大數據入門課程_我根據數千個數據點對互聯網上的每門數據科學入門課程進行了排名...

大數據入門課程

by David Venturi

大衛·文圖里(David Venturi)

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計算機科學程序之一。我開始使用在線資源創建自己的數據科學碩士課程。我意識到我可以通過edX，Coursera和Udacity學習所需的一切。而且我可以更快，更有效地學習它，而費用卻只有一小部分。

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.

我現在快要完蛋了。我參加了許多與數據科學相關的課程，并對更多課程進行了審計。我知道那里的選擇，以及學習者準備數據分析師或數據科學家角色需要哪些技能。 幾個月前，我開始創建一個以評論為導向的指南，為數據科學中的每個學科推薦最佳課程。

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.

對于本系列的第一個指南，我為初學者數據科學家推薦了一些編碼類。然后是統計和概率分類。

現在介紹數據科學。 (Now onto introductions to data science.)

(Don’t worry if you’re unsure of what an intro to data science course entails. I’ll explain shortly.)

(不用擔心，如果您不確定數據科學課程的介紹會帶來什么。我將在稍后進行解釋。)

For this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 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.

對于本指南，我花了10多個小時來嘗試確定截至2017年1月提供的每門在線數據科學課程介紹，從他們的教學大綱和評論中提取關鍵信息，并編制其評分。對于此任務，我只選擇了開放源碼的Class Central社區及其包含數千個課程評分和評論的數據庫。

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年以來， Class Central的創始人Dhawal Shah一直在關注在線課程，這一點可以說是世界上其他任何人所不及的。達瓦爾親自幫助我整理了這份資源清單。

我們如何選擇要考慮的課程 (How we picked courses to consider)

Each course must fit three criteria:

每門課程必須符合三個條件：

It must teach the data science process. More on that soon.
它必須教授數據科學過程。 很快就可以了。
It must be on-demand or offered every few months.
必須按需或每幾個月提供一次。
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.
它必須是交互式的在線課程，因此沒有書籍或只讀教程 。盡管這些是可行的學習方法，但本指南重點介紹課程。

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.

我們相信，我們涵蓋了符合上述條件的所有重要課程。由于關于Udemy的課程似乎有數百種，因此我們選擇只考慮評論次數最多和評分最高的課程。不過，總有可能我們錯過了一些東西。因此，如果我們留下了好的課程，請在評論部分讓我們知道。

我們如何評估課程 (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.

我們匯總了Class Central和其他評論網站的平均評分和評論數量，以計算每個課程的加權平均評分。我們閱讀了文字評論，并使用此反饋來補充數字等級。

We made subjective syllabus judgment calls based on two factors:

我們基于兩個因素進行了主觀的課程提綱判斷：

1. Coverage of the data science process. Does the course brush over or skip certain subjects? Does it cover certain subjects in too much detail? See the next section for what this process entails.

1. 數據科學過程的覆蓋范圍。 課程會否跳過或跳過某些科目？它是否涵蓋了某些主題的過多細節？有關此過程的含義，請參見下一部分。

2. Usage of common data science tools. Is the course taught using popular programming languages like Python and/or R? These aren’t necessary, but helpful in most cases so slight preference is given to these courses.

2. 通用數據科學工具的使用。 該課程是否使用流行的編程語言(例如Python和/或R)教授？這些不是必需的，但在大多數情況下很有幫助，因此對這些課程略有偏愛。

什么是數據科學過程？ (What is the data science process?)

What is data science? What does a data scientist do? These are the types of fundamental questions that an intro to data science course should answer. The following infographic from Harvard professors Joe Blitzstein and Hanspeter Pfister outlines a typical data science process, which will help us answer these questions.

什么是數據科學？ 數據科學家做什么？ 這些是數據科學課程入門應回答的基本問題的類型。以下來自哈佛大學教授Joe Blitzstein和Hanspeter Pfister的信息圖概述了典型的數據科學過程 ，這將有助于我們回答這些問題。

Our goal with this introduction to data science course is to become familiar with the data science process. We don’t want too in-depth coverage of specific aspects of the process, hence the “intro to” portion of the title.

