udacity开源的数据_评论：Udacity数据分析师纳米学位计划
udacity開源的數(shù)據(jù)
by David Venturi
大衛(wèi)·文圖里(David Venturi)
評(píng)論：Udacity數(shù)據(jù)分析師納米學(xué)位計(jì)劃 (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計(jì)劃是在線教育革命中最早的在線數(shù)據(jù)科學(xué)計(jì)劃之一。 它旨在“確保您掌握建立數(shù)據(jù)科學(xué)職業(yè)所需的確切技能。” 它實(shí)現(xiàn)了目標(biāo)嗎？ 它是最好的選擇嗎？
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年秋季完成了該計(jì)劃。借鑒Class Central的開源審查模板的啟發(fā)，這是我對(duì)Udacity的Data Analyst Nanodegree計(jì)劃的審查。
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： 數(shù)據(jù)分析Nanodegree計(jì)劃于9月與新的內(nèi)容和學(xué)生服務(wù)2017年刷新細(xì)節(jié)在這里 。 我也被帶去幫助重新創(chuàng)建一些新內(nèi)容。 此評(píng)論的大部分內(nèi)容保持不變。 實(shí)際更新以斜體字體表示。
背景資料 (Background information)
是什么讓我決定參加此課程的？ (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，我開始使用在線資源創(chuàng)建自己的數(shù)據(jù)科學(xué)碩士課程。 (您可以在此處閱讀有關(guān)內(nèi)容。)我注冊(cè)Data Analyst Nanodegree程序有以下幾個(gè)原因：
- I wanted a guide for my introduction to data science. 我想要一個(gè)有關(guān)數(shù)據(jù)科學(xué)入門的指南。
- I wanted a cohesive program instead of individual courses from a variety of providers. 我想要一個(gè)有凝聚力的計(jì)劃，而不是來自各種提供商的單獨(dú)課程。
It received stellar reviews.
它得到了好評(píng) 。
- I had taken a few Udacity courses before and I was a fan of their teaching style. 之前我參加過一些Udacity課程，并且我很喜歡他們的教學(xué)風(fēng)格。
我的目標(biāo)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.
盡管該程序可以充當(dāng)工作的橋梁(稍后會(huì)詳細(xì)介紹)，但我還是想將該程序用作對(duì)更高級(jí)材料的介紹。 此“更高級(jí)的材料”適用于計(jì)劃中涵蓋的主題和未涵蓋的主題。
什么是Udacity納米學(xué)位課程？ (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是領(lǐng)先的在線教育提供商之一。 斯坦福大學(xué)前教授，Google X創(chuàng)始人塞巴斯蒂安·特倫(Sebastian Thrun)創(chuàng)立了公司，并在Udacity擔(dān)任總裁兼董事長(zhǎng)，致力于創(chuàng)新。 Vish Makhijani是首席執(zhí)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.
納米學(xué)位課程是Udacity提供的在線憑證。 它們是Udacity課程的匯編(有些是免費(fèi)提供的，有些不是免費(fèi)的)，這些課程已附加項(xiàng)目，并由Udacity的付費(fèi)項(xiàng)目審閱者進(jìn)行審閱。 他們還提供大量學(xué)生服務(wù)。
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用作社區(qū)工具，Udacity的學(xué)生可以在其中與其他學(xué)生以及他們的計(jì)劃的講師和其他Udacity員工進(jìn)行交互。 在大多數(shù)計(jì)劃中，學(xué)生分配了導(dǎo)師并通過Udacity教室中始終可用的私人聊天頻道與他們進(jìn)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納米學(xué)位計(jì)劃最初于2014年發(fā)布。它是Udacity的第二個(gè)納米學(xué)位計(jì)劃。 盡管多年來已經(jīng)發(fā)生了一些變化，但該計(jì)劃的核心是完整??的。
誰是講師，他們的背景是什么？ (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課程的匯編(同樣，有些是免費(fèi)的，有些則不是)，因此有幾位講師。 他們的簡(jiǎn)歷通常包括在大型科技公司中的重要角色以及美國(guó)頂尖學(xué)校的學(xué)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.
他們本身并不是“講師”，但是與您互動(dòng)最多的人是Udacity的項(xiàng)目審閱者， 導(dǎo)師和學(xué)生體驗(yàn)人員(他們與講師一起監(jiān)控Slack) 。 他們是如此，非常有幫助。 以后再說。
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.
