数据可视化课程_互联网上每个数据可视化课程的概述

数据可视化课程

by David Venturi

大卫·文图里(David Venturi)

互联网上每个数据可视化课程的概述 (An overview of every Data Visualization course on the internet)

A year ago, I dropped out of one of the best computer science programs in Canada. I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. And I could learn it faster, more efficiently, and for a fraction of the cost.

一年前，我退出了加拿大最好的计算机科学程序之一。 我开始使用在线资源创建自己的数据科学硕士课程 。 我意识到我可以通过edX，Coursera和Udacity学习所需的一切。 而且我可以更快，更有效地学习它，而费用却只有一小部分。

I’m almost finished now. I’ve taken many data science-related courses and audited portions of many more. I know the options out there, and what skills are needed for learners preparing for a data analyst or data scientist role. A few months ago, I started creating a review-driven guide that recommends the best courses for each subject within data science.

我现在快要完蛋了。 我参加了许多与数据科学相关的课程，并对更多课程进行了审计。 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。 几个月前，我开始创建一个以评论为导向的指南，为数据科学中的每个学科推荐最佳课程。

For the first guide in the series, I recommended a few coding classes for the beginner data scientist. Then it was statistics and probability classes. Then it was intros to data science itself.

对于本系列的第一个指南，我为初学者数据科学家推荐了一些编码类 。 然后是统计和概率分类 。 然后是数据科学本身的介绍 。

现在进入数据可视化。 (Now onto data visualization.)

For this guide, I spent 10+ hours trying to identify every online data visualization course offered as of March 2017, 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10多个小时来尝试确定截至2017年3月提供的每项在线数据可视化课程，从其教学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 three criteria:

每门课程必须符合三个条件：

1. The majority of the course must be focused on explanatory data visualization. Coverage of data preparation, for example, is permitted given it is an important part of the data visualization process. Courses that cover less relevant topics (statistical modeling, for example) are excluded. More on the explanatory distinction below.

   本课程的大部分内容必须侧重于解释性数据可视化。 例如，允许覆盖数据准备，因为它是数据可视化过程的重要组成部分。 涵盖较少主题的课程(例如统计建模)被排除在外。 更多关于下面的解释区别。

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 an interactive online course, so no books or read-only tutorials. Though these are viable ways to learn, 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 。 尽管这些是可行的学习方法，但本指南重点介绍课程。

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。 但是，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，请在评论部分告诉我们。

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 to calculate a weighted average rating for each course. 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，以计算每个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 two factors, with the first given preference over the second:

我们基于两个因素做出了主观的课程提纲判断，第一个优先于第二个：

1. Coverage of data visualization theory. Are the motivations for visualization choices explained? Does the course only teach the tool? More on this in the next section.

   数据可视化理论的覆盖范围。 是否解释了可视化选择的动机？ 课程只教工具吗？ 下一节将对此进行更多介绍。

2. Coverage of chosen data visualization tool(s). Does the course effectively teach common visualization tools (Tableau, ggplot2, Seaborn, etc.)? Do students have opportunities to practice these skills? No preference for tool choice is given.

   所选数据可视化工具的覆盖范围。 该课程是否有效地教授常见的可视化工具(Tableau，ggplot2，Seaborn等)？ 学生有机会练习这些技能吗？ 没有选择工具的偏好。

为什么要优先考虑可视化理论 (Why prioritize visualization theory)

Mastery of a specific tool is wasteful without knowledge of the fundamentals of effective visualization. Plus, tools are often interchangeable depending on the setting.

在不了解有效可视化基础知识的情况下，对特定工具的掌握非常浪费。 另外，根据设置，工具通常可以互换。

More importantly, doing good data visualization is more complex than most people think. Careful thought is required from the planning stages to execution. Choosing the right chart, balancing complexity and clutter, leveraging preattentive properties, and more, data visualization is both an art and a science. It is easy to go wrong, and sometimes horribly (see below).