我們對數據科學課程的介紹的目標是要熟悉數據科學過程。我們不想太深入地介紹過程的特定方面，因此不希望標題的“簡介”部分。

For each aspect, the ideal course explains key concepts within the framework of the process, introduces common tools, and provides a few examples (preferably hands-on).

對于每個方面，理想的課程都將解釋流程框架內的關鍵概念，介紹通用工具，并提供一些示例(最好是動手實踐)。

We’re only looking for an introduction. This guide therefore won’t include full specializations or programs like Johns Hopkins University’s Data Science Specialization on Coursera or Udacity’s Data Analyst Nanodegree. These compilations of courses elude the purpose of this series: to find the best individual courses for each subject to comprise a data science education. The final three guides in this series of articles will cover each aspect of the data science process in detail.

我們只是在尋找介紹。因此，本指南將不包括約翰霍普金斯大學的Coursera 數據專業或Udacity的Data Analyst Nanodegree等完整的專業或計劃。這些課程的匯編沒有達到本系列課程的目的：為每個學科尋找最佳的個別課程，以構成數據科學教育。本系列文章中的最后三本指南將詳細介紹數據科學過程的每個方面。

所需的基本編碼，統計數據和概率經驗 (Basic coding, stats, and probability experience required)

Several courses listed below require basic programming, statistics, and probability experience. This requirement is understandable given that the new content is reasonably advanced, and that these subjects often have several courses dedicated to them.

下面列出的幾門課程需要基本的編程，統計學和概率經驗。鑒于新內容已經相當高級，并且這些主題通常都有專門針對它們的幾門課程，因此這一要求是可以理解的。

This experience can be acquired through our recommendations in the first two articles (programming, statistics) in this Data Science Career Guide.

可通過本《數據科學職業指南》的前兩篇文章( 編程，統計資料 )中的建議來獲得這種經驗。

我們選擇的最佳數據科學入門課程是…… (Our pick for the best intro to data science course is…)

Data Science A-Z?: Real-Life Data Science Exercises Included (Kirill Eremenko/Udemy)
數據科學AZ?：包括現實生活中的數據科學練習 (Kirill Eremenko / Udemy)

Kirill Eremenko’s Data Science A-Z? on Udemy is the clear winner in terms of breadth and depth of coverage of the data science process of the 20+ courses that qualified. It has a 4.5-star weighted average rating over 3,071 reviews, which places it among the highest rated and most reviewed courses of the ones considered.

Kirill Eremenko的Udemy上的Data Science AZ?在合格的20多個課程的數據科學過程的廣度和深度方面顯然是贏家。它擁有4.5顆星的加權平均評分，超過3,071條評論，使其躋身于所考慮課程中評分最高和評價最高的課程之一。

It outlines the full process and provides real-life examples. At 21 hours of content, it is a good length. Reviewers love the instructor’s delivery and the organization of the content. The price varies depending on Udemy discounts, which are frequent, so you may be able to purchase access for as little as $10.

它概述了整個過程，并提供了實際示例。在21個小時的內容中，這是一個不錯的時長。評論者喜歡講師的授課內容和內容的組織方式。價格根據Udemy的折扣而有所不同，而Udemy的折扣經常出現，因此您可以以低至10美元的價格購買訪問權限。

Though it doesn’t check our “usage of common data science tools” box, the non-Python/R tool choices (gretl, Tableau, Excel) are used effectively in context. Eremenko mentions the following when explaining the gretl choice (gretl is a statistical software package), though it applies to all of the tools he uses (emphasis mine):

盡管未選中“通用數據科學工具的使用”框，但非Python / R工具選擇(gretl，Tableau，Excel)在上下文中有效使用。 Eremenko在解釋gretl選擇(gretl是一個統計軟件包)時提到了以下內容，盡管它適用于他使用的所有工具(重點是我的)：

