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.
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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.
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.
Very interesting and enjoyed doing the Assignment.\n\nbut the assignment instructions are not clear.A lot of time was wasted trying to figure out what data is what are what are we interested in.
Loved the structure of the course. Learned a lot. The course project seemed a little funky , especially creating the codebook for an already existing set of data but was a useful teaching aid.
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. ;)
This course is amazing! I have spent the majority of my time in R merely doing analytics. This course taught me the tools needed to go out and grab the data that I need for those analytics.
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.
The course material is ok - it would really need a book like the other courses have. It is much easier to check something from a book than slides/video when doing the assignments etc. .
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
I found the last project insufficiently explained. I was struggling in understanding what the task is. A bit more clear task description (as in Course 2) would be really appreciated.
Good coverage of topics. A bit scattered in early slides and assignments were often inconsistent with coursework. Overall a great introduction of what to expect when gathering data.
Actually, very interesting and helpful class. The one area around more complex structures (API, XML) warrants more attention, since I assume those are more dominant access methods.
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.
This is a very well put together course. It teaches the basics of data cleansing and how to setup data for modeling--by far the most foundational technical aspect of data analysis.
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.
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.
good introduction and practice being given on handling data cleaning. Lesser thought on getting data merely introduction. Still good to give a try if you are new in data analysis.
Very good content, but could have more content added.. I had much more free time with this one than what I did with the R Programming. Would be better to have more swirl lessons
Great ready-to-use skills for common tasks of a Data Scientist. Lays the foundations for further self-development in the topics taught. Heavy on R. Very challenging assignments.
learnt a lot of real world applications - fetching data from databases, apis, internet etc and basics of how to tidy data. Addditionally, I am more confident of my R skills now.
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