Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
约翰霍普金斯大学課程信息
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學生職業成果
37%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
13%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
完成時間大約為20 小時
英語（English）
字幕：法語（French）, 中文（簡體）, 越南語, 韓語, 俄語（Russian）, 英語（English）
您將學到的內容有
Understand common data storage systems
Apply data cleaning basics to make data "tidy"
Use R for text and date manipulation
Obtain usable data from the web, APIs, and databases
您將獲得的技能
Data ManipulationRegular Expression (REGEX)R ProgrammingData Cleansing
學生職業成果
37%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
13%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
完成時間大約為20 小時
英語（English）
字幕：法語（French）, 中文（簡體）, 越南語, 韓語, 俄語（Russian）, 英語（English）
提供方
约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 2 小時
Week 1
In this first week of the course, we look at finding data and reading different file types.
完成時間為 2 小時
9 個視頻 （總計 67 分鐘）, 4 個閱讀材料, 1 個測驗
9 個視頻
Raw and Processed Data7分鐘
Components of Tidy Data9分鐘
Downloading Files7分鐘
Reading Local Files4分鐘
Reading Excel Files3分鐘
Reading XML12分鐘
Reading JSON5分鐘
The data.table Package11分鐘
4 個閱讀材料
Welcome to Week 110分鐘
Syllabus10分鐘
Pre-Course Survey10分鐘
Practical R Exercises in swirl Part 110分鐘
1 個練習
Week 1 Quiz30分鐘
完成時間為 1 小時
Week 2
Welcome to Week 2 of Getting and Cleaning Data! The primary goal is to introduce you to the most common data storage systems and the appropriate tools to extract data from web or from databases like MySQL.
完成時間為 1 小時
5 個視頻 （總計 41 分鐘）
5 個視頻
Reading from HDF56分鐘
Reading from The Web6分鐘
Reading From APIs7分鐘
Reading From Other Sources4分鐘
1 個練習
Week 2 Quiz30分鐘
完成時間為 11 小時
Week 3
Welcome to Week 3 of Getting and Cleaning Data! This week the lectures will focus on organizing, merging and managing the data you have collected using the lectures from Weeks 1 and 2.
完成時間為 11 小時
7 個視頻 （總計 60 分鐘）, 1 個閱讀材料, 4 個測驗
7 個視頻
Summarizing Data11分鐘
Creating New Variables10分鐘
Reshaping Data9分鐘
Managing Data Frames with dplyr - Introduction3分鐘
Managing Data Frames with dplyr - Basic Tools12分鐘
Merging Data6分鐘
1 個閱讀材料
Practical R Exercises in swirl Part 210分鐘
1 個練習
Week 3 Quiz30分鐘
完成時間為 5 小時
Week 4
Welcome to Week 4 of Getting and Cleaning Data! This week we finish up with lectures on text and date manipulation in R. In this final week we will also focus on peer grading of Course Projects.
完成時間為 5 小時
5 個視頻 （總計 34 分鐘）, 2 個閱讀材料, 3 個測驗
5 個視頻
Regular Expressions I5分鐘
Regular Expressions II8分鐘
Working with Dates6分鐘
Data Resources3分鐘
2 個閱讀材料
Practical R Exercises in swirl Part 410分鐘
Post-Course Survey10分鐘
1 個練習
Week 4 Quiz30分鐘
審閱
來自获取和整理数据的熱門評論
由 HS 提供2020年5月2日
This course provides an introduction of some important concepts and tools on a very important aspect of data science: cleaning and organizing data before any analysis. A must for any data scientist.
由 CW 提供2020年8月6日
I think that the level of difficulty of the exercises and final assignment does not match with the depth of the lectures; without a textbook, I feel lost, don't have a reference, and have to guess.
由 DH 提供2016年2月1日
Easy, mostly instructive Course. The Assignments and quizzes are quite good, and illustrates the lessons very well. See the videos for general presentation, but use the energy on the excersizes.
由 BE 提供2016年10月25日
This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.
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