In gretl, we will be able to do the same modeling just like in R and Python but we won’t have to code. That’s the big deal here. Some of you may already know R very well, but some may not know it at all. My goal is to show you how to build a robust model and give you a framework that you can apply in any tool you choose. gretl will help us avoid getting bogged down in our coding.
在gretl中，我們將能夠像在R和Python中一樣進行相同的建模，但是我們不必編寫代碼。這很重要。你們中有些人可能已經非常了解R，但有些人可能根本不知道。我的目標是向您展示如何構建健壯的模型，并為您提供一個可應用于所選任何工具的框架 。 gretl將幫助我們避免陷入編碼困境。

One prominent reviewer noted the following:

一位著名的審稿人指出：

Kirill is the best teacher I’ve found online. He uses real life examples and explains common problems so that you get a deeper understanding of the coursework. He also provides a lot of insight as to what it means to be a data scientist from working with insufficient data all the way to presenting your work to C-class management. I highly recommend this course for beginner students to intermediate data analysts!

Kirill是我在網上找到的最好的老師。他使用現實生活中的示例并解釋了常見問題，以便您對課程學習有更深入的了解。對于從缺乏足夠的數據到將您的工作呈現給C級管理的全過程，成為數據科學家意味著什么，他也提供了很多見識。我強烈建議初學者向中級數據分析師推薦此課程！

出色的Python簡介 (A great Python-focused introduction)

Intro to Data Analysis (Udacity)
數據分析簡介 (Udacity)

Udacity’s Intro to Data Analysis is a relatively new offering that is part of Udacity’s popular Data Analyst Nanodegree. It covers the data science process clearly and cohesively using Python, though it lacks a bit in the modeling aspect. The estimated timeline is 36 hours (six hours per week over six weeks), though it is shorter in my experience. It has a 5-star weighted average rating over two reviews. It is free.

Udacity的數據分析入門是相對較新的產品，它是Udacity受歡迎的Data Analyst Nanodegree的一部分。盡管它在建模方面缺乏一點，但它使用Python清晰，連貫地涵蓋了數據科學過程。估計的時間范圍為36小時(六周內每周六小時)，但根據我的經驗來看時間較短。該酒店在2條評論中獲得5星級加權平均評分。這是免費的。

The videos are well-produced and the instructor (Caroline Buckey) is clear and personable. Lots of programming quizzes enforce the concepts learned in the videos. Students will leave the course confident in their new and/or improved NumPy and Pandas skills (these are popular Python libraries). The final project — which is graded and reviewed in the Nanodegree but not in the free individual course — can be a nice add to a portfolio.

視頻制作精良，講師(Caroline Buckey)清晰且風度翩翩。許多編程測驗強制實施了視頻中學習的概念。學生將對新的和/或改進的NumPy和Pandas技能(這些是流行的Python庫)充滿信心。最終項目(可以在Nanodegree中進行評分和審查，但不能在免費的個人課程中進行評估)可以很好地添加到項目組合中。

沒有評論數據的令人印象深刻的產品 (An impressive offering with no review data)

Data Science Fundamentals (Big Data University)
數據科學基礎 (大數據大學)

Data Science Fundamentals is a four-course series provided by IBM’s Big Data University. It includes courses titled Data Science 101, Data Science Methodology, Data Science Hands-on with Open Source Tools, and R 101.

數據科學基礎知識是IBM大數據大學提供的四門課程。它包括名為“ 數據科學101” ，“ 數據科學方法論” ， “使用開放源代碼工具進行數據科學動手”和“ R 101”的課程。

It covers the full data science process and introduces Python, R, and several other open-source tools. The courses have tremendous production value. 13–18 hours of effort is estimated, depending on if you take the “R 101” course at the end, which isn’t necessary for the purpose of this guide. Unfortunately, it has no review data on the major review sites that we used for this analysis, so we can’t recommend it over the above two options yet. It is free.

它涵蓋了整個數據科學過程，并介紹了Python，R和其他幾個開源工具。這些課程具有巨大的生產價值。估計需要13到18個小時的工作量，具體取決于您是否在最后完成“ R 101”課程，對于本指南而言，這不是必需的。不幸的是，它在我們用于此分析的主要評論網站上都沒有評論數據，因此我們不能在以上兩個選項中推薦它。這是免費的。

競賽 (The competition)

Our #1 pick had a weighted average rating of 4.5 out of 5 stars over 3,068 reviews. Let’s look at the other alternatives, sorted by descending rating. Below you’ll find several R-focused courses, if you are set on an introduction in that language.

