udacity开源的数据_评论：Udacity数据分析师纳米学位计划

udacity开源的数据

by David Venturi

大卫·文图里(David Venturi)

评论：Udacity数据分析师纳米学位计划 (Review: Udacity Data Analyst Nanodegree Program)

Udacity’s Data Analyst Nanodegree program was one of the first online data science programs in the online education revolution. It aims to “ensure you master the exact skills necessary to build a career in data science.” Does it accomplish its goal? Is it the best option available?

Udacity的Data Analyst Nanodegree计划是在线教育革命中最早的在线数据科学计划之一。 它旨在“确保您掌握建立数据科学职业所需的确切技能。” 它实现了目标吗？ 它是最好的选择吗？

I completed the program in Fall 2016. Using inspiration from Class Central’s open-source review template, here is my review for Udacity’s Data Analyst Nanodegree program.

我于2016年秋季完成了该计划。借鉴Class Central的开源审查模板的启发，这是我对Udacity的Data Analyst Nanodegree计划的审查。

UPDATE: The Data Analyst Nanodegree program was refreshed with new content and student services in September 2017. Details here. I was also brought on board to help recreate some of this new content. The majority of this review is unchanged. Factual updates are indicated by italic font.

更新： 数据分析Nanodegree计划于9月与新的内容和学生服务2017年刷新细节在这里 。 我也被带去帮助重新创建一些新内容。 此评论的大部分内容保持不变。 实际更新以斜体字体表示。

背景资料 (Background information)

是什么让我决定参加此课程的？ (What made me decide to take this program?)

In early 2016, I started creating my own data science master’s program using online resources. (You can read about that here.) I enrolled in the Data Analyst Nanodegree program for a few reasons:

2016年初，我开始使用在线资源创建自己的数据科学硕士课程。 (您可以在此处阅读有关内容。)我注册Data Analyst Nanodegree程序有以下几个原因：

• I wanted a guide for my introduction to data science.
  我想要一个有关数据科学入门的指南。
• I wanted a cohesive program instead of individual courses from a variety of providers.
  我想要一个有凝聚力的计划，而不是来自各种提供商的单独课程。

• It received stellar reviews.

  它得到了好评 。

• I had taken a few Udacity courses before and I was a fan of their teaching style.
  之前我参加过一些Udacity课程，并且我很喜欢他们的教学风格。

我的目标是什么？ (What were my goals?)

Though the program can act as a bridge to a job (more on that later), I wanted to use the program as an introduction to more advanced material. This “more advanced material” applies to both subjects that are covered in the program and subjects that aren’t.

尽管该程序可以充当工作的桥梁(稍后会详细介绍)，但我还是想将该程序用作对更高级材料的介绍。 此“更高级的材料”适用于计划中涵盖的主题和未涵盖的主题。

什么是Udacity纳米学位课程？ (What is a Udacity Nanodegree program?)

Udacity is one of the leading online education providers. Sebastian Thrun, ex-Stanford professor and Google X founder, founded the company and focuses on innovation at Udacity as president and chairman. Vish Makhijani is CEO.

Udacity是领先的在线教育提供商之一。 斯坦福大学前教授，Google X创始人塞巴斯蒂安·特伦(Sebastian Thrun)创立了公司，并在Udacity担任总裁兼董事长，致力于创新。 Vish Makhijani是首席执行官 。

Nanodegree programs are online credentials provided by Udacity. They are compilations of Udacity courses (some available for free, others not) that have projects attached to them, which are reviewed by Udacity’s paid project reviewers. They also come with a bunch of student services.

纳米学位课程是Udacity提供的在线凭证。 它们是Udacity课程的汇编(有些是免费提供的，有些不是免费的)，这些课程已附加项目，并由Udacity的付费项目审阅者进行审阅。 他们还提供大量学生服务。

Slack is used as a community tool, where Udacity students can interact with other students as well as their program’s instructors and other Udacity staff. In most programs, students have assigned mentors and communicate with them through a private chat channel that is always available in the Udacity classroom.

