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Course Date: 01 September 2014 to 29 September 2014 (4 weeks)
Learn how to gather, clean, and manage data from a variety of sources. This is the third course in the Johns Hopkins Data Science Specialization.
Jeff Leek is an Assistant Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and co-editor of the Simply Statistics Blog. He received his Ph.D. in Biostatistics from the University of Washington and is recognized for his contributions to genomic data analysis and statistical methods for personalized medicine. His data analyses have helped us understand the molecular mechanisms behind brain development, stem cell self-renewal, and the immune response to major blunt force trauma. His work has appeared in the top scientific and medical journals Nature, Proceedings of the National Academy of Sciences, Genome Biology, and PLoS Medicine. He created Data Analysis as a component of the year-long statistical methods core sequence for Biostatistics students at Johns Hopkins. The course has won a teaching excellence award, voted on by the students at Johns Hopkins, every year Dr. Leek has taught the course.
Roger D. Peng is an Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Co-Editor of the Simply Statistics blog. He received his Ph.D. in Statistics from the University of California, Los Angeles and is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for environmental data. He created the course Statistical Programming at Johns Hopkins as a way to introduce students to the computational tools for data analysis. Dr. Peng is also a national leader in the area of methods and standards for reproducible research and is the Reproducible Research editor for the journal Biostatistics. His research is highly interdisciplinary and his work has been published in major substantive and statistical journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. Dr. Peng is the author of more than a dozen software packages implementing statistical methods for environmental studies, methods for reproducible research, and data distribution tools. He has also given workshops, tutorials, and short courses in statistical computing and data analysis.
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.
How do the courses in the Data Science Specialization depend on each other? We have created a handy course dependency chart to help you see how the nine courses in the specialization depend on each other.
Will I get a Statement of Accomplishment after completing this class? Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.
What resources will I need for this class? Students must have an active GitHub account and the latest version of R and RStudio installed.
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.
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.