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Getting and Cleaning Data(으)로 돌아가기

존스홉킨스대학교의 Getting and Cleaning Data 학습자 리뷰 및 피드백

6,447개의 평가
1,000개의 리뷰

강좌 소개

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....

최상위 리뷰


Oct 26, 2016

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.


Feb 02, 2016

Easy, mostly instructive Course. The Assignments and quizzes are quite good, and illustrates the lessons very well.\n\nSee the videos for general presentation, but use the energy on the excersizes.

필터링 기준:

Getting and Cleaning Data의 960개 리뷰 중 251~275

교육 기관: Tacao M

Feb 03, 2016

Very interesting material and basic knowledge on R to read files and produce tidy data

교육 기관: Murat Z

Feb 11, 2018

Great course for data mining and cleaning. If you planning to take Reproducible Research course, I'd recommend to at least audit that course's second week for markdown and knitr skills prior to taking Getting and Cleaning Data course, coz you're going to face need for those skills during the course project.

교육 기관: Juan P S

Jan 12, 2016

Best instructors, best learning . . .

교육 기관: 陆俞凯

Mar 22, 2016

very helpful

교육 기관: Harshdeep K

Aug 29, 2016

Learning how to create a tidy Data set was one of the perks in this course

교육 기관: Light0617

Aug 07, 2016

It taught a lot of tools and introductions

교육 기관: Charles K

Feb 06, 2016

This is a very well put together course. It teaches the basics of data cleansing and how to setup data for modeling--by far the most foundational technical aspect of data analysis.

교육 기관: Gaalle A A P M P

Jan 23, 2018

Thank you very much for letting me do this course. Learned alot

교육 기관: Diego T B

Nov 07, 2017

Very interesting. Global view to process Data and try to explain with very useful techniques the processing or cleaning data with regular expressions. I enjoyed it!

교육 기관: Anna B R

Sep 27, 2017

Fantastic course!

교육 기관: Dominic C

Aug 01, 2016

Using R with training through your course seemed almost too easy, your book also greatly helped, thank you for such a well designed course which is so practically based and geared towards commercial programmers like myself.

교육 기관: Marcus V M d S

May 29, 2017

Learned a lot, manipulating real data sets to produce "tidy" data sets, suitable for analysis. A great course!

교육 기관: Ben H

May 10, 2018

Not the most exciting course but has been so, so useful.

교육 기관: Sudhir B

Jul 08, 2016

The examples used were good.

교육 기관: Diana S

Feb 11, 2016

Thank you son much!!!!

I really like the course.

It help me in my job =)

교육 기관: Juan C L T

Oct 13, 2017

great course, very useful and insightful; challenging final project.

교육 기관: Catharine

Mar 17, 2017

Very helpful and practical course to learn about data sourcing and cleaning.

교육 기관: Kristin K

Aug 04, 2017

This course solidified any gaps that were left from the R Programming Course and opens the world of data science to everyone in a very practical way. I really enjoyed the presentation of the material and am very happy I took the class.

교육 기관: Abhishek S

Jan 25, 2018

nice learning course

교육 기관: Stefan B

Apr 10, 2016


교육 기관: Tanuj S

Sep 28, 2017


교육 기관: Amy B

Jun 05, 2017

Excellent class, lectures and assignments matched up well.

교육 기관: Alberto T

Apr 05, 2016

Love the new strategies that learn in the course.

교육 기관: Fabio R C

Jul 03, 2017

Muito bom!

교육 기관: Eugene K

Jan 08, 2018

Great course on tidy data. Very useful in understanding how to use different types of data (csv, XML, API) and how to manipulate the data so that you can perform analyses on it.