Slack用作社区工具，Udacity的学生可以在其中与其他学生以及他们的计划的讲师和其他Udacity员工进行交互。 在大多数计划中，学生分配了导师并通过Udacity教室中始终可用的私人聊天频道与他们进行交流。

The Data Analyst Nanodegree program was originally released in 2014. It was Udacity’s second Nanodegree program. Though it has undergone some changes over the years, the core of the program is intact.

Data Analyst纳米学位计划最初于2014年发布。它是Udacity的第二个纳米学位计划。 尽管多年来已经发生了一些变化，但该计划的核心是完整​​的。

谁是讲师，他们的背景是什么？ (Who are the instructors and what are their backgrounds?)

Because the Data Analyst Nanodegree program is a compilation of Udacity courses (again, some free, others not), there are several instructors. Their resumes often include prestigious roles in major tech companies and degrees from top U.S. schools.

由于Data Analyst Nanodegree程序是Udacity课程的汇编(同样，有些是免费的，有些则不是)，因此有几位讲师。 他们的简历通常包括在大型科技公司中的重要角色以及美国顶尖学校的学位。

They aren’t “instructors” per se, but Udacity’s project reviewers, mentors, and student experience staff (who monitor Slack along with instructors) are among the people you interact with the most. They are so, so helpful. More on that later.

他们本身并不是“讲师”，但是与您互动最多的人是Udacity的项目审阅者， 导师和学生体验人员(他们与讲师一起监控Slack) 。 他们是如此，非常有帮助。 以后再说。

成本 (Cost)

The program is split into two terms. The first term costs $499 USD. The second term costs $699 USD. If you have a strong grasp on the skills taught in the first term, you can skip it, complete the second term only, and still obtain the credential.

该程序分为两个术语。 第一学期的费用为499美元。 第二学期的费用为699美元。 如果您对第一学期所教授的技能有很强的把握，则可以跳过该课程，仅完成第二学期，仍然获得证书。

建议的先决条件 (Recommended prerequisites)

For Term 1, Udacity recommends that students are familiar with descriptive statistics and have some experience working with data in spreadsheets or SQL.

对于第一学期，Udacity建议学生熟悉描述性统计数据，并具有处理电子表格或SQL中的数据的经验。

For Term 2, students should have experience analyzing data using Python, as well as a solid understanding of inferential statistics and its applications.

对于第二学期，学生应具有使用Python分析数据的经验，并对推理统计及其应用有扎实的理解。

我的背景/进入程序的技能 (My background / skills entering the program)

I started the program in May 2016 when I had a few months of programming experience, mostly in C and Python. The vast majority of this experience was from the bridging module for my data science master’s program, where I took Harvard’s CS50: Introduction to Computer Science and Udacity’s Intro to Programming Nanodegree program.

我于2016年5月开始该程序，当时我有几个月的编程经验，主要是使用C和Python。 这些经验的绝大部分来自于我的数据科学硕士课程的桥接模块，在那里我学习了哈佛大学的CS50：计算机科学入门和Udacity的程序设计纳米学位入门 。

I had also finished my undergraduate chemical engineering program and had 24 months of quant-related job experience. This meant I had taken several statistics courses and was comfortable with data.

我还完成了本科化学工程课程，并且拥有24个月与量化相关的工作经验。 这意味着我参加了几门统计学课程并且对数据感到满意。

该程序 (The Program)

结构体 (Structure)

The Data Analyst Nanodegree program is split up into two terms. Each term has three courses and four projects (the extra project being an intro project that helps you get used to the Udacity learning environment). Mat Leonard, the program’s curriculum lead at the time of the refresh, is present throughout the program as he introduces each course, its purpose in the program, and its instructor(s).

Data Analyst Nanodegree程序分为两个术语。 每个学期都有三门课程和四个项目(额外的项目是一个介绍性项目，可以帮助您适应Udacity的学习环境)。 Mat伦纳德 ( Mat Leonard)是刷新时该课程的课程负责人，在他介绍每门课程，其在课程中的目的以及其讲师时，他在课程中始终存在。

Course content is made up of a combination of videos, text, and quizzes. Videos tend to range from 30 seconds to five minutes, as per Udacity’s style. Automatically graded quizzes often follow these short videos. These quizzes are usually multiple choice, fill-in-the-blank, or small programming tasks. After acquiring CloudLabs, these programming tasks are now carried out in Jupyter Notebook and SQL coding environments in the Udacity classroom.