更重要的是，进行良好的数据可视化比大多数人想象的要复杂。 从计划阶段到执行，都需要仔细考虑。 选择正确的图表，平衡复杂性和混乱状况，利用细心的属性等，数据可视化既是一门艺术，也是一门科学。 这很容易出错，有时甚至很可怕(见下文)。

探索性与解释性可视化 (Exploratory vs. explanatory visualization)

As described by Indiana University professor Yong-Yeol Ahn, the aim of explanatory data visualization is to communicate insights and messages, while the aim of exploratory visualization is to discover hidden patterns.

如所描述由印第安纳大学教授永烈安， 说明数据可视化的目的是要进行通信的见解和消息，而试探性可视化的目的是发现隐藏的模式。

This article focuses on explanatory data visualization courses. Courses like Udacity’s Data Analysis with R (exclusively an exploratory course) are therefore excluded from this article. The topic is important; there just aren’t enough courses to justify a standalone article. It will be covered briefly in the summary article for this series.

本文重点介绍解释性数据可视化课程。 因此，本文不包括Udacity的R数据分析之类的课程(排他性的探索性课程)。 这个话题很重要； 只是没有足够的课程来证明独立文章的合理性。 本系列的摘要文章将对此进行简要介绍。

有时需要编码经验 (Coding experience sometimes required)

Some courses listed below require basic coding skills in the course’s language of instruction. If you have very little programming experience, our recommendations in the first article in this series — the best intro to programming courses for data science — would be a great start. Both Python and R courses are covered.

下面列出的某些课程需要使用该课程的教学语言的基本编码技能。 如果您几乎没有编程经验，那么本系列第一篇文章中的建议(关于数据科学的编程课程的最佳入门)将是一个不错的开始。 涵盖了Python和R课程。

审查资料不足 (Review data lacking)

Compared to the other articles in this series, there is a lack of review data for data visualization courses that fit the above criteria. There is also no clear best data visualization course yet. The recommendations below are therefore not as conclusive as past articles. As always, but especially here, try to pick the course that best fits your needs.

与本系列的其他文章相比，缺少符合上述条件的数据可视化课程的评论数据。 还没有明确的最佳数据可视化课程。 因此，以下建议并不像过去的文章那样具有决定性。 与往常一样，尤其是在这里，请尝试选择最适合您需求的课程。

我们选择的最佳数据可视化课程是… (Our pick for the best data visualization course is…)

• Data Visualization with Tableau Specialization by the University of California, Davis on Coursera

  加利福尼亚大学戴维斯分校有关Coursera的Tableau专业化数据可视化

…which contains the following five courses:

…包含以下五门课程：

• Fundamentals of Visualization with Tableau

  Tableau的可视化基础

• Essential Design Principles for Tableau

  Tableau的基本设计原则

• Visual Analytics with Tableau

  使用Tableau进行视觉分析

• Creating Dashboards and Storytelling with Tableau

  使用Tableau创建仪表板和讲故事

• Data Visualization with Tableau Project

  使用Tableau Project进行数据可视化

The University of California, Davis’ Data Visualization with Tableau Specialization has the best combination of theory and tool coverage available based on this article’s evaluation criteria. It dives deep into theory like few other courses. There are opportunities to practice Tableau via walkthroughs and a final project, though mastering Tableau is not the main focus. It is a fairly new specialization (late 2016) and the courses only have one 4-star rating between them on the review sites used for this analysis.

基于本文的评估标准，加州大学戴维斯分校的Tableau专业化数据可视化具有理论和工具覆盖率的最佳组合。 它像其他课程一样深入理论。 尽管精通Tableau并不是主要重点，但仍有机会通过演练和最终项目来实践Tableau。 这是一个相当新的专业化(2016后期)和课程只对用于该分析的评论网站，它们之间的一个4 -星级评级。

Govind Acharya, Hunter Whitney, and Suk Brar are the instructors. Acharya is a Principal Analyst at UC Davis. Whitney and Brar are respected industry professionals. Between them, they have decades of data visualization experience that is clearly conveyed through the course content. The videos are well-produced.