在3,068條點評中，我們的＃1選擇加權平均評分為5星(滿分5星)中的4.5。讓我們看看其他選擇，按降序排列。如果您以該語言為基礎進行介紹，則可以在下面找到一些針對R的課程。

Python for Data Science and Machine Learning Bootcamp (Jose Portilla/Udemy): Full process coverage with a tool-heavy focus (Python). Less process-driven and more of a very detailed intro to Python. Amazing course, though not ideal for the scope of this guide. It, like Jose’s R course below, can double as both intros to Python/R and intros to data science. 21.5 hours of content. It has a 4.7-star weighted average rating over 1,644 reviews. Cost varies depending on Udemy discounts, which are frequent.
適用于數據科學和機器學習訓練營的Python (Jose Portilla / Udemy)：全面的過程報道，重點關注工具(Python)。較少的過程驅動，更多地是Python的非常詳細的介紹。很棒的課程，盡管不是本指南范圍的理想選擇。就像下面的Jose的R課程一樣，它既可以作為Python / R的簡介，也可以作為數據科學的簡介。 21.5小時的內容。它擁有4.7星級加權平均評分，超過1,644條評論。成本因Udemy折扣而異，這是很常見的。
Data Science and Machine Learning Bootcamp with R (Jose Portilla/Udemy): Full process coverage with a tool-heavy focus (R). Less process-driven and more of a very detailed intro to R. Amazing course, though not ideal for the scope of this guide. It, like Jose’s Python course above, can double as both intros to Python/R and intros to data science. 18 hours of content. It has a 4.6-star weighted average rating over 847 reviews. Cost varies depending on Udemy discounts, which are frequent.
帶有R的數據科學和機器學習訓練營(Jose Portilla / Udemy)：全面的過程覆蓋，重點關注工具(R)。盡管不是本指南的理想選擇，但過程驅動較少，而R.Amazing課程的介紹非常詳細。就像上面的Jose的Python課程一樣，它既可以作為Python / R的簡介，也可以作為數據科學的簡介。 18小時的內容。在847條評論中，它具有4.6星級加權平均評分。成本因Udemy折扣而異，這是很常見的。

Data Science and Machine Learning with Python — Hands On! (Frank Kane/Udemy): Partial process coverage. Focuses on statistics and machine learning. Decent length (nine hours of content). Uses Python. It has a 4.5-star weighted average rating over 3,104 reviews. Cost varies depending on Udemy discounts, which are frequent.
使用Python進行數據科學和機器學習-動手！ (弗蘭克·凱恩/烏迪米)：部分過程報道。專注于統計和機器學習。體面的長度(九個小時的內容)。使用Python。該酒店獲得3,104條評論的4.5星級加權平均評分。成本因Udemy折扣而異，這是很常見的。
Introduction to Data Science (Data Hawk Tech/Udemy): Full process coverage, though limited depth of coverage. Quite short (three hours of content). Briefly covers both R and Python. It has a 4.4-star weighted average rating over 62 reviews. Cost varies depending on Udemy discounts, which are frequent.
數據科學導論 (Data Hawk Tech / Udemy)：完整的過程覆蓋，但覆蓋深度有限。很短(三個小時的內容)。簡要介紹R和Python。它有超過62條評論的4.4星級加權平均評分。成本因Udemy折扣而異，這是很常見的。
Applied Data Science: An Introduction (Syracuse University/Open Education by Blackboard): Full process coverage, though not evenly spread. Heavily focuses on basic statistics and R. Too applied and not enough process focus for the purpose of this guide. Online course experience feels disjointed. It has a 4.33-star weighted average rating over 6 reviews. Free.
《應用數據科學：入門》 (錫拉丘茲大學/ Blackboard開放教育)：涵蓋了完整的過程，盡管分布不均。大量關注基本統計數據和R。對于本指南而言，應用過于集中，對流程的關注不足。在線課程體驗讓人感到脫節。它在6條評論中擁有4.33星級加權平均評分。自由。
Introduction To Data Science (Nina Zumel & John Mount/Udemy): Partial process coverage only, though good depth in the data preparation and modeling aspects. Okay length (six hours of content). Uses R. It has a 4.3-star weighted average rating over 101 reviews. Cost varies depending on Udemy discounts, which are frequent.
數據科學導論 (Nina Zumel和John Mount / Udemy)：盡管在數據準備和建模方面有很好的深度，但僅涵蓋了部分過程。好的長度(六個小時的內容)。使用R。在101條評論中獲得4.3星級加權平均評分。成本因Udemy折扣而異，這是很常見的。
Applied Data Science with Python (V2 Maestros/Udemy): Full process coverage with good depth of coverage for each aspect of the process. Decent length (8.5 hours of content). Uses Python. It has a 4.3-star weighted average rating over 92 reviews. Cost varies depending on Udemy discounts, which are frequent.
使用Python的應用數據科學 (V2 Maestros / Udemy)：完整的過程覆蓋范圍，并且對過程的每個方面都有很好的覆蓋深度。體面的長度(8.5小時的內容)。使用Python。該酒店在92條評論中獲得4.3星級加權平均評分。成本因Udemy折扣而異，這是很常見的。