课程内容由视频，文本和测验组成。 根据Udacity的风格，视频的长度通常在30秒到5分钟之间。 这些短片通常会自动评分。 这些测验通常是多项选择，填空或小型编程任务。 收购CloudLabs之后 ，现在可以在Udacity教室的Jupyter Notebook和SQL编码环境中执行这些编程任务。

Again, each section has a graded project. These projects and the feedback from Udacity’s paid project reviewers are where a lot of the value lies for students.

同样，每个部分都有一个分级项目。 这些项目以及Udacity付费项目审阅者的反馈对学生来说是很多价值所在。

教学大纲 (Syllabus)

My edition of the program had seven parts:

我的程序版本包括七个部分：

• P1: Descriptive and Inferential Statistics
  P1：描述性和推断性统计
• P2: Intro to Data Analysis (with NumPy and pandas)
  P2：数据分析简介(使用NumPy和Pandas)
• P3: Data Wrangling with MongoDB (or SQL)
  P3：使用MongoDB(或SQL)处理数据
• P4: Exploratory Data Analysis (with R)
  P4：探索性数据分析(带R)
• P5: Intro to Machine Learning
  P5：机器学习入门
• P6: Data Visualization with D3.js
  P6：使用D3.js进行数据可视化
• P7: Design an A/B Test
  P7：设计A / B测试

The new program’s first term is called Data Analysis with Python and SQL. The courses and projects include:

新程序的第一个术语称为使用Python和SQL进行数据分析 。 这些课程和项目包括：

• Intro project: Explore Weather Trends. SQL and spreadsheets (or Python/R if you are already familiar) are used to analyze and visualize temperature data.

  简介项目： 探索天气趋势。 SQL和电子表格(如果您已经熟悉，则为Python / R)用于分析和可视化温度数据。

• Course: Introduction to Python. Project: Explore US Bikeshare Data.

  课程： Python入门。 项目：探索美国Bikeshare数据。

• Course: Introduction to Data Analysis, which includes The Data Analysis Process and SQL for Data Analysis. Project: Investigate a Dataset.

  课程： 数据分析简介，其中包括数据分析过程和用于数据分析SQL。 项目：研究数据集。

• Course: Practical Statistics. Project: Analyze A/B Test Results.

  课程： 实践统计。 项目：分析A / B测试结果。

The second term is called Advanced Data Analysis. The courses and projects include:

第二个术语称为高级数据分析 。 这些课程和项目包括：

• Intro project: Test a Perceptual Phenomenon. Compute descriptive statistics and perform a statistical test on a dataset based on a psychological phenomenon called the Stroop Effect.

  简介项目： 测试一种感知现象。 基于称为Stroop效应的心理现象，计算描述性统计数据并对数据集进行统计检验。

• Course: Data Wrangling (with Python). Project: Wrangle and Analyze Data. This is the course and project that I created. ?

  课程： 数据整理(使用Python)。 项目： Wrangle和分析数据。 这是我创建的课程和项目。 ？

• Course: Exploratory Data Analysis (with R). Project: Explore and Summarize Data.

  课程： 探索性数据分析(带R)。 项目：探索和汇总数据。

• Course: Data Storytelling (with Tableau). Project: Create a Tableau Story.

  课程： 数据故事讲述(与Tableau一起使用)。 项目：创建一个Tableau Story。

The big changes, with full details described in this blog post:

重大更改，此博客文章中描述了全部详细信息：

• Python is now taught in the program.

  现在在程序中教授Python。

• Machine Learning and A/B Testing are now included as optional material and are no longer requirements to graduate from the program. Reasoning: “The focus of this program is to prepare you for data analyst jobs. Our research shows that machine learning is not a requirement for the vast majority of data analyst positions.” The basics of A/B testing are now covered in the new practical stats course, giving students the exposure that they’ll need on the job.

  现在，机器学习和A / B测试已作为可选材料包括在内，不再需要从该程序中毕业。 推理：“该计划的重点是为您做好数据分析师工作做准备。 我们的研究表明，机器学习并不是绝大多数数据分析师职位的必要条件。” 新的实用统计课程现在涵盖了A / B测试的基础知识，使学生有工作所需的知识。

• New courses and projects. Specifically, Intro to Data Analysis (which includes Python for Data Analysis and SQL for Data Analysis), Practical Statistics (taught by Sebastian Thrun), and Data Wrangling.