讲师是Govind Acharya，Hunter Whitney和Suk Brar。 Acharya是加州大学戴维斯分校的首席分析师。 惠特尼和布拉尔是受人尊敬的行业专家。 在他们之间，他们拥有数十年的数据可视化经验，这些经验可以通过课程内容清楚地传达出来。 视频制作精良。

The estimated timeline for the specialization on Coursera is 22 weeks with weekly commitments ranging from three to eight hours per week. These estimates are assuredly too high, as noted by several reviewers and my experience with Coursera. Free (auditing each course individually) and paid (paying for the Specialization) options are currently available.

Coursera的专业化时间表预计为22周，每周承诺时间为每周3到8个小时。 正如一些评论家和我在Coursera的经验所指出的，这些估计值肯定过高。 当前提供免费(分别审核每个课程)和付费(支付专业化课程)选项。

Several prominent reviewers on Coursera noted the following:

Coursera上的几位著名评论家指出：

They not only tell you how to do the visualization design but also tell you why (the physiology, the principles). I would highly recommend this class.

他们不仅告诉您如何进行可视化设计，还告诉您原因(生理学，原理)。 我强烈推荐这堂课。

Great course — guards against some subtle pitfalls in visualization preparation.

出色的课程–防止可视化准备中的一些细微陷阱。

Although a very basic introduction to the use of Tableau, the course provides a broad and interesting background that should prove useful to anyone seeking to enhance their understanding of visualization fundamentals.

尽管这是对Tableau的使用的非常基本的介绍，但本课程提供了广泛而有趣的背景，对于寻求增强其对可视化基础知识的理解的人来说，应该证明是有用的。

可视化理论和R，是通过实践中学到的 (Visualization theory and R, learned by doing)

• Data Visualization with ggplot2 by DataCamp
  通过ggplot2通过DataCamp进行数据可视化

…for which there are three parts:

…包括三个部分：

• Data Visualization with ggplot2 (Part 1)

  使用ggplot2进行数据可视化(第1部分)

• Data Visualization with ggplot2 (Part 2)

  使用ggplot2进行数据可视化(第2部分)

• Data Visualization with ggplot2 (Part 3)

  ggplot2进行数据可视化(第3部分)

Another great option is DataCamp’s Data Visualization with ggplot2 series, especially if you want to learn R and, more specifically, ggplot2. A substantial amount of theory is covered, which is fitting given that ggplot2 is inspired by The Grammar of Graphics. Tool coverage and practice are impressive as well — you will know R and its quirky syntax quite well leaving these courses. There are no reviews for these courses on the review sites used for this analysis.

另一个不错的选择是DataCamp的ggplot2系列数据可视化，特别是如果您想学习R，更具体地说，是ggplot2 。 涵盖了大量理论，鉴于ggplot2受Graphics语法的启发，这是很合适的 。 工具的涵盖范围和实践也给人留下了深刻的印象-离开这些课程，您将非常了解R及其古怪的语法。 用于此分析的评论网站上没有这些课程的评论。

The instructor for all three courses is Rick Scavetta, who is a biologist, workshop trainer, freelance data scientist, and cofounder of Science Craft. DataCamp’s hybrid teaching style leverages video (starring Scavetta) and text-based instruction with lots of examples through an in-browser code editor. The video, text, and code content is polished nicely.

这三门课程的讲师都是Rick Scavetta ，他是生物学家，讲习班培训师，自由数据科学家和Science Craft的联合创始人。 DataCamp的混合教学风格通过浏览器内代码编辑器利用视频(由Scavetta主演)和基于文本的指令以及许多示例。 视频，文本和代码内容被很好地修饰。

Together, the estimated timeline for all three courses is 16 hours. The first chapter of each course is available for free. A DataCamp subscription, which is currently $29 per month or $300 per year, is required for full access.