Want to be a Data Scientist? (V2 Maestros/Udemy): Full process coverage, though limited depth of coverage. Quite short (3 hours of content). Limited tool coverage. It has a 4.3-star weighted average rating over 790 reviews. Cost varies depending on Udemy discounts, which are frequent.
想成為一名數據科學家嗎？ (V2 Maestros / Udemy)：盡管覆蓋范圍有限，但完整的過程覆蓋范圍。很短(3個小時的內容)。有限的工具覆蓋范圍。它具有790條評論中的4.3星級加權平均評分。成本因Udemy折扣而異，這是很常見的。
Data to Insight: an Introduction to Data Analysis (University of Auckland/FutureLearn): Breadth of coverage unclear. Claims to focus on data exploration, discovery, and visualization. Not offered on demand. 24 hours of content (three hours per week over eight weeks). It has a 4-star weighted average rating over 2 reviews. Free with paid certificate available.
數據到洞察力：數據分析簡介 (奧克蘭大學/ FutureLearn)：覆蓋范圍不清楚。聲稱專注于數據探索，發現和可視化。未按需提供。 24小時的內容(八周中每周三小時)。它擁有2條評論的4星級加權平均評分。免費提供付費證書。
Data Science Orientation (Microsoft/edX): Partial process coverage (lacks modeling aspect). Uses Excel, which makes sense given it is a Microsoft-branded course. 12–24 hours of content (two-four hours per week over six weeks). It has a 3.95-star weighted average rating over 40 reviews. Free with Verified Certificate available for $25.
數據科學方向 (Microsoft / edX)：部分過程覆蓋(缺少建模方面)。使用Excel，鑒于它是Microsoft品牌的課程，因此很有意義。 12-24小時的內容(六周內每周兩到四小時)。在40條評論中，它擁有3.95星的加權平均評分。免費提供經過驗證的證書，價格為25美元。
Data Science Essentials (Microsoft/edX): Full process coverage with good depth of coverage for each aspect. Covers R, Python, and Azure ML (a Microsoft machine learning platform). Several 1-star reviews citing tool choice (Azure ML) and the instructor’s poor delivery. 18–24 hours of content (three-four hours per week over six weeks). It has a 3.81-star weighted average rating over 67 reviews. Free with Verified Certificate available for $49.
數據科學基礎知識 (Microsoft / edX)：完整的過程覆蓋范圍，每個方面的覆蓋范圍都很好。涵蓋R，Python和Azure ML(Microsoft機器學習平臺)。幾篇1星評論提到了工具的選擇(Azure ML)和講師的教學效果不佳。 18-24小時的內容(六周內每周三到四小時)。在67條評論中，它獲得了3.81星級加權平均評分。免費提供經過驗證的證書，價格為$ 49。

