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.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
- 5 stars67.61%
- 4 stars23.67%
- 3 stars5.84%
- 2 stars1.58%
- 1 star1.28%
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.
I think that the level of difficulty of the exercises and final assignment does not match with the depth of the lectures; without a textbook, I feel lost, don't have a reference, and have to guess.
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.
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.