总之，这三门课程的估计时间表为16小时。 每门课程的第一章都是免费的。 要完全访问，需要有DataCamp 订阅 ，目前每月需要29美元或每年300美元。

The following endorsement is from Hadley Wickham, Chief Scientist at RStudio and ggplot2 creator:

以下是RStudio首席科学家和ggplot2创作者Hadley Wickham的 认可 ：

I thoroughly recommend “Data Visualization with ggplot2” by Rick Scavetta. It gives you an excellent introduction to ggplot2. You’ll learn both the underlying theory, and get hands on practice in DataCamp’s online learning environment.

我彻底推荐Rick Scavetta的“使用ggplot2进行数据可视化”。 它为您提供了ggplot2的出色介绍。 您将学习基础理论，并在DataCamp的在线学习环境中进行实践。

有关Tableau的实用入门和出色的讲师 (A practical intro to Tableau with an excellent instructor)

Tableau 10 Series by Kirill Eremenko and the SuperDataScience Team on Udemy, which includes:

Kirill Eremenko和Udemy的SuperDataScience团队制作的Tableau 10系列，其中包括：

• Tableau 10 A-Z: Hands-On Tableau Training For Data Science!

  Tableau 10 AZ：数据科学动手Tableau培训！

• Tableau 10 Advanced Training: Master Tableau in Data Science

  Tableau 10高级培训：数据科学硕士Tableau

Taught by Kirill Eremenko, SuperDataScience’s Tableau 10 Series is an effective practical introduction. It focuses mostly on tool coverage (Tableau) rather than data visualization theory. Eremenko is one of the most well-regarded instructors in these guides with consistently positive reviews across of his courses. The A-Z course is a prerequisite to the Advanced Training course. Together, the courses in the series have a 4.6-star weighted average rating over 3,724 reviews.

SuperDataScience的Tableau 10系列由Kirill Eremenko讲授，是有效的实用介绍。 它主要侧重于工具覆盖率(Tableau)，而不是数据可视化理论。 埃雷缅科(Eremenko)是这些指南中最受推崇的讲师之一，他的课程始终受到积极评价。 AZ课程是高级培训课程的前提条件。 总之，该系列课程的4.6颗星加权平均评分超过3,724条评论。

The series has seventeen hours of video content. The cost of each course varies depending on Udemy discounts, but these are are frequent, and can be purchased for as little as $10.

该系列有十七个小时的视频内容。 每门课程的费用根据Udemy的折扣而有所不同，但是这些折扣很常见，购买价格低至10美元。

Several prominent reviewers noted the following:

几位杰出的审稿人指出：

This was great. I use Tableau daily but it was an awesome refresher on some of the items i don’t use and a great study aid for sitting the Tableau Certified Professional Exam. Good job Kirill and the Team!

太好了 我每天都使用Tableau，但是对于一些我不使用 的项目，它真是令人耳目一新， 也是参加Tableau认证专业考试的绝佳学习帮助。 好的基里尔和团队！

Kirill is a tremendous teacher and students taking this course will clearly see why he has dozens of courses and thousands of students — he’s able to teach complex skills, in a real world business context and do so incrementally thereby combining the often complex task of teaching both fundamentals and context specific applications simultaneously.

基里尔(Kirill)是一位出色的老师，参加本课程的学生将清楚地知道为什么他拥有数十门课程和数千名学生-他能够在现实的商业环境中教授复杂的技能，并逐步做到这一点，从而结合了通常将两者兼而有之的教学任务基本原理和特定于上下文的应用程序同时进行。

竞赛 (The competition)

Let’s look at the other alternatives, sorted by descending rating.

让我们看看其他选择，按降序排列。

Interactive Data Visualization with Python & Bokeh (Ardit Sulce/Udemy): Tool focus (Python and Bokeh). Includes a section on creating web applications. Seven hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.6-star weighted average rating over 103 reviews.