Applied Data Science with R (V2 Maestros/Udemy): The R companion to V2 Maestros’ Python course above. Full process coverage with good depth of coverage for each aspect of the process. Decent length (11 hours of content). Uses R. It has a 3.8-star weighted average rating over 212 reviews. Cost varies depending on Udemy discounts, which are frequent.
帶R的應用數據科學 (V2 Maestros / Udemy)：V2 Maestros上面的Python課程的R伴侶。全面的過程覆蓋，對過程的每個方面都具有良好的覆蓋深度。體面的長度(11個小時的內容)。使用R。它在212條評論中獲得3.8星加權平均評分。成本因Udemy折扣而異，這是很常見的。
Intro to Data Science (Udacity): Partial process coverage, though good depth for the topics covered. Lacks the exploration aspect, though Udacity has a great, full course on exploratory data analysis (EDA). Claims to be 48 hours in length (six hours per week over eight weeks), but is shorter in my experience. Some reviews think the set-up to the advanced content is lacking. Feels disorganized. Uses Python. It has a 3.61-star weighted average rating over 18 reviews. Free.
數據科學概論(Udacity)：部分過程覆蓋，但涵蓋的主題深度不錯。盡管Udacity在探索性數據分析(EDA)方面有完整的課程，但缺乏探索方面的知識。自稱時長48小時(八周中每周六小時)，但根據我的經驗，時間較短。一些評論認為缺少針對高級內容的設置。感覺雜亂無章。使用Python。它在18條評論中獲得3.61星級加權平均評分。自由。
Introduction to Data Science in Python (University of Michigan/Coursera): Partial process coverage. No modeling and vizualization, though courses #2 and #3 in the Applied Data Science with Python Specialization cover these aspects. Taking all three courses would be too in depth for the purpose of this guides. Uses Python. Four weeks in length. It has a 3.6-star weighted average rating over 15 reviews. Free and paid options available.
Python數據科學概論 (密歇根大學/ Coursera)：部分過程覆蓋。盡管使用Python專業化的應用數據科學中的課程2和課程3涵蓋了這些方面，但是沒有建模和虛擬化。就本指南而言，修讀所有三門課程都太深入了。使用Python。長度為四個星期。它有超過15條評論的3.6星級加權平均評分。提供免費和付費選項。

Data-driven Decision Making (PwC/Coursera): Partial coverage (lacks modeling) with a business focus. Introduces many tools, including R, Python, Excel, SAS, and Tableau. Four weeks in length. It has a 3.5-star weighted average rating over 2 reviews. Free and paid options available.
數據驅動的決策 (PwC / Coursera)：以業務為重點的部分覆蓋(缺少建模)。引入了許多工具，包括R，Python，Excel，SAS和Tableau。長度為四個星期。該酒店在2條評論中擁有3.5星級加權平均評分。提供免費和付費選項。
A Crash Course in Data Science (Johns Hopkins University/Coursera): An extremely brief overview of the full process. Too brief for the purpose of this series. Two hours in length. It has a 3.4-star weighted average rating over 19 reviews. Free and paid options available.
數據科學速成課程 (約翰霍普金斯大學/庫塞拉)：整個過程的極其簡短的概述。對于本系列的目的來說太簡短了。長兩個小時。它在19條評論中擁有3.4星級加權平均評分。提供免費和付費選項。
The Data Scientist’s Toolbox (Johns Hopkins University/Coursera): An extremely brief overview of the full process. More of a set-up course for Johns Hopkins University’s Data Science Specialization. Claims to have 4–16 hours of content (one-four hours per week over four weeks), though one reviewer noted it could be completed in two hours. It has a 3.22-star weighted average rating over 182 reviews. Free and paid options available.
數據科學家工具箱 (約翰霍普金斯大學/庫塞拉)：整個過程的極其簡短的概述。更多有關約翰霍普金斯大學數據科學專業的設置課程。聲稱具有4-16小時的內容(在四個星期內每周四個小時)，盡管一位審閱者指出，它可以在兩個小時內完成。它在182條評論中擁有3.22星級加權平均評分。提供免費和付費選項。
Data Management and Visualization (Wesleyan University/Coursera): Partial process coverage (lacks modeling). Four weeks in length. Good production value. Uses Python and SAS. It has a 2.67-star weighted average rating over 6 reviews. Free and paid options available.
數據管理和可視化 (衛斯理大學/庫塞拉)：部分過程覆蓋(缺少模型)。長度為四個星期。良好的生產價值。使用Python和SAS。在6條評論中，它獲得了2.67星級加權平均評分。提供免費和付費選項。

The following courses had no reviews as of January 2017.

