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

4.6
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7,861 Bewertungen
1,288 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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1051 - 1075 von 1,251 Bewertungen für Getting and Cleaning Data

von Michael M J

11. Nov. 2020

The material that's ultimately learned from this course is very powerful and provides a major milestone in one's data science journey. However, I had to mostly teach myself. The instructor's video lectures only teach at the most basic level, and the quizzes are much more advanced - thus creating a very large gap which I needed to fill by lots of self learning and research. The course project is a great opportunity to put the course's objectives into practice, but it wasn't explained clearly - it took me many hours to understand what needed to be done. All in all, I am satisfied with the course's material, but not the way it's communicated to the students. Three stars.

von Joshua S

10. Juli 2020

Lecture content is very drab and filled with "...and here's another thing you can do...". I think it would be a lot more effective with more problems and various solutions. There should be a project every week along with the quizzes.

I also found the peer review process for the course project to be sub-par. I personally put a LOT of work into the course project, and put together what was a really thoughtful and well-written README and CODEBOOK just to have my project downgraded because someone wasn't sure the run_analysis did what it was supposed to. It's not a big deal but I think if an instructor saw it they would've found it very thoughtful, thorough and accurate.

von Jaymes P

23. Okt. 2020

This course was largely helpful and the instructor was clear in the lectures. However, much like many of the courses in this specialization, the quizzes and the final project were misaligned with what was taught in the lectures. For example, we sit through a whole week of lectures on the details of reading in many different data types, but never talk about fixed-width format once, it's never even mentioned. And that is what is on the quiz. The final project was challenging and fun to figure out once I was on the right track, but the directions were not sufficiently clear and I wasted a lot of time needlessly simply trying to figure out what the instructor wanted.

von Anton K

8. Aug. 2019

This is a very brief course, many of the topics deserve a much more thorough explanations. This part of the data analysis (i.e. data cleaning and acquisition) is in fact a complex subject and subjects are not covered in this course. There were also technical issues. For instance, the audio quality of lectures by prof. Jeff Leek is very poor. And the other major problem that I had with this course is the ambiguity of the requirements, although it wasn't difficult to finish. But if you are planning on taking this course, be ready to spend considerable amount of time on understanding the structure of the final submission's materials.

von Harris W

27. Mai 2020

Like all courses in this specialization, there is an incredible lack of practice and application for a large amount of the skills taught in lecture. I would say that only about 60 percent, or maybe less, of the content in the lecture is assessed in the quizzes and assignments. Additionally, the peer review process is wildly flawed for the final project. I did not receive any constructive criticism from anyone, and I doubt they even truly looked at my code to make sure it worked. I don't blame them, they have little incentive. Rather I place blame on the system of grading and lack of feedback.

von Rashaad J

24. Juli 2017

I have 2 key concerns with this course. First, I don't feel like the material presented adequately prepares you for the quizzes. For at least 2 of the 4 quizzes, I had to spend a substantial amount of time locating and reviewing other resources to answer the questions. My second concern is that for the final course assignment, there is a lack of specificity with the instructions. Not being able to answer a question is vastly different from not understanding what the question is asking and I found myself spending more time doing the latter (which is wasteful) and less time with the former.

von Constantin S

20. Feb. 2016

In some weeks only about an hour of input where several topics have already been covered in R Programming. That's very little value for money.

The final course project again feels like it's done in a rush and without another review: The submitted dataset should be automatically checked. It's simply impossible to derive from it whether the student did everything right, but it could be easily done programmatically. Some of the questions have wording and grammar issues that make it hard to understand. Also there is slightly contradicting instructions between the task and review description.

von Angela W

14. Juli 2017

I did learn a lot, but I thought the first half of the course (getting the data) was very challenging.

What does annoy me though is that links aren't clickable, sometimes they're wrong, there are typos on the slides etc. The response to these complaints in the forums is that these lectures were recorded a while ago and it takes time to change things and so on - but for $50 a month, I don't think it's too much to ask that the course materials be kept up to date!

So honestly, I feel like I'm being ripped off a bit here.

I did really enjoy the course project though.

von Carla P

25. Okt. 2021

In my opinion, the lessons are just a basic overview of some concept and do not gives you the competences you need to pass the Quiz and the Peer Graded Assignment. Therefore, for most of the questions of the assignments, you need to look for the tools you need somewhere else in the web! On one side, without the lessons, yhou would probably not know what to look for on google, however the lessons are not enough to achieve a good grade in the assignments! Also the peer graded assignment takes to long to receive the evaluation!

von Luis P

25. Jan. 2018

The most challenging so far of the 9 courses on the Data Scientist track. Would like to see some errors removed from slides. Some parts of the lectures seemed rushed. Would like to see some of the non-self-evident usage of some functions to be described a little better in more detail. I found myself having to look at multiple online areas to really understand some of the functions that were glossed over. Otherwise, this was a very helpful course that should be taught to all disciplines involving any amount or type of data.

