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Kursteilnehmer-Bewertung und -Feedback für Getting and Cleaning Data von Johns Hopkins University

7,191 Bewertungen
1,145 Bewertungen

Über den Kurs

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....



May 03, 2020

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.


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.

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1051 - 1075 von 1,108 Bewertungen für Getting and Cleaning Data

von nguyen l m

Jun 23, 2020

the instructor of this course, unlike the other 1, is quite unclear about what needed to be done. a lots of the packages of the course are not up to date.

more quiz and exercise would be highly beneficial

von Jason Y

Aug 02, 2017

Mediocre presentation of tidy data, which is probably the most critical topic. Otherwise, its mostly just walking through what commands to use in R to load in various file formats.

von Patti M

Jan 04, 2017

This class needs more content, more explanation. It is clearly a very important aspect of Data Science, but the assignments were more complex than the given course content.

von Sheila B

May 07, 2018

I learned a lot but my usually happy & grateful attitude was sorely challenged by the fact that so many facts in the videos and obvious course material was, well, wrong.

von James K

May 12, 2020

Out of date material. Many links broken. Some of the functions taught are sunset. Week 2 was too surface level to do anything useful. Weeks 3 and 4 better than week 2.

von Joseph S

May 14, 2020

This course has a very interesting subject and a concise syllabus, but is very poorly prepared. I hope coursera will pass on the message to Johns Hopkins University!

von Albert B

Aug 14, 2016

Too difficult practical exercises with the theorical background given. I know that hackers skill should be used but it is too much assumption in the projects!!

von Seyed A T

Jul 19, 2016

It is somehow just an extension on R Programming course, with many unnecessary details that will be forgotten in a few days after the course.

von Sergio C d F

Aug 23, 2016

The video is simple and good.

But the final project and some test are too hard based on material presented.

Also staff's support are not good.

von Gianluca M

Sep 19, 2016

The only interesting part was dplyr. The rest was too confusing, with lots of lists and no explanations.

von Adam M

Jan 18, 2020

The information in the lectures is very stale, which makes it extremely frustrating to learn from.

von Sudarshan P

Dec 05, 2017

The course material needs update. There are code snippets that do not work.

von Aditya D

Sep 18, 2017

This course could have been better. It was all textual and it got boring.

von James C

May 30, 2017

Final assignment is not well detailed, and may cause confusion.

von Guy P

Mar 03, 2016

This course lacks projects to implement the skills we learn.

von Lee D

May 19, 2016

The course was a bit mixed in terms of its quality.

von Adam K

Aug 26, 2019

Very poor instructions for assignments.

von Rafee S

Feb 25, 2019

waste of time for software engineers

von Maximilian P

Jul 11, 2018

Too many things in one place

von Sergio B S

Nov 17, 2017

Worst class in this series.

von Michal K

Apr 29, 2016

too superficial

von Leandro J G D

May 12, 2020

Lacking focus.

von Warren

Aug 05, 2016


von Walson Q

Nov 29, 2018


von Neil J

Jul 23, 2016

R is really just the worst, and the instructors do not make it better. The code in this class is unreadable:

- too many one liners, because "it's faster to write", though harder for other people to read

- variables are named cryptic things like spIns or x, rather than names with meaning (eg,, again "because it's faster to type"

- way too many cases of "there is more than one way to do it", which just makes things confusing because the other ways tend not to be equivalent

What I'm most concerned about is that I've seen lots of poorly written code in many different languages: Java, C++, C, Python, Perl, and now R. But I've also seen really well-written code in all the languages *but* R, I have yet to see any code in R that is flexible, maintainable, and clear. Which leads me to think that no such code exists, or it's so rare that it doesn't matter. It is clear to me that if I am to do data analysis, then I will need a different set of tools; but because this specialization is taught entirely around R (the lectures are about R, not about higher-level concepts), then this specialization is not useful to me.