2 de mai de 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.
1 de fev de 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.
por D. D•
15 de mar de 2016
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.
por Thomas F•
4 de mai de 2021
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!
por Dominic C•
1 de ago de 2016
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.
por Орехов А И•
12 de mar de 2020
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! :)
por Vinayak N•
26 de jul de 2019
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!
por Abhiram R P•
17 de mai de 2017
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.
por Eduardo R R S•
9 de abr de 2021
es muy buen curso recomendada al 100% la ruta solo que siento que deberían hacer una introducción, a purr ya que los bucles de este paquete de tidyverse son mas efectivos que algunas de las funciones que enzeñan.
por Angie M•
19 de jul de 2020
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.
por Francisco M M•
20 de out de 2017
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.
por Nicholas A•
3 de out de 2017
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.
por Herson P C d M•
6 de dez de 2016
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!
por RONAL O R G•
24 de set de 2020
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.
por Nima A•
8 de jun de 2020
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.
por 현 허•
3 de mar de 2018
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.
por Vyasraj V•
26 de nov de 2017
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. ;)
por Anna M D C•
2 de jan de 2019
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.
por Edwin R V C•
7 de mar de 2016
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.
por Vincent B•
5 de nov de 2017
Very good course! It is a topic which is very often underestimated and we all need to learn to get more productive on this, as most of the time is spend on it in the "real world".
por Gianmarco P•
3 de mai de 2020
Very well done. Clear example and balanced explanation. Big advantage if you spend more time looking at the suggested readings. I found usefullpeer- review thanks to other students.
por Balaji P•
4 de fev de 2018
The course is an excellent introduction to the dplyr package and string manipulation in r. I thought the assignment at the end of the course was a little vague and hard to understand
por B S•
14 de nov de 2017
Great course if you are working with R. I learned how to load data in R and various handy features (plyr, dplyr, lubridate packages) to clean data before starting the data analysis.
por BOUZENNOUNE Z E•
3 de mar de 2018
Amazing, you get to see almost every aspect of data science.
It is true that you won't get deeepeeeer, but this course allow you to not fear any kind of data science. That's amazing.
por Andrew B•
16 de out de 2017
This course is very enlightening. The techniques demonstrated in this course are critical for gathering raw data from various sources and turning it into useful data for analysis.
por Kelly S•
22 de mai de 2019
I really liked this course and believe that my work, although seemingly noob-ish, will get much better as I see others works from the peer review and examples noted in the lessons.
por Sudhin B•
17 de mar de 2018
So knowledgeable and interesting course. I have learned much about data cleaning and getting from different sources. Finally thanks to coursera team for giving us the opportunity.