截至2017年1月，以下課程沒有任何評論。

CS109 Data Science (Harvard University): Full process coverage in great depth (probably too in depth for the purpose of this series). A full 12-week undergraduate course. Course navigation is difficult since the course is not designed for online consumption. Actual Harvard lectures are filmed. The above data science process infographic originates from this course. Uses Python. No review data. Free.
CS109數據科學 (哈佛大學)：全面深入地介紹了整個過程(對于本系列而言，可能太深了)。完整的12周本科課程。由于課程不是為在線消費而設計的，因此課程導航非常困難。錄制了實際的哈佛講座。上面的數據科學過程圖就是源于本課程的。使用Python。沒有評論數據。自由。

Introduction to Data Analytics for Business (University of Colorado Boulder/Coursera): Partial process coverage (lacks modeling and visualization aspects) with a focus on business. The data science process is disguised as the “Information-Action Value chain” in their lectures. Four weeks in length. Describes several tools, though only covers SQL in any depth. No review data. Free and paid options available.
商業數據分析簡介 (科羅拉多大學博爾德分校/庫塞拉分校)：部分流程覆蓋(缺少建模和可視化方面)，并且側重于業務。在他們的演講中，數據科學過程被偽裝成“信息行動價值鏈”。長度為四個星期。描述了幾種工具，盡管僅涵蓋了任何深度SQL。沒有評論數據。提供免費和付費選項。
Introduction to Data Science (Lynda): Full process coverage, though limited depth of coverage. Quite short (three hours of content). Introduces both R and Python. No review data. Cost depends on Lynda subscription.
數據科學導論 (Lynda)：盡管覆蓋范圍有限，但是完整的過程覆蓋范圍。很短(三個小時的內容)。引入了R和Python。沒有評論數據。費用取決于Lynda訂閱。

結語 (Wrapping it Up)

This is the third 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 and statistics and probability in the second article. The remainder of the series will cover other data science core competencies: data visualization and machine learning.

這是一個由六部分組成的系列文章的第3部分，該系列涵蓋了使您入門數據科學領域的最佳在線課程。我們在第一篇文章中介紹了編程，在第二篇文章中介紹了統計和概率。該系列的其余部分將涵蓋其他數據科學核心能力：數據可視化和機器學習。

If you want to learn Data Science, start with one of these programming classes

如果您想學習數據科學，請從以下編程課程之一開始

If you want to learn Data Science, take a few of these statistics classes

如果您想學習數據科學，請參加一些此類統計課程

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.

最后的文章將是這些文章的摘要，以及有關其他關鍵主題的最佳在線課程，例如數據整理，數據庫甚至軟件工程。

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.

如果您正在尋找數據科學在線課程的完整列表，可以在Class Central的數據科學和大數據主題頁面上找到它們。

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

如果您喜歡閱讀本文，請查看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.

這里有250個常春藤盟軍課程，您可以立即在線免費獲得 來自布朗，哥倫比亞，康奈爾，達特茅斯，哈佛，佩恩，普林斯頓和耶魯的250個MOOC。

The 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…

根據數據，前50名最佳的免費在線大學課程 當我于2011年11月啟動Class Central時，大約有18種左右的免費在線課程，并且幾乎所有…

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

如果您對我錯過的課程有任何建議，請在回復中告訴我！

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

如果您認為這有幫助，請單擊“？”。因此更多的人會在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.

這是我在Class Central上發表的原始文章的精簡版本，其中包括更多的課程說明，教學大綱和多篇評論。