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Bewertung und Feedback des Lernenden für Getting and Cleaning Data von Johns Hopkins University

4.5
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7,881 Bewertungen
1,293 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....

Top-Bewertungen

HS
2. Mai 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.

DH
1. Feb. 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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1201 - 1225 von 1,257 Bewertungen für Getting and Cleaning Data

von Albert B

14. Aug. 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

19. Juli 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

23. Aug. 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 Cintia K

9. März 2021

Unfortunately the course's lectures are quite outdated, so you won't pass week 1 without all the research done by yourself.

von Ruwaa I

18. Aug. 2020

I learned "ask Google" and dplyr, nothing more. Not as satisfied as with the other courses in the specialization.

von Gianluca M

19. Sep. 2016

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

von Adam M

17. Jan. 2020

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

von DESIREE P

10. März 2021

Messier than the 2 previous courses. Lacks explanations for codebook in the peer-graded exam.

von Sudarshan P

5. Dez. 2017

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

von Aditya D

18. Sep. 2017

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

von James C

29. Mai 2017

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

von Guy P

3. März 2016

This course lacks projects to implement the skills we learn.

von Lee D

18. Mai 2016

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

von Colin H

21. Okt. 2020

Guidance for assessments could be a lot better

von Adam K

25. Aug. 2019

Very poor instructions for assignments.

von Rafee S

25. Feb. 2019

waste of time for software engineers

von Maximilian P

11. Juli 2018

Too many things in one place

von Sergio B S

17. Nov. 2017

Worst class in this series.

von Michal K

29. Apr. 2016

too superficial

von Leandro J G D

12. Mai 2020

Lacking focus.

von Warren

5. Aug. 2016

Boring.

von Walson Q

29. Nov. 2018

2

von Dan H

16. Jan. 2018

This course is about getting data from the web and processing it using a computer language and packages in that language that are under active development. There is a github repo with course content and other electronic resources that are made to be easy to update. It has never been updated, even once since the course first went live 4 years ago. There are many broken links, several new features and bugs in packages that make lecture content obsolete or broken, errors found by students, etc. None of these issues have been addressed, even once, in any of the material, including the extremely easy to update content on github. This is disappointing and not very professional. Additionally, many of the notes are not particularly good to begin with. Much of it is essentially cribbed from other online tutorials, examples in the documentation, and in a few cases, someone else's (also broken) lectures. Take this course if you want a study group (the forums are actually quite useful) to help you go through 4 year old lectures rehashing online tutorials from 4 years ago about a topic that changes pretty quickly.

von Grant I

22. Jan. 2018

Made it all the way to week four and decided to drop this entire specialization. The data set in the final project is poorly referenced (despite the code book provided). The data set comes in 24 text files you have to merge (which isn't a problem in R) but what is a problem is when you don't understand what the variables and observations are. Perhaps if I worked in the medical field these measurements would mean more, but to a business major, they are incomprehensible with the limited documentation provided. So my assumption was, if I am having difficulty understanding what the final data structure should look like, others will be having the same problem......and its peer reviewed. How can I possible grade someone else

von Abdulaziz M A A

2. Juli 2020

I have to date completed the first 2 courses in Data Science: Foundations using R Specialization.

Today I have cancelled my subscription for the following reasons:

1 Poor course design and delivery

Lesson contents inadequately covered and sourced, lecturers deliver a fast paced recordings with very little examples and references making it hard for beginner students to keep pace and find themselves unprepared for the required quizzes and exams.

2 Course materials needs to be updated and presented to facilitate learning , eg. often times students are referred to static links and too many many times new and un-familiar concepts/ functions are rushed thru with no introduction or explanation.