  新课程和新项目。 具体来说，数据分析简介(包括用于数据分析的Python和用于数据分析SQL)，实用统计(由Sebastian Thrun教授)和数据整理。

Grading

等级

Projects are graded on a pass/fail (officially, “meets specifications” and “requires changes”) basis according to a unique rubric. Your project must satisfy all sections of the rubric. If all of your projects meet specifications, you graduate. This means that the automatically-graded quizzes do not count towards your grade.

根据唯一的评判标准对项目的通过/失败(正式地，“符合规格”和“需要更改”)进行分级。 您的项目必须满足所有规则。 如果您所有的项目都符合规范，那么您就毕业了。 这意味着自动评分的测验不会计入您的成绩。

If a project submission requires changes, your project reviewer will give you actionable feedback. After you implement these changes, you can resubmit. There is no submission limit.

如果项目提交需要更改，则项目审阅者将为您提供可行的反馈。 实施这些更改后，您可以重新提交。 没有提交限制。

我的经验 (My experience)

时间线 (Timeline)

Udacity’s estimated timeline for the Data Analyst Nanodegree program was 378 hours when I started, which meant students took 6–7 months on average to complete it. According to Toggl (a time tracking app), the whole program took me 369 hours over five months. This timeline included dedicating serious time to making my projects portfolio-quality, as opposed to producing the minimum to satisfy the pass/fail rubric.

我刚开始时，Udacity估计的Data Analyst纳米学位课程的时间表为378小时，这意味着学生平均需要6-7个月才能完成该课程。 根据Toggl (一个时间跟踪应用程序)，整个程序在五个月内花了我369个小时。 这个时间表包括花大量的时间来提高我的项目的投资组合质量，而不是花最少的时间来满足通过/失败的标准。

The program was condensed in the Fall 2017 refresh. The new estimated timeline is 260 hours. Each term is paced at 10 hours per week over 13 weeks, though students are given 19 weeks to complete each term.

该程序在2017年秋季更新中得到了压缩。 新的预计时间表是260小时 。 每个学期的课程安排为在13周内每周10个小时，尽管学生有19周的时间完成每个学期。

课程内容如何？ (How was the course content?)

For my edition of the program, the course content from P1 (Statistics), P2 (Intro to Data Analysis), P4 (Exploratory Data Analysis), P5 (Machine Learning), and P7 (A/B Testing) get five stars out of five from me. P3 (Data Wrangling) and P6 get three-and-a-half stars.

在我的程序版本中，P1(统计)，P2(数据分析入门)，P4(探索性数据分析)，P5(机器学习)和P7(A / B测试)的课程内容获得5星我五个。 P3(数据整理)和P6获得三颗半星。

The exploratory data analysis content with Facebook employees (P4) was so illuminating. The intro to machine learning course with Sebastian Thrun and Katie Malone (P5) was the most fun I’ve had in any online course. The A/B testing content with Google employees (P7) is so unique. I’d give those three courses six stars if I could.

与Facebook员工(P4)进行的探索性数据分析内容非常具有启发性。 Sebastian Thrun和Katie Malone(P5)开设的机器学习课程入门是我在任何在线课程中获得的最大乐趣。 Google员工(P7)的A / B测试内容是如此独特。 如果可以的话，我会给这三个课程六个星。

The SQL and Data Wrangling content (P3) weren’t amazing. Same with the data visualization content (P6), though that probably was because D3.js is super difficult to teach to JavaScript newbies. These opinions aren’t uncommon, according to the Class Central’s reviews for those courses. Check them out here and here.

SQL和数据整理内容(P3)并不令人惊讶。 与数据可视化内容(P6)相同，但这可能是因为D3.js很难向JavaScript新手教。 根据Class Central对这些课程的评论，这些意见并不少见。 在这里和这里检查一下 。

This “not amazing” content from the old program was removed in the Fall 2017 refresh. Revamped content for intro to data analysis, SQL, statistics, data wrangling, and data visualization is now included. The Practical Statistics content focuses on inferential statistics, with descriptive statistics being a prerequisite and taught in the Data Foundations Nanodegree program. The data visualization course is now taught with Tableau instead of D3.js.