von Raymond B

27. Sep. 2020

The "Reading from..." lessons from week 1 and week2 were extremely frustrating, since we did not get much info on where we would see them most often or the benefit of using one over the others. Instead, we simply sat for hours listening to lectures moving from one type of document to the next before being handed the quiz. The dplyr and data manipulation lectures were great and I really anticipate using them frequently in the future. I think regular expressions deserved more lecture time/ practice.

von Chanchal D

8. Juli 2020

The Course Design is good however what i give three stars is for the following reason

The Sound Quality is straight up very poor . i have to put my speakers to full volume to atleast make it clear and audible , which leads to other pc programs to cause loud noise with the same sound volume

Many Topics in the course like Factors etc were not clear in the tutorial videos and i had to most probably go out of my way to find the meaning and uses

Rest The Course Is Top Quality . Thank You For the course

von Paul R

11. März 2019

This is really R part 2, getting into file/API handling, data frames, regular expressions etc. The specialization focuses on data frames though little coverage of data tables needed for the capstone. Some of the ordering of the materials was confusing e.g. this course revisits date/time handling which was started in the previous course. Assignments are interesting and Swirl exercises are useful. All in all, the combination of these R courses gets you up to speed.

von Lawrence G D

29. Nov. 2020

Very challenging but rewarding. The first two weeks of material were a bit condensed I think, hard to follow how to import some obscure data types into R and too complex to be covered in a 5 minute video. Could have been spread out more or omit some that are not probably practically useful. The quizzes and the final project were difficult to navigate using only the material provided in the lectures, and had to rely a lot on Googling stuff.

von Kai P

8. Aug. 2018

The quality of this course is much better than the earlier two. Although this course still has the problem of feeling like a disjointed series of topics on singular functions, there is much more of a cohesive overall theme and structure so it feels a bit more like you're building towards an overarching goal. The final project directly relates to the lectures and felt like a solid way to connect most of the ideas to a project on real data.

von Daniel H

18. Mai 2020

I suggest changing the quizzes and assignment questions more often because they're all over the internet for this course and rest of the courses in the specialization. I understand that students who are cheating are mostly hurting themselves, but it also affects the value and credibility of the certificates you're giving out.

In terms of course modeling and content, it's very nice. I really enjoyed. The swirl package is genius. Thanks.

von Mary S

20. Apr. 2016

There were a lot of good nuggets in here, but overall this course felt somewhat disjointed compared to the others. It would be nice to have more practice with some of the different formats (e.g., JSON) and for exercises to loop back to some of the early content. I did like that the final exercise required a fair amount of investigation into understanding the documentation and relationship between the files before undertaking to code.

von Mark B

7. Apr. 2020

The data downloads for two quizes appear to have been updated, meaning that there is no way to come up with the right answer. The course project could use some minor clarifications. I was very difficult to determine what was wanted, and this lead to my having to re-submit twice. The deliverables seemed to be confusing to both me and the graders. Course was difficult because of this kind of confusion, not because of the material.

von Jake T T

30. Mai 2017

Difficult course, I had to complete it over two sessions. I came into the Data Science track with no knowledge of computer language, which has made learning R particularly difficult; however, after the previous classes I am finally able to search for the information I need to complete the assignment. The other reviewers are correct that the final assignment is a doozy - it took me several hours to complete.

von Christoph J

9. Aug. 2017

I would have given the course 4 stars if it wasn't for the last assignment which relies on other students to review your coursework. I understand that it is difficult to find another way of grading the assignments but the results of the process here are just too subjective and people influence your grade based on their subjective view on things, which I think is just wrong. Otherwise the course was good

von Jo S

27. Jan. 2016

The content in this course is essential, but the delivery is patchy and the course project is hard to complete with just the learning materials provided. Read around the course and visit the data science specialisation wiki for extra information, and work through it at your own pace, rather than that suggested by the course. It's much easier to do this now it's on the new Coursera platform :o)

von Tim j

31. Dez. 2016

decent enough but this is a heavy subject and really it is not that interesting although clearly necessary. I feel maybe it could have been organized better to make it more interesting Also reading some of Haldey wickhams book he deliberately keeps this part of Data Science away from new learners as it can be a bit dreary, so my recommendation would be to do some of the other courses first.

von L M

8. Dez. 2020

Slides are images and cannot copy text or code, same with some of the quiz Qs - cannot copy the code.

Many issues with people not getting expected results with some quiz questions, different systems give different results.

Should be teaching tibble library, not data.table (tibble data frames can be used to pass/receive via pipes)

Audio quality is terrible - needs better recording equipment.

von Bill J

7. Jan. 2020

In weeks two and three, the course presents a list of data format and how to read them into R. I would have preferred a better description on why tidy data sets are considered tidy that included some side-by-side comparisons and downstream effects of untidy data. This would help me evaluate the effort and risk of introducing errors from tidying the data against the benefit of tidying it.

von Daniel P

24. Okt. 2019

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good. I like the videos and the assignment. There is cerain redundancy of information. Much of the "new" information was already elaborated in the previous courses of the same specialization. Additionally, the grading system is based on other students whose knolledge may be not beyond the course scope and submitting an inovative solution can mean not passing the course.