該程序分為兩個(gè)術(shù)語。 第一學(xué)期的費(fèi)用為499美元。 第二學(xué)期的費(fèi)用為699美元。 如果您對(duì)第一學(xué)期所教授的技能有很強(qiáng)的把握，則可以跳過該課程，僅完成第二學(xué)期，仍然獲得證書。
建議的先決條件 (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.
對(duì)于第一學(xué)期，Udacity建議學(xué)生熟悉描述性統(tǒng)計(jì)數(shù)據(jù)，并具有處理電子表格或SQL中的數(shù)據(jù)的經(jīng)驗(yàn)。
For Term 2, students should have experience analyzing data using Python, as well as a solid understanding of inferential statistics and its applications.
對(duì)于第二學(xué)期，學(xué)生應(yīng)具有使用Python分析數(shù)據(jù)的經(jīng)驗(yàn)，并對(duì)推理統(tǒng)計(jì)及其應(yīng)用有扎實(shí)的理解。
我的背景/進(jìn)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月開始該程序，當(dāng)時(shí)我有幾個(gè)月的編程經(jīng)驗(yàn)，主要是使用C和Python。 這些經(jīng)驗(yàn)的絕大部分來自于我的數(shù)據(jù)科學(xué)碩士課程的橋接模塊，在那里我學(xué)習(xí)了哈佛大學(xué)的CS50：計(jì)算機(jī)科學(xué)入門和Udacity的程序設(shè)計(jì)納米學(xué)位入門 。
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.
我還完成了本科化學(xué)工程課程，并且擁有24個(gè)月與量化相關(guān)的工作經(jīng)驗(yàn)。 這意味著我參加了幾門統(tǒng)計(jì)學(xué)課程并且對(duì)數(shù)據(jù)感到滿意。
該程序 (The Program)
結(jié)構(gòu)體 (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程序分為兩個(gè)術(shù)語。 每個(gè)學(xué)期都有三門課程和四個(gè)項(xiàng)目(額外的項(xiàng)目是一個(gè)介紹性項(xiàng)目，可以幫助您適應(yīng)Udacity的學(xué)習(xí)環(huán)境)。 Mat倫納德 ( Mat Leonard)是刷新時(shí)該課程的課程負(fù)責(zé)人，在他介紹每門課程，其在課程中的目的以及其講師時(shí)，他在課程中始終存在。
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.
課程內(nèi)容由視頻，文本和測(cè)驗(yàn)組成。 根據(jù)Udacity的風(fēng)格，視頻的長(zhǎng)度通常在30秒到5分鐘之間。 這些短片通常會(huì)自動(dòng)評(píng)分。 這些測(cè)驗(yàn)通常是多項(xiàng)選擇，填空或小型編程任務(wù)。 收購CloudLabs之后 ，現(xiàn)在可以在Udacity教室的Jupyter Notebook和SQL編碼環(huán)境中執(zhí)行這些編程任務(wù)。
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.
同樣，每個(gè)部分都有一個(gè)分級(jí)項(xiàng)目。 這些項(xiàng)目以及Udacity付費(fèi)項(xiàng)目審閱者的反饋對(duì)學(xué)生來說是很多價(jià)值所在。
教學(xué)大綱 (Syllabus)
My edition of the program had seven parts:
我的程序版本包括七個(gè)部分：
- P1: Descriptive and Inferential Statistics P1：描述性和推斷性統(tǒng)計(jì)
- P2: Intro to Data Analysis (with NumPy and pandas) P2：數(shù)據(jù)分析簡(jiǎn)介(使用NumPy和Pandas)
- P3: Data Wrangling with MongoDB (or SQL) P3：使用MongoDB(或SQL)處理數(shù)據(jù)
- P4: Exploratory Data Analysis (with R) P4：探索性數(shù)據(jù)分析(帶R)
- P5: Intro to Machine Learning P5：機(jī)器學(xué)習(xí)入門
- P6: Data Visualization with D3.js P6：使用D3.js進(jìn)行數(shù)據(jù)可視化
- P7: Design an A/B Test P7：設(shè)計(jì)A / B測(cè)試
The new program’s first term is called Data Analysis with Python and SQL. The courses and projects include:
新程序的第一個(gè)術(shù)語稱為使用Python和SQL進(jìn)行數(shù)據(jù)分析 。 這些課程和項(xiàng)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.
簡(jiǎn)介項(xiàng)目： 探索天氣趨勢(shì)。 SQL和電子表格(如果您已經(jīng)熟悉，則為Python / R)用于分析和可視化溫度數(shù)據(jù)。
Course: Introduction to Python. Project: Explore US Bikeshare Data.