旧程序中的此“不惊人”内容已在2017年秋季更新中删除 。 现在包括用于数据分析，SQL，统计信息，数据整理和数据可视化的新内容。 实用统计学的内容侧重于推论统计学，描述性统计学是前提条件，并在Data Foundations Nanodegree程序中进行了讲授。 现在使用Tableau而不是D3.js讲授数据可视化课程。

项目进展如何？ (How were the projects?)

Again, projects are where Udacity sets themselves apart from the rest of the online education platforms. They invest in their project review process and it pays off. The Data Analyst Nanodegree program was no exception.

再次，项目是Udacity与其他在线教育平台区分开来的地方。 他们在项目审查过程中进行了投资，并且得到了回报。 Data Analyst Nanodegree程序也不例外。

All of the projects reinforce the content you learned in the videos. The project reviewers know their stuff. They tell you where you succeeded and where your mistakes and/or omissions are. Supervised learning by doing. It works.

所有项目都巩固了您在视频中学到的内容。 项目审阅者知道他们的东西。 他们会告诉您成功的地方以及错误和/或遗漏的地方。 有监督地边做边学。 有用。

The forums and the forum mentors are especially helpful when you get stuck. Search the forums to see if your problem is a common one (they usually are). No luck? Post a new question yourself. There is one forum mentor, Myles Callan, who seems to know everything about everything and responds within hours. I have my doubts that he sleeps.

当您遇到困难时，论坛和论坛指导者特别有用。 搜索论坛以查看您的问题是否很常见(通常是)。 没运气？ 自己发布一个新问题。 有一位论坛指导者Myles Callan，他似乎了解所有事情，并在数小时内做出回应。 我怀疑他睡着了。

Though forums still exist and work, Slack and classroom mentors are now the recommended support avenues. Students can post questions, and answers are provided with the same or greater level of immediacy (within hours and often sooner). The Slack community is overseen by Udacity instructors as well as their student experience staff, who ensure that student questions, comments, etc. are addressed in a timely fashion. The famed Myles Callan is now a mentor.

尽管论坛仍然存在并且可以正常工作，但是现在推荐使用Slack和课堂指导者作为支持途径。 学生可以发布问题，并在相同或更高级别的即时性下(在几个小时内，通常更快)提供答案。 Slack社区由Udacity讲师及其学生体验人员监督，他们确保及时解决学生的问题，评论等。 著名的迈尔斯·卡伦(Myles Callan)现在是一名导师。

If you’re curious to see what these projects look like, check out this Github repository.

如果您想看看这些项目的样子，请查看此Github存储库 。

有多难？ (How hard was it?)

The statistics content was easy for me because I had taken several stats courses in undergrad. This would probably be true for every topic in the Nanodegree program if you had prior experience in it.

统计数据内容对我来说很容易，因为我在本科生上过几门统计学课程。 如果您已有纳米学位课程的经验，那么这对于每一个主题都是正确的。

I’d categorize most of the program as intermediate difficulty. Lecture content that doesn’t have many quizzes (they often do, though) can be a breeze, which isn’t necessarily a bad thing. The projects exercise your brain. Each will probably take you more than twenty hours if you want to be thorough.

我会将大多数程序归为中等难度。 没有很多测验的演讲内容(尽管经常有)，可以轻而易举，这不一定是一件坏事。 这些项目可以锻炼您的大脑。 如果您想彻底了解，每个过程可能会花费您20多个小时。

The Exploratory Data Analysis project was the most challenging to pass. It took me 3.5 submissions. Check out this Twitter thread for more details.

探索性数据分析项目是最具挑战性的。 我花了3.5份意见书。 查看此Twitter线程以了解更多详细信息。

您可以在毕业后立即申请工作吗？ (Can you apply for jobs immediately post-graduation?)

You can. The program should equip you with the required skills for an entry-level data analyst role if you take it seriously. Eli Kastelein is a perfect example of that. You can read more about his story below.