使用Python和Bokeh进行交互式数据可视化 (Ardit Sulce / Udemy)：关注工具(Python和Bokeh)。 包括有关创建Web应用程序的部分。 七个小时的视频。 成本因Udemy折扣而异，这是很常见的。 在103条评论中，它获得了4.6星级加权平均评分。

Information Visualization (IVMOOC) (Indiana University/Independent): Covers theory and multiple tools in great detail. Impressive real-life project. Registration did not work when attempted despite emails to the course administrators. A full twelve-week graduate course. Free. It has a 4.5-star weighted average rating over 2 reviews.

信息可视化(IVMOOC) (印第安纳大学/独立学院)：详细介绍了理论和多种工具。 令人印象深刻的现实生活项目。 尽管向课程管理员发送了电子邮件，但尝试注册仍无法进行。 完整的十二周研究生课程。 自由。 它在2条评论中拥有4.5星级加权平均评分。

Tableau for Beginners — Get Certified Accelerate Your Career (Lukas Halim/Udemy): Tool focus (Tableau). Four hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.5-star weighted average rating over 649 reviews.

适用于初学者的 Tableau- 获得认证可加快您的职业生涯 (Lukas Halim / Udemy)：关注工具(Tableau)。 四个小时的视频。 成本因Udemy折扣而异，这是很常见的。 它具有649条评论中的4.5星级加权平均评分。

Analyzing and Visualizing Data with Power BI (Microsoft/edX): Tool focus (Power BI). Tailored for business users invested in the Microsoft ecosystem. Part of the Microsoft Professional Program Certificate in Data Science. Estimated timeline of two to four hours per week over six weeks. Free with a Verified Certificate available for purchase. It has a 4.5-star weighted average rating over 117 reviews.

使用Power BI (Microsoft / edX) 分析和可视化数据 ：以工具为中心(Power BI)。 为投资于Microsoft生态系统的业务用户量身定制。 数据科学Microsoft专业计划证书的一部分 。 估计在六个星期内每周工作两至四个小时。 免费提供可购买的已验证证书。 它拥有117条评论的4.5星级加权平均评分。

Analyzing and Visualizing Data with Excel (Microsoft/edX): Tool focus (Excel). Tailored for business users invested in the Microsoft ecosystem. Part of the Microsoft Professional Program Certificate in Data Science. Estimated timeline of two to four hours per week over six weeks. Free with a Verified Certificate available for purchase. It has a 4.5-star weighted average rating over 972 reviews.

使用Excel (Microsoft / edX) 分析和可视化数据 ：以工具为中心(Excel)。 为投资于Microsoft生态系统的业务用户量身定制。 数据科学Microsoft专业计划证书的一部分 。 估计在六个星期内每周工作两至四个小时。 免费提供可购买的已验证证书。 它拥有4.5颗星的加权平均评分，超过972条评论。

Data Visualize Data with D3.js The Easy Way (Infinite Skills/Udemy): Tool focus (D3.js). Four hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 262 reviews.

数据使用D3.js可视化数据简便方法 (Infinite Skills / Udemy)：关注工具(D3.js)。 四个小时的视频。 成本因Udemy折扣而异，这是很常见的。 它拥有262条评论中的4.4星加权平均评分。

Data Visualization with Python and Matplotlib (Stone River eLearning/Udemy): Tool focus (Python and Matplotlib). Six hours of video. Cost varies depending on Udemy discounts, which are frequent. It has a 4.4-star weighted average rating over 92 reviews.

使用Python和Matplotlib进行数据可视化 (Stone River电子教学/ Udemy)：关注工具(Python和Matplotlib)。 六个小时的视频。 成本因Udemy折扣而异，这是很常见的。 它在92条评论中获得4.4星级加权平均评分。

Data Analysis: Visualization and Dashboard Design (Delft University of Technology/edX): Tool (Excel) and business focus. Estimated timeline of four to six hours per week over six weeks. Free with a Verified Certificate available for purchase. It has a 4.2-star weighted average rating over 5 reviews.