課程： Python入門。 項(xiàng)目：探索美國(guó)Bikeshare數(shù)據(jù)。
Course: Introduction to Data Analysis, which includes The Data Analysis Process and SQL for Data Analysis. Project: Investigate a Dataset.
課程： 數(shù)據(jù)分析簡(jiǎn)介，其中包括數(shù)據(jù)分析過程和用于數(shù)據(jù)分析SQL。 項(xiàng)目：研究數(shù)據(jù)集。
Course: Practical Statistics. Project: Analyze A/B Test Results.
課程： 實(shí)踐統(tǒng)計(jì)。 項(xiàng)目：分析A / B測(cè)試結(jié)果。
The second term is called Advanced Data Analysis. The courses and projects include:
第二個(gè)術(shù)語稱為高級(jí)數(shù)據(jù)分析 。 這些課程和項(xiàng)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.
簡(jiǎn)介項(xiàng)目： 測(cè)試一種感知現(xiàn)象。 基于稱為Stroop效應(yīng)的心理現(xiàn)象，計(jì)算描述性統(tǒng)計(jì)數(shù)據(jù)并對(duì)數(shù)據(jù)集進(jìn)行統(tǒng)計(jì)檢驗(yàn)。
Course: Data Wrangling (with Python). Project: Wrangle and Analyze Data. This is the course and project that I created. ?
課程： 數(shù)據(jù)整理(使用Python)。 項(xiàng)目： Wrangle和分析數(shù)據(jù)。 這是我創(chuàng)建的課程和項(xiàng)目。 ？
Course: Exploratory Data Analysis (with R). Project: Explore and Summarize Data.
課程： 探索性數(shù)據(jù)分析(帶R)。 項(xiàng)目：探索和匯總數(shù)據(jù)。
Course: Data Storytelling (with Tableau). Project: Create a Tableau Story.
課程： 數(shù)據(jù)故事講述(與Tableau一起使用)。 項(xiàng)目：創(chuàng)建一個(gè)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詳細(xì)信息：
Python is now taught in the program.
現(xiàn)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.
現(xiàn)在，機(jī)器學(xué)習(xí)和A / B測(cè)試已作為可選材料包括在內(nèi)，不再需要從該程序中畢業(yè)。 推理：“該計(jì)劃的重點(diǎn)是為您做好數(shù)據(jù)分析師工作做準(zhǔn)備。 我們的研究表明，機(jī)器學(xué)習(xí)并不是絕大多數(shù)數(shù)據(jù)分析師職位的必要條件。” 新的實(shí)用統(tǒng)計(jì)課程現(xiàn)在涵蓋了A / B測(cè)試的基礎(chǔ)知識(shí)，使學(xué)生有工作所需的知識(shí)。
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.
新課程和新項(xiàng)目。 具體來說，數(shù)據(jù)分析簡(jiǎn)介(包括用于數(shù)據(jù)分析的Python和用于數(shù)據(jù)分析SQL)，實(shí)用統(tǒng)計(jì)(由Sebastian Thrun教授)和數(shù)據(jù)整理。
Grading
等級(jí)
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.
根據(jù)唯一的評(píng)判標(biāo)準(zhǔn)對(duì)項(xiàng)目的通過/失敗(正式地，“符合規(guī)格”和“需要更改”)進(jìn)行分級(jí)。 您的項(xiàng)目必須滿足所有規(guī)則。 如果您所有的項(xiàng)目都符合規(guī)范，那么您就畢業(yè)了。 這意味著自動(dòng)評(píng)分的測(cè)驗(yàn)不會(huì)計(jì)入您的成績(jī)。
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.
如果項(xiàng)目提交需要更改，則項(xiàng)目審閱者將為您提供可行的反饋。 實(shí)施這些更改后，您可以重新提交。 沒有提交限制。
我的經(jīng)驗(yàn) (My experience)
時(shí)間線 (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.
我剛開始時(shí)，Udacity估計(jì)的Data Analyst納米學(xué)位課程的時(shí)間表為378小時(shí)，這意味著學(xué)生平均需要6-7個(gè)月才能完成該課程。 根據(jù)Toggl (一個(gè)時(shí)間跟蹤應(yīng)用程序)，整個(gè)程序在五個(gè)月內(nèi)花了我369個(gè)小時(shí)。 這個(gè)時(shí)間表包括花大量的時(shí)間來提高我的項(xiàng)目的投資組合質(zhì)量，而不是花最少的時(shí)間來滿足通過/失敗的標(biāo)準(zhǔn)。
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.