您可以。 如果您认真对待的话，该程序应该为您提供入门级数据分析师角色所需的技能。 Eli Kastelein就是一个很好的例子。 您可以在下面阅读有关他的故事的更多信息。

How to Build a Career in Tech Without a CS DegreeIn the spring of 2014, I was a fresh college dropout on a Greyhound bus headed nowhere in particular.medium.com

如何在没有CS学士学位的情况下建立技术职业 2014年Spring，我是乘坐灰狗公车上大学的新人，头也不回。 medium.com

You can also continue onto more advanced courses, both for the subjects covered in the program and for other subjects. This is what I chose to do.

您还可以继续学习更高级的课程，包括该课程涵盖的主题和其他主题。 这就是我选择要做的。

最后的想法 (Final thoughts)

我是否会再次知道我现在知道的程序？ (Would I take the program again knowing what I know now?)

Somewhere towards the end of the program, I started creating Class Central’s Data Science Career Guide. This entailed researching every single online course offered for every subject within data science.

在计划结束的某个地方，我开始创建Class Central的《 数据科学职业指南》 。 这需要研究为数据科学中的每个学科提供的每一个在线课程。

Though I enjoyed the majority of courses within the Nanodegree program (update: new courses have replaced the courses I didn’t enjoy), there are courses from other providers that receive better reviews for certain subjects. Statistics, for example. If I had access to my guide back when I started, I would consider the separate-course-for-each-subject route. Udacity’s student services and project review process, however, are so effective for learning that I would take the Data Analyst Nanodegree program regardless.

尽管我喜欢Nanodegree计划中的大多数课程(更新：新课程取代了我不喜欢的课程) ，但有些其他提供商的课程则对某些学科给予了更好的评价。 例如， 统计信息 。 如果我一开始就可以访问我的指南，那么我将考虑针对每个主题的单独课程路线。 但是，Udacity的学生服务和项目审核过程对于学习是如此有效，以至于无论如何我都会参加Data Analyst纳米学位课程。

If you’re the kind of person who wants a 100% custom online education experience but wants to take advantage of Udacity’s projects and services, researching your favorite courses for each subject (I recommend using Class Central) then enrolling in the Nanodegree program to complete the projects is something to consider.

如果您是那种希望获得100％自定义在线教育经验，但又想利用Udacity的项目和服务的人，请针对每个主题研究自己喜欢的课程(我建议使用Class Central )，然后注册Nanodegree计划以完成这些项目是要考虑的。

替代品 (The alternatives)

These are the five alternative programs that I was considering when I enrolled in the Data Analyst Nanodegree program:

这些是我注册Data Analyst Nanodegree程序时正在考虑的五个替代程序：

• Johns Hopkins University’s Data Science Specialization on Coursera

  约翰霍普金斯大学Coursera的数据科学专业

• Microsoft’s Professional Program Certificate in Data Science on edX

  edX上的Microsoft 数据科学专业计划证书

• Wesleyan University’s Data Analysis and Interpretation Specialization on Coursera

  卫斯理大学在Coursera上的数据分析和解释专业

• DataCamp’s Python and R tracks

  DataCamp的Python和R轨道

• Dataquest’s Data Analyst and Data Scientist paths

  Dataquest的数据分析师和数据科学家路径

Note: I have removed my comments on these programs due to Udacity policy regarding commenting on other providers.

注意：由于Udacity关于对其他提供商进行评论的政策，我已删除了对这些程序的评论。

结论 (Conclusion)

Udacity’s Data Analyst Nanodegree program gives you the foundational skills you need for a career in data science. Post-graduation, you’ll be able to target your strengths and weaknesses, and supplement your learning where necessary. Plus, you’ll leave with a handful of portfolio-ready projects.

Udacity的Data Analyst Nanodegree程序为您提供从事数据科学职业所需的基础技能。 毕业后，您将能够针对自己的长处和短处，并在必要时补充学习内容。 另外，您将离开一些准备就绪的项目。

I loved it, as did others.

我喜欢它， 其他人也喜欢。

★★★★¾

★★★★¾

翻译自: https://www.freecodecamp.org/news/review-udacity-data-analyst-nanodegree-1e16ae2b6d12/

udacity开源的数据