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.
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.
创建者 Daniel M D V•
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.
创建者 Kunal P•
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.
创建者 Martin H•
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.
创建者 Oleksandr K•
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.
创建者 Kristin K•
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.
创建者 Kang I B•
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!
创建者 Jan K•
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.
创建者 Tomer E•
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.
创建者 Miguel C•
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.
创建者 Tim S•
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!
创建者 Dmytro D•
I am happy now with the single file HTML Documentation for the whole course, generated from md-Files in the cloned repo
It is much handier than the standard downloadable PDFs.
创建者 Dominic C•
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.
创建者 Орехов А И•
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! :)
创建者 Vinayak N•
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!
创建者 Abhiram R P•
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.
创建者 Angie M•
One of the most useful courses I've taken so far in Coursera from a beginners perspective. The course does need some updating but overall I was able to complete the assignments with the information provided.
创建者 Francisco M M•
Me pareció un excelente curso, muy didáctico y con mucha información adicional para poder estudiar por nuestra cuenta para lograr una mayor profundidad en algunos temas en especial. Lo recomendaría sin duda.
创建者 Nicholas A•
I really enjoyed this class. Cleaning data is not very difficult, but it is a very important aspect of Data Science. This class taught me the importance on making data easily readable on top of the process.
创建者 Herson P C d M•
Excepcional, estes cursos estão abrindo completamente minha mente para novos horizontes, novas possibilidades. Enfim, estou cada dia mais motivado e mais entusiasmado com tudo de novo que tenho aprendido!
创建者 RONAL O R G•
It´s a good course to learn how to sort and get a tidy data, the course project it´s a good challenge but it took time to get the 4 perviews, I think many people have problems with the Git Hub account.
创建者 Nima A•
A very useful course. The audio quality of some lectures (especially those by the main instructor) was not good. This course completes the sister course of R programming and they work together.
创建者 현 허•
I really really loved this course. Some of courses before were outdated because there are lots of changes in packages or others. However, materials in this course were not changed that much.
创建者 Vyasraj V•
A lot of insight and practical knowledge of cleaning data that is available in many places in the Internet. I loved this course and it took me 2 tries to pass the peer graded assignment. ;)
创建者 Anna M D C•
It was pretty hard for someone like me who has a weakness in programming but it provided sufficient exposure and tasks for me to learn within my capabilities. I did enjoy its challenges.
创建者 Edwin R V C•
Excellent course. It helps to complement the knowledge of data analysis. The project was quite interesting and illustrative, especially considering that they were real experimental data.