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.
25. Okt. 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.
von Igor T•
8. Jan. 2017
Really great course which helps to understand basics of data tidying. I've also enjoyed regex explanation, it's really clear!
von Victor A d S P•
12. Dez. 2019
Great course, some background needed. If you are taking the Data Science specialization program, then this is a great catch.
von William H•
5. Aug. 2019
Thank you for the case study, very valuable to me to see how you downloaded all the data and began your exploratory analysis
von Bill S•
25. Mai 2017
I greatly enjoyed this course and I really got a lot of hands-on practice that I can apply in a real-world work environment.
von Jim M•
26. Apr. 2020
The content is very important when working with Big Data, yet I think JHU is unique in the importance paid to the concepts.
von Sebastian O V•
6. Aug. 2018
Great course teaches intermediate and andvanced skills to manipullate and clean data. Begginers should make an extra effort
von James L J J•
26. Dez. 2019
The Value of the course is in the course projects. I found that working through them really accelerated the understanding.
von Xun Y•
10. Juli 2017
Great course, providing very useful information. It might be better to provide few examples for codebook and readme files.
von João A P B•
25. Feb. 2017
Amazing course! Even after a few years using R in a daily basis, I still learned quite a bit of new / useful information.
von David R•
3. Sep. 2018
Great course covering a wide range of data types likely to be encountered in the field of data science. Well explained.
von Anne M•
10. Jan. 2016
Really great class. I found the lectures easy to follow. The quizzes and homework helped me master the course material.
von Andrés H V•
28. Juni 2020
I believe it was a very good course ,challenging and interesting. As a beginner in Data Science and R I learned a lot.
von Cathryn S•
2. Jan. 2017
A good course, which helped me understand how to get data off the web and from other sources, and improved my R skills
von Nelson S S•
10. Nov. 2020
Muchas gracias por compartir sus conocimientos y el gran aporte que hacen en éstas épocas de pandemia
von FARZAD R•
26. Feb. 2017
This course is very interesting, useful and practical
I appreciate for all of efforts of my professors in this course
von Pitchayen S•
14. Feb. 2017
Very fundamental things that all data scientist must learn. You will know how useful of data.table and dplyr package.
von Anastasia T•
19. Okt. 2016
Interesting, challenging course. Opened lots of questions to the topic and gave a good direction to find the answers.
von Cristóbal A•
2. Apr. 2016
Entrega conceptos y herramientas útiles. El material es de buena calidad y es presentado de una forma bastante clara.
von Suryadipta D•
28. März 2018
Very simple and introductory course that teaches the concepts really well. Forms a brilliant specialization course.
von Steven C•
15. März 2017
Great coverage of useful R libraries for retrieving and tidying up data, and difficult but valuable course project.
22. März 2016
Very good class for a beginner. Helps become more comfortable with R programming and aspects of getting data into R
von Vivek G•
8. Nov. 2019
Challenging course for working professionals from time perspective but very educative and useful. I learned a lot.
von Vipin J•
20. Nov. 2018
A must take course for data science carrer... Very good concept explaination with really good hands on with Swirl.
von Philip S•
9. Juli 2018
Excellent survey of data cleaning methods, good grounding in tidyverse tools, builds nicely on previous 2 courses.
von Matheus L N•
6. Juni 2018
was a hard module for whose isn't a programmer, but was well done structureted to easy learning with hard studying