該程序在2017年秋季更新中得到了壓縮。 新的預(yù)計(jì)時(shí)間表是260小時(shí) 。 每個(gè)學(xué)期的課程安排為在13周內(nèi)每周10個(gè)小時(shí)，盡管學(xué)生有19周的時(shí)間完成每個(gè)學(xué)期。
課程內(nèi)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(統(tǒng)計(jì))，P2(數(shù)據(jù)分析入門)，P4(探索性數(shù)據(jù)分析)，P5(機(jī)器學(xué)習(xí))和P7(A / B測(cè)試)的課程內(nèi)容獲得5星我五個(gè)。 P3(數(shù)據(jù)整理)和P6獲得三顆半星。
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.
與Facebook員工(P4)進(jìn)行的探索性數(shù)據(jù)分析內(nèi)容非常具有啟發(fā)性。 Sebastian Thrun和Katie Malone(P5)開設(shè)的機(jī)器學(xué)習(xí)課程入門是我在任何在線課程中獲得的最大樂趣。 Google員工(P7)的A / B測(cè)試內(nèi)容是如此獨(dú)特。 如果可以的話，我會(huì)給這三個(gè)課程六個(gè)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和數(shù)據(jù)整理內(nèi)容(P3)并不令人驚訝。 與數(shù)據(jù)可視化內(nèi)容(P6)相同，但這可能是因?yàn)镈3.js很難向JavaScript新手教。 根據(jù)Class Central對(duì)這些課程的評(píng)論，這些意見并不少見。 在這里和這里檢查一下 。
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.
舊程序中的此“不驚人”內(nèi)容已在2017年秋季更新中刪除 。 現(xiàn)在包括用于數(shù)據(jù)分析，SQL，統(tǒng)計(jì)信息，數(shù)據(jù)整理和數(shù)據(jù)可視化的新內(nèi)容。 實(shí)用統(tǒng)計(jì)學(xué)的內(nèi)容側(cè)重于推論統(tǒng)計(jì)學(xué)，描述性統(tǒng)計(jì)學(xué)是前提條件，并在Data Foundations Nanodegree程序中進(jìn)行了講授。 現(xiàn)在使用Tableau而不是D3.js講授數(shù)據(jù)可視化課程。
項(xiàng)目進(jìn)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，項(xiàng)目是Udacity與其他在線教育平臺(tái)區(qū)分開來的地方。 他們?cè)陧?xiàng)目審查過程中進(jìn)行了投資，并且得到了回報(bào)。 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.
所有項(xiàng)目都鞏固了您在視頻中學(xué)到的內(nèi)容。 項(xiàng)目審閱者知道他們的東西。 他們會(huì)告訴您成功的地方以及錯(cuò)誤和/或遺漏的地方。 有監(jiān)督地邊做邊學(xué)。 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.
當(dāng)您遇到困難時(shí)，論壇和論壇指導(dǎo)者特別有用。 搜索論壇以查看您的問題是否很常見(通常是)。 沒運(yùn)氣？ 自己發(fā)布一個(gè)新問題。 有一位論壇指導(dǎo)者M(jìn)yles Callan，他似乎了解所有事情，并在數(shù)小時(shí)內(nèi)做出回應(yīng)。 我懷疑他睡著了。
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.
盡管論壇仍然存在并且可以正常工作，但是現(xiàn)在推薦使用Slack和課堂指導(dǎo)者作為支持途徑。 學(xué)生可以發(fā)布問題，并在相同或更高級(jí)別的即時(shí)性下(在幾個(gè)小時(shí)內(nèi)，通常更快)提供答案。 Slack社區(qū)由Udacity講師及其學(xué)生體驗(yàn)人員監(jiān)督，他們確保及時(shí)解決學(xué)生的問題，評(píng)論等。 著名的邁爾斯·卡倫(Myles Callan)現(xiàn)在是一名導(dǎo)師。
If you’re curious to see what these projects look like, check out this Github repository.
如果您想看看這些項(xiàng)目的樣子，請(qǐng)查看此Github存儲(chǔ)庫 。
有多難？ (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.
統(tǒng)計(jì)數(shù)據(jù)內(nèi)容對(duì)我來說很容易，因?yàn)槲以诒究粕线^幾門統(tǒng)計(jì)學(xué)課程。 如果您已有納米學(xué)位課程的經(jīng)驗(yàn)，那么這對(duì)于每一個(gè)主題都是正確的。
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.