数据分析：可视化和仪表板设计 (代尔夫特工业大学/ edX)：工具(Excel)和业务重点。 估计在六个星期内每周工作四至六个小时。 免费提供可购买的已验证证书。 它在5条评论中获得4.2星级加权平均评分。

Big Data: Data Visualisation (Queensland University of Technology/FutureLearn): Balanced theory/tool focus. Exposure to a variety of tools. Starts August 2017. Estimated timeline of two hours per week over three weeks. Free with an “upgrade” available for purchase. It has a 4-star rating over 1 review.

大数据：数据可视化 (昆士兰科技大学/ FutureLearn)：平衡的理论/工具。 接触各种工具。 从2017年8月开始。预计时间表为三周，每周两小时。 可免费购买“升级”。 在1条评论中获得4星级评分。

Data Visualization and Communication with Tableau (Duke University/Coursera): Tool (Tableau) and business focus. Part of the Excel to MySQL: Analytic Techniques for Business Specialization. Estimated timeline of six to eight hours per week over five weeks. Free and paid options available. It has a 3.67-star weighted average rating over 9 reviews.

与Tableau进行数据可视化和通信 (杜克大学/库塞拉大学)：工具(Tableau)和业务重点。 MySQL的Excel的一部分：业务专业化的分析技术 。 估计在五个星期内每周工作六至八个小时。 提供免费和付费选项。 它在9条评论中拥有3.67星的加权平均评分。

Data Visualization (University of Illinois at Urbana-Champaign/Coursera): Theory focus. Part of the Data Mining Specialization. Estimated timeline of four to six hours per week over four weeks. Free and paid options available. It has a 3.14-star weighted average rating over 22 reviews.

数据可视化 (伊利诺伊大学香槟分校/库塞拉分校)：理论重点。 数据挖掘专业化的一部分 。 估计在四个星期内每周工作四至六个小时。 提供免费和付费选项。 它在22条评论中拥有3.14星级加权平均评分。

Data Visualization and D3.js (Udacity): Balanced theory/tool focus. The D3.js instruction feels “incomplete” and “out of place.” Estimated timeline of seven weeks. Free. It has a 2.83-star weighted average rating over 6 reviews.

数据可视化和D3.js (Udacity)：平衡的理论/工具重点。 D3.js指令感觉“不完整”和“不合适”。 预计时间表为七个星期。 自由。 它在6条评论中获得2.83星级加权平均评分。

Data Management and Visualization (Wesleyan University/Coursera): Balanced theory/tool focus. Covers multiple tools (Python and SAS). Part of Wesleyan’s Data Analysis and Interpretation Specialization. Estimated timeline of four to five hours per week over four weeks. Free and paid options available. It has a 2.67-star weighted average rating over 6 reviews.

数据管理和可视化 (卫斯理大学/库塞拉)：平衡的理论/工具重点。 涵盖多种工具(Python和SAS)。 Wesleyan的数据分析和解释专业化的一部分 。 在四个星期内，每周估计需要四到五个小时。 提供免费和付费选项。 在6条评论中，它获得了2.67星级加权平均评分。

Applied Plotting, Charting & Data Representation in Python (University of Michigan/Coursera): Balanced theory and tool focus. Free and paid options available. It has a 2-star weighted average rating over 4 reviews.

Python中的应用绘图，制图和数据表示 (密歇根大学/库塞拉大学)：兼顾理论和工具重点。 提供免费和付费选项。 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 March 2017.

截至2017年3月，以下课程没有任何评论。

Data Visualization in Tableau (Udacity): Theory focus with excellent coverage. Brief tool coverage (Tableau). Primarily text-based instruction with multiple choice quizzes. Part of Udacity’s Data Analyst Nanodegree and Predictive Analytics for Business Nanodegree. This course is likely bound for a top three spot when updated with videos to complement the text. Estimated timeline of three weeks. Free.

