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.
Weekly lecture videos and quizzes and a final peer-assessed project.
As part of this class you will be required to set up a GitHub account. GitHub is a tool for collaborative code sharing and editing. During this course and other courses in the Specialization you will be submitting links to files you publicly place in your GitHub account as part of peer evaluation. If you are concerned about preserving your anonymity you should set up an anonymous GitHub account and be careful not to include any information you do not want made available to peer evaluators.
Upon completion of this course you will be able to obtain data from a variety of sources. You will know the principles of tidy data and data sharing. Finally, you will understand and be able to apply the basic tools for data cleaning and manipulation.
4 weeks (4-9 hours/week)
6th April, 2015- 4th May, 2015
- Knowledge of Data Scientist’s toolbox
- Knowledge of R programming
- R studio
- Brian Caffo, Phd
- Jeff Leel Phd
- Roger D. Peng