我會(huì)將大多數(shù)程序歸為中等難度。 沒有很多測(cè)驗(yàn)的演講內(nèi)容(盡管經(jīng)常有)，可以輕而易舉，這不一定是一件壞事。 這些項(xiàng)目可以鍛煉您的大腦。 如果您想徹底了解，每個(gè)過程可能會(huì)花費(fèi)您20多個(gè)小時(shí)。
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.
探索性數(shù)據(jù)分析項(xiàng)目是最具挑戰(zhàn)性的。 我花了3.5份意見書。 查看此Twitter線程以了解更多詳細(xì)信息。
您可以在畢業(yè)后立即申請(qǐng)工作嗎？ (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。 如果您認(rèn)真對(duì)待的話，該程序應(yīng)該為您提供入門級(jí)數(shù)據(jù)分析師角色所需的技能。 Eli Kastelein就是一個(gè)很好的例子。 您可以在下面閱讀有關(guān)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
如何在沒有CS學(xué)士學(xué)位的情況下建立技術(shù)職業(yè) 2014年Spring，我是乘坐灰狗公車上大學(xué)的新人，頭也不回。 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.
您還可以繼續(xù)學(xué)習(xí)更高級(jí)的課程，包括該課程涵蓋的主題和其他主題。 這就是我選擇要做的。
最后的想法 (Final thoughts)
我是否會(huì)再次知道我現(xiàn)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.
在計(jì)劃結(jié)束的某個(gè)地方，我開始創(chuàng)建Class Central的《 數(shù)據(jù)科學(xué)職業(yè)指南》 。 這需要研究為數(shù)據(jù)科學(xué)中的每個(gè)學(xué)科提供的每一個(gè)在線課程。
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.
盡管我喜歡Nanodegree計(jì)劃中的大多數(shù)課程(更新：新課程取代了我不喜歡的課程) ，但有些其他提供商的課程則對(duì)某些學(xué)科給予了更好的評(píng)價(jià)。 例如， 統(tǒng)計(jì)信息 。 如果我一開始就可以訪問我的指南，那么我將考慮針對(duì)每個(gè)主題的單獨(dú)課程路線。 但是，Udacity的學(xué)生服務(wù)和項(xiàng)目審核過程對(duì)于學(xué)習(xí)是如此有效，以至于無論如何我都會(huì)參加Data Analyst納米學(xué)位課程。
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.
如果您是那種希望獲得100％自定義在線教育經(jīng)驗(yàn)，但又想利用Udacity的項(xiàng)目和服務(wù)的人，請(qǐng)針對(duì)每個(gè)主題研究自己喜歡的課程(我建議使用Class Central )，然后注冊(cè)Nanodegree計(jì)劃以完成這些項(xiàng)目是要考慮的。
替代品 (The alternatives)
These are the five alternative programs that I was considering when I enrolled in the Data Analyst Nanodegree program:
這些是我注冊(cè)Data Analyst Nanodegree程序時(shí)正在考慮的五個(gè)替代程序：
Johns Hopkins University’s Data Science Specialization on Coursera
約翰霍普金斯大學(xué)Coursera的數(shù)據(jù)科學(xué)專業(yè)
Microsoft’s Professional Program Certificate in Data Science on edX
edX上的Microsoft 數(shù)據(jù)科學(xué)專業(yè)計(jì)劃證書
Wesleyan University’s Data Analysis and Interpretation Specialization on Coursera
衛(wèi)斯理大學(xué)在Coursera上的數(shù)據(jù)分析和解釋專業(yè)
DataCamp’s Python and R tracks
DataCamp的Python和R軌道
Dataquest’s Data Analyst and Data Scientist paths
Dataquest的數(shù)據(jù)分析師和數(shù)據(jù)科學(xué)家路徑
Note: I have removed my comments on these programs due to Udacity policy regarding commenting on other providers.
注意：由于Udacity關(guān)于對(duì)其他提供商進(jìn)行評(píng)論的政策，我已刪除了對(duì)這些程序的評(píng)論。
結(jié)論 (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?yàn)槟峁氖聰?shù)據(jù)科學(xué)職業(yè)所需的基礎(chǔ)技能。 畢業(yè)后，您將能夠針對(duì)自己的長(zhǎng)處和短處，并在必要時(shí)補(bǔ)充學(xué)習(xí)內(nèi)容。 另外，您將離開一些準(zhǔn)備就緒的項(xiàng)目。
I loved it, as did others.
我喜歡它， 其他人也喜歡。
★★★★?
★★★★?
翻譯自: https://www.freecodecamp.org/news/review-udacity-data-analyst-nanodegree-1e16ae2b6d12/
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