Tableau (Udacity)中的数据可视化 ：理论重点突出，覆盖面广。 简短的工具介绍(Tableau)。 主要是基于文本的指令，具有多项选择测验。 Udacity的数据分析师纳米度和业务纳米度预测分析的一部分 。 当使用视频更新文字以补充内容时，本课程可能会排在前三位。 预计时间表为三周。 自由。

Building Data Visualization Tools (Johns Hopkins University/Coursera): Tool focus (R and ggplot2). Part of JHU’s Mastering Software Development in R Specialization. Estimated timeline of two hours per week over four weeks. Free and paid options available.

建筑数据可视化工具 (Johns Hopkins University / Coursera)：工具重点(R和ggplot2)。 JHU R专业化Mastering Software Development的一部分 。 估计的时间表是在四个星期内每周两个小时。 提供免费和付费选项。

Data Visualization for All (Trinity College/edX): Theory focus. Estimated timeline of three hours per week over six weeks. Free with Verified Certificate available for purchase.

所有人的数据可视化 (Trinity College / edX)：理论重点。 预计在六个星期内每周三个小时的时间表。 免费提供可购买的已验证证书。

Data Visualization with Advanced Excel (PwC/Coursera): Tool focus (Excel). Part of PwC’s Data Analysis and Presentation Skills: the PwC Approach Specialization. Estimated timeline of three to four hours per week over four weeks. Free and paid options available.

使用高级Excel (PwC / Coursera)进行数据可视化 ：以工具为中心(Excel)。 普华永道数据分析和演示技巧的一部分：普华永道方法专业化 。 在四个星期内，每周估计需要三到四个小时的时间表。 提供免费和付费选项。

Communicating Business Analytics Results (University of Colorado Boulder/Coursera): Theory and business focus. Part of Colorado Boulder’s Data Analytics for Business Bootcamp Specialization. Estimated timeline of four weeks. Free and paid options available.

传达业务分析结果 (科罗拉多大学博尔德分校/库塞拉分校)：理论和业务重点。 科罗拉多博尔德数据分析的一部分，用于商业训练营专业化 。 预计时间表为四个星期。 提供免费和付费选项。

Storytelling Through Data Visualization (Dataquest): Mostly a tool focus (Python, Matplotlib, and Seaborn). Estimated timeline unclear. Mostly free, but a subscription is required for full access.

通过数据可视化讲故事 (Dataquest)：主要关注工具(Python，Matplotlib和Seaborn)。 估计时间表不清楚。 大多数情况下是免费的，但要完全访问，需要订阅 。

Data Visualization Learning Path (O’Reilly): Balanced tool/theory focus. Covers D3.js. Multiple instructors. Fifteen hours of content. Free with a ten-day free trial.

数据可视化学习路径 (O'Reilly)：平衡的工具/理论重点。 涵盖了D3.js。 多名教官。 十五个小时的内容。 免费试用十天。

Data Visualization for Developers (Dan Appleman/Pluralsight): Theory focus. Tailored for developers. Two hours of content. Free with a ten-day free trial.

开发人员的数据可视化 (Dan Appleman / Pluralsight)：理论重点。 专为开发人员量身定制。 两个小时的内容。 免费试用十天。

The following four courses are created by Bill Shander of Beehive Media and offered on Lynda. They are listed in chronological order by release date.

以下四门课程由Beehive Media的Bill Shander创建，并在Lynda上提供。 它们按发布日期的时间顺序列出。

Data Visualization Fundamentals (Bill Shander/Lynda): Theory focus. Four hours of content. Free with a ten-day free trial.

数据可视化基础知识 (Bill Shander / Lynda)：理论重点。 四个小时的内容。 免费试用十天。

Designing a Data Visualization (Bill Shander/Lynda): Theory focus. Covers creating a specific project from concept to data analysis to design and execution. Four hours of content. Free with a ten-day free trial.

设计数据可视化 (Bill Shander / Lynda)：理论重点。 涵盖创建从概念到数据分析再到设计和执行的特定项目。 四个小时的内容。 免费试用十天。

Data Visualization for Data Analysts (Bill Shander/Lynda): Theory focus. Tailored for data analysts. Two hours of content. Free with a ten-day free trial.

