and Statistical Thinking in Python (Part 2) (DataCamp): Uses coding and Python specifically, making it one of few worthy courses or series that use that language. Seven hours of video and 120+ exercises. DataCamp is a popular option.

  Python中的统计思维(第1部分)和Python中的统计思维(第2部分) (DataCamp)：特别使用了编码和Python，使其成为使用该语言的少数有价值的课程或系列之一。 七个小时的视频和120多个练习。 DataCamp是一个受欢迎的选项。

• A Hands-on Introduction to Statistics with R (DataCamp): Uses coding. 26 hours of video and 150+ exercises. Again, DataCamp is a popular option.

  使用R (DataCamp) 进行统计的动手入门 ：使用编码。 26小时的视频和150多次练习。 同样，DataCamp是一个流行的选项。

• Statistical Computing with R — a gentle introduction (University College London/Independent): Uses coding.

  使用R进行统计计算-简要介绍 (伦敦大学学院/独立学院)：使用编码。

• Probability & Statistics (Carnegie Mellon): Uses R. Primarily text-based instruction. Designed to be equivalent to one semester of a college statistics course.

  概率与统计 (Carnegie Mellon)：使用R。主要是基于文本的指令。 设计相当于大学统计课程的一个学期。

• Introduction to Probability and Statistics (Massachusetts Institute of Technology/MIT OCW): Traditional lecture format (video-taped).

  概率统计简介 (麻萨诸塞理工学院/麻省理工学院OCW)：传统讲课格式(录像)。

• Fundamentals of Engineering Statistical Analysis (The University of Oklahoma/Janux): Engineering focus.

  工程统计分析基础 (俄克拉何马大学/哈努克斯分校)：工程重点。

• Elementary Business Statistics (The University of Oklahoma/Janux): Business focus.

  基本业务统计 (俄克拉何马大学/哈努克斯分校)：业务重点。

• STAT101x: Biostatistics for Big Data Applications (The University of Texas Medical Branch/edX): Biostatistics focus.

  STAT101x：大数据应用程序的生物统计学 (德克萨斯大学医学分校/ edX)：关注生物统计学。

• 416.1x: Probability: Basic Concepts & Discrete Random Variables(Purdue University/edX): Part of a 2-course series.

  416.1x：概率：基本概念和离散随机变量 (Purdue University / edX)：2门课程系列的一部分。

• 416.2x: Probability: Distribution Models & Continuous Random Variables (Purdue University/edX): Part of a 2-course series.

  416.2x：概率：分布模型和连续随机变量 (Purdue University / edX)：2门课程系列的一部分。

• Business Statistics and Analysis Specialization (Rice University/Coursera): Uses Excel.

  商业统计和分析专业 (Rice University / Coursera)：使用Excel。

• Statistics 110: Probability (Harvard University): Traditional lecture format (video-taped). Often recommended on Quora.

  统计数据110：概率 (哈佛大学)：传统演讲形式(录像带)。 通常在Quora上推荐。

• Statistics (Dataquest): A multi-course series with about 12 hours of content. Subscription required. One of two courses/series to teach statistics with a focus of coding up examples in Python. A note from Dataquest: “the statistics courses are being entirely re-written at the moment, due for release around the end of November.”

  统计资料 (Dataquest)：多课程系列，内容约12小时。 需要订阅。 教授统计学的两门课程/系列之一，重点是用Python编写示例。 Dataquest的注释：“统计课程目前正在完全重写，计划于11月底发布。”

结语 (Wrapping it Up)

This is the second of a six-piece series that covers the best MOOCs for launching yourself into the data science field. We covered programming in the first article, and the remainder of the series will cover several other data science core competencies: the data science process, data visualization, and machine learning.

这是一个由六部分组成的系列的第二部分，该系列涵盖了最佳的MOOC，可帮助您进入数据科学领域。 我们在第一篇文章中介绍了编程，本系列的其余部分将介绍其他几个数据科学核心能力： 数据科学过程 ，数据可视化和机器学习。

The final piece will be a summary of those courses, and the best MOOCs for other key topics such as data wrangling, databases, and even software engineering.

最后的部分将是这些课程的总结，以及其他关键主题(例如数据整理，数据库甚至软件工程)的最佳MOOC。

If you want to learn Data Science, start with one of these programming classesA comprehensive guide to online intro to programming courses.medium.freecodecamp.comI ranked every Intro to Data Science course on the internet, based on thousands of data pointsA comprehensive guide to online intro to data science courses.medium.freecodecamp.com

如果您想学习数据科学，请从以下编程课程之一开始 。编程课程在线入门的综合指南。 medium.freecodecamp.com 我根据数千个数据点对互联网上的每门数据科学入门课程进行了排名 。数据科学课程在线入门的综合指南。 medium.freecodecamp.com

If you’re looking for a complete list of Data Science MOOCs, you can find them on Class Central’s Data Science and Big Data subject page.

如果要查找数据科学MOOC的完整列表，可以在Class Central的数据科学和大数据主题页面上找到它们。

If you enjoyed reading this, check out some of Class Central’s other pieces:

如果您喜欢阅读本文，请查看Class Central的其他部分：

Here are 250 Ivy League courses you can take online right now for free250 MOOCs from Brown, Columbia, Cornell, Dartmouth, Harvard, Penn, Princeton, and Yale.medium.freecodecamp.comThe 50 best free online university courses according to dataWhen I launched Class Central back in November 2011, there were around 18 or so free online courses, and almost all of…medium.freecodecamp.com

这里有250个常春藤盟军课程，您可以立即在线免费获得 来自布朗，哥伦比亚，康奈尔，达特茅斯，哈佛，佩恩，普林斯顿和耶鲁的250个MOOC。 根据数据， media.freecodecamp.com上前 50个最好的免费在线大学课程 当我于2011年11月启动Class Central时，大约有18个左右的免费在线课程，几乎所有…

If you have suggestions for courses I missed, let me know in the responses!

如果您对我错过的课程有任何建议，请在回复中告诉我！

If you found this helpful, click the ? so more people will see it here on Medium.

如果您认为这有帮助，请单击“？”。 因此更多的人会在Medium上看到它。

This is a condensed version of the original article published on Class Central, where course descriptions, syllabi, and multiple reviews are included.

这是在Class Central上发布的原始文章的精简版本，其中包括课程说明，教学大纲和多项评论。

翻译自: https://www.freecodecamp.org/news/if-you-want-to-learn-data-science-take-a-few-of-these-statistics-classes-9bbabab098b9/

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