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Learner Reviews & Feedback for Getting and Cleaning Data by Johns Hopkins University

4.6
stars
7,815 ratings
1,277 reviews

About the Course

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 reviews

HS
May 2, 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.

BE
Oct 25, 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.

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1026 - 1050 of 1,239 Reviews for Getting and Cleaning Data

By Nithya M

Jun 23, 2017

Great!

By Shreya S

Mar 20, 2017

great

By Veena M

Feb 21, 2016

good.

By Chuang M

Feb 6, 2016

Great

By Marcos A

May 12, 2019

Good

By Nithin K G

Nov 16, 2018

Good

By Ashish S

Feb 6, 2017

good

By shipra g

Aug 8, 2016

Good

By Vivekananda R C

Feb 7, 2016

Good

By Mrunal 1

May 8, 2021

-

By Dimitri d

Feb 23, 2017

.

By Sergio R

Nov 27, 2016

4

By sugyoo

Oct 22, 2016

A

By Borja C

May 7, 2016

.

By Miguel C

Mar 25, 2020

I really enjoyed and learned a lot in this course.

I feel a lot more comfortable with looking for and reading data. I learned how to clean data and getting it ready for further analysis. I think the course project was particularly good for completely understanding the process of tidying data and all the aspects it involves, such as writing a code book and a README file for accompanying it.

Furthermore, I believe I further developed my R programming skills, by learning how to code new things or things I already knew but in a more efficient way, by using new packages and techniques.

Moreover, I found Professor Jeffrey Leek quite engaging, very easy to understand and I had complete confidence in his knowledge on this subject.

However, I believe the course is slightly outdated. I was often disheartened and frustrated by not being able to replicate what was being done in the lecture videos. For example, there were many links that did not work anymore and sometimes information that simply wasn't correct anymore. I found the discussion forums and many mentors responses to be very helpful. I think this can easily be fixed by writing up an errata or updating the lecture videos.

By Tomasz J

Dec 11, 2017

The course is teaches you some principles of tidy data and cleaning data but it's very messy.

There is no systematic approach to plyr and dplyr libraries. The teachers peak some functions from one and some functions from other library, but without any clear principle. It looks that prof. Leek and prof. Peng are presenting their favorite functions without consulting each other. They are doing it in the right way, though very confusing. On the other hand loading data is approached very encyclopedically.

Assignments not only check what was taught in the videos but also sometimes require new skills and going through stackoverflow etc. (e.g. codes to read fortran files). This is not the way how you construct good coursers.

Additionally some instruction in the final assignment are provided in submission part! (Expected names of the files should be provided in the assignment description, not on the submission page).

Prof. Peng and prof. Leek are very skilled, they know their job, but they don't know how to teach efficiently. Nonetheless if you are motivated to learn, this course may be very helpful.

By Guillermo A G

Oct 23, 2017

This course was quite challenging in comparison with the first two. I felt that the material provided by the instructors was not enough to approach the quizes and assignments, so it's necessary to spent a lot of time researching for your own in other sources. I struggled with the Course Project Assignment because I didn't understand what I was supposed to do exactly. Fortunately, the forum threads were really helpful. Nevertheless, the course's intention is very valuable and if you are patient and go all the way through it you will improve your data science skills, learn very useful techniques and habits, specially if you're a beginner. But I strongly suggest the instructors to make the course contents more explicit and helpful.

By John Y

Aug 4, 2017

Great class for an important piece of data analytics and data science. One issue I've been noticing with R compared to using Anaconda/Python is that a lot of the libraries required for the class aren't explicitly mentioned. That's fine if you're experienced with these environments and able to read error codes with familiarity. Minor annoyance to me when I run a script and realize I don't have a library installed.

I'd imagine though its extremely frustrating for beginners who may have written perfectly good code but haven't figured out that they simply need to install certain packages to answer quiz or homework questions. Perhaps having a full library or package list for Course 1 of this series will be helpful.

By Dan S

Oct 9, 2020

There was a lot to like about this course but there were several aspects that kept the course from being stellar. This course attempts to convey a lot of important information without connecting the dots. The videos did not align well with the Swirl exercises and the videos did not attempt to address foundational principles of the course topics. Rather, many topics were covered on a superficial level without in-depth examples that demonstrate application of the topics. I think the course needs to updated as some of the quiz questions and material seem to be out of date. The final project description had aspects that were vague and hard to interpret given the data set we had to work with.

By Michael M J

Nov 11, 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.

By Joshua S

Jul 10, 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.

By Jaymes P

Oct 23, 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.

By Anton K

Aug 8, 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.

By Harris W

May 27, 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.

By Rashaad J

Jul 24, 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.