존스홉킨스대학교의 Getting and Cleaning Data 학습자 리뷰 및 피드백

Excellent course. It gets through the "dirty job" of obtaining data from diverse sources (including API, web, and others), cleaning it, and transforming it into a "tidy" dataset. Highly recommended, along with the R programming course (which you should take first).

Good overview of what it means to get and clean your own data. Really enjoyed the final project as it challenged you to, with minimal guidance, think through what a tidy dataset really means, and figure out how to make that happen with the dataset you are provided.

For someone with no programming background and limited experience working with data, this was a challenging, sometimes frustrating, course. But perseverance through the struggle can end in a deep sense of satisfaction. Happily, this is how it was - quite rewarding.

Wonderful course. gets you through the basics and beyond in getting and cleaning data from diverse sources. Very well thought and explained. There is a lot to be learnt from this course, and it requires devoting a good amount of time to let the material sink in.

Good course, but needs an update. Week 2 was really difficult compared to what was explained in the lectures and regex expressions should be explained using R, it was a little hard to learn to use them directly in R. I feel that I learned a lot in this course.

Excellent course! i learned a lot with the packages mentioned dplyr, tidyr, readr, lubridate. the swirl package is perfect to learn by doing and the assignment is very challenging and it is good because it incentivates you to research deeply and learn more.

Very enlightening course. It is the first course where I felt like I was actually doing something data sciency. Would recommend even as a stand alone course because I have now come to appreciate the importance of tidy data in performing successful analyses.

Really can learn practical skills! I like that each sub course of data science specialisation just focus on a certain areas and takes only 4 weeks, this way I won't be overburden between work and learning, and also easier for me to absorb the new skills.

Another brilliant course from Johns Hopkins University in the data science specialisation. Data preparation is a step where an analyst may spend considerable time before beginning any analysis task. I found this course useful and practical. It provided

Excellent! From my point of view, this is the best course so far. The general concepts that are thought here can be applied to any programming language you use for data analysis. The specific R concepts really shows the power R has to manipulate data.

This was one of the best class. Recommend more side reading material on data. SWIRL has a reading link but the link is not provided anywhere else on the board. Also, it would be beneficial if the links can be made clickable in lecture slides. Thanks.

Exellent course, which brings you to the next level of a Data Scientist.

Getting and Cleaning data principles can be used in alot of situations. I found the build up of this and the assignment at the end to be very well tought trough and important.

Very good course and lectures. However, it would be good to have a book covering all of the material in this course. That would make work on final project much easier. In my opinion, it is impossible to finish final project in just 2 hours.

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.

This was so hard to me, because I didn't know anything about 'Making tidy dataset'. So, when I took a course project, I was struggling to find 'what should I do'. Comprehending raw data is so hard then you think, newbies! Be careful!

Covers a wide range of topics without loosing transparency. In my opinion requires more work than the other courses, but is really worth a go. You end up having a firm basis for working with data and learning more about the process.

Very nice course.

helped to understand how to find sources of data (I found that extremely important), and strengthened my R skills.

It would be nice though to have the links which were shown in the slides available for the students.

This is a very complete course. It covers the basics of what you have to know to adquire data from different sources and filter that data to be used in further steps of data analysis. It offered great notions on Data Mining also.

I learned a lot. The videos were clear and helpful. The assignments were just the right level, not too easy and not hard but still challenging.

The swirl package for interactive practice/learning is also very helpful. I Love it!

I am happy now with the single file HTML Documentation for the whole course, generated from md-Files in the cloned repo

https://github.com/DataScienceSpecialization/courses/

It is much handier than the standard downloadable PDFs.

Great introduction to getting and cleaning data. Good exercises to practise the tools and concepts learned. The lectures were very focussed and informative. I liked the accompanying interactive tool swirl very much. Thank you!

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.

This course is very interesting and not as difficult as it seems. I learned many new stuff about data analysis in R, as well as how to work in swirl, something I have never encountered before. Otherwise, awesome course! :)

Great content, challenging assignments and quality videos. Loved the coursework and grateful to have learned from such highly experienced professors. Thanks Coursera and Johns Hopkins University for making this happen!

Good course design, challenging material. I love the fact that the course doesn't spoon feed everything, we are encouraged to learn more on our own. This course gives you almost everything required to handle data in R.