数据分析师的数据可视化 (Bill Shander / Lynda)：理论重点。 专为数据分析师而设计。 两个小时的内容。 免费试用十天。

Data Visualization Storytelling Essentials (Bill Shander/Lynda): Theory focus. Two hours of content. Free with a ten-day free trial.

数据可视化讲故事要点 (Bill Shander / Lynda)：理论重点。 两个小时的内容。 免费试用十天。

Visualization in R, From Beginner to Advanced (Nathan Yau/FlowingData): A four-week course. Subscription required.

R中的可视化，从入门到高级 (Nathan Yau / FlowingData)：为期四周的课程。 需要订阅 。

The following four courses are offered by DataCamp. As noted above, DataCamp’s hybrid teaching style leverages video and text-based instruction with lots of examples through an in-browser code editor.

DataCamp提供以下四门课程。 如上所述，DataCamp的混合教学风格通过浏览器内代码编辑器利用基于视频和文本的教学以及许多示例。

Data Visualization in R (DataCamp): Balanced theory/tool focus. Covers base R graphics. Estimated timeline of four hours. Subscription required for full access.

R中的数据可视化 (DataCamp)：平衡的理论/工具重点。 涵盖基本R图形。 预计时间为四个小时。 要完全访问，需要订阅。

Introduction to Data Visualization with Python (DataCamp): Tool focus (Python, Matplotlib, and Seaborn). Estimated timeline of four hours. Subscription required for full access.

Python数据可视化简介 (DataCamp)：关注工具(Python，Matplotlib和Seaborn)。 预计时间为四个小时。 要完全访问，需要订阅。

Interactive Data Visualization with Bokeh (DataCamp): Tool focus (Python and Bokeh). Estimated timeline of four hours. Subscription required for full access.

使用Bokeh (DataCamp)进行交互式数据可视化 ：关注工具(Python和Bokeh)。 预计时间为四个小时。 要完全访问，需要订阅。

Data Visualization in R with ggvis (DataCamp): Balanced theory/tool focus. Covers R and ggvis. Estimated timeline of four hours. Subscription required for full access.

使用ggvis (DataCamp) 在R中进行数据可视化 ：平衡理论/工具。 涵盖R和ggvis。 预计时间为四个小时。 要完全访问，需要订阅。

结语 (Wrapping it up)

This is the fourth of a six-piece series that covers the best online courses for launching yourself into the data science field. We covered programming in the first article, statistics and probability in the second article, and intros to data science in the third article. The remainder of the series will cover other data science core competencies. Next up is machine learning.

这是一个由六部分组成的系列文章的第4部分，该系列涵盖了使您入门数据科学领域的最佳在线课程。 我们在第一篇文章中介绍了编程，在第二篇文章中介绍了统计和概率，在第三篇文章中介绍了数据科学。 该系列的其余部分将涵盖其他数据科学核心能力。 接下来是机器学习。

If you want to learn Data Science, start with one of these programming classesmedium.freecodecamp.comIf you want to learn Data Science, take a few of these statistics classesmedium.freecodecamp.comI ranked every Intro to Data Science course on the internet, based on thousands of data points medium.freecodecamp.com

如果您想学习数据科学，请从以下其中一种编程课程 medium.freecodecamp.com开始。 如果您想学习数据科学，请选择其中一些统计课程 medium.freecodecamp.com 。互联网，基于数千个数据点 medium.freecodecamp.com

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

最后的文章将是这些文章的摘要，以及有关其他关键主题的最佳在线课程，例如数据整理，数据库甚至软件工程。

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

如果您正在寻找数据科学在线课程的完整列表，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 my original article published on Class Central, where I’ve included further course descriptions, syllabi, and multiple reviews.

这是我在Class Central上发表的原始文章的精简版本，其中包括更多的课程说明，课程大纲和多篇评论。

翻译自: https://www.freecodecamp.org/news/an-overview-of-every-data-visualization-course-on-the-internet-9ccf24ea9c9